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Very informative and explained in just 5 mins - Answer is B for Quiz as the "Error is always calculated at output layer and then weight are adjusted to provide accurate results next time as application trains by itself"
Hi Shraddha, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
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Hi everyone, exactly a week ago, we conducted a quiz contest in this video. The answer to the quiz is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. We are pleased to announce the 3 lucky winners who got the right answer for our quiz: 1. Nayan Agarwal 2. Sahitya Reddy 3. Luis Mo Congratulations to all the winners! They've won an Amazon voucher worth INR 500 / $10.
Hi Jorge, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Subhadip, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Tomorrow Is my ai exam I didn't understood anything in class or by books But this video got me whole concept explain in hardly five minutes Thank you so much 😊 It saved my hours of useless attempts of my own
A is true as the activation function uses the threshold to determine whether is the neuron should be activated and in turn propagate data through the network. B is false as error is only calculated when the neural network makes a prediction, thus error is only calculated after the output layer. C is true as both forward and backward propagation are iterative processes during the training process. D is true as most data is processed at the hidden layers(usually one or more), most classification of the features takes place here. Answer is B
You're right! The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
The answer is B. since the error is validated and cross-checked in the output layer after a prediction has been determined and not in every layer. I just subscribed to your channel because of this video and the blockchain one. I’m pretty sure I’ll dive deeper in to your channel since you make complex concepts seem easy. Thank you SimpliLearn!! Sending love from the Philippines!
Hi Joshua, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your constant love and support. You can dive deeper to become an AI engineer: www.simplilearn.com/artificial-intelligence-masters-program-training-course. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Very informative, this explanation is really easily understandable - Answer for the quiz question is "B" (Because error is calculated at the output layer to adjust the weight to get accurate result, not in every layer).
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Shweta, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Shweta, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks for the excellent explanation in a visual form. Since I teach Machine Learning, Kindly let me know how you create these animated videos. I think this may help my students to understand the concept in easy manner.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Very informative and interesting video, made it really easy for me to learn neural networks. Thank you The correct answer is B- Error is always calculated at the output layer.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hello, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Answer is B. Error is calculated at each layer of the neural network. This statement is wrong since errors could only be rectified using back-propagation that occurs during the training of the neural network. Thank You Simplilearn.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Nice video sis/bro Also can a neural network can be used to find a best combination of parameters from multiple parameters Like if parameters (1,2,3,4,5,6,7,8) are fed as input , can it identify the best combination( pair of parameters) like (1&2, 1&3, 1&7, 2&8, 3&5, 7&1) for efficient performance of a system I was given this project for fuel cell performance estimation by inputing its operating and design parameters and finding the best combination which influences the performance most Can it be done in MATLAB? Pls show some light
Hi Hrushikesh, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
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Im no expert but let me try give it a shot.. initially, weights and biases are set at random..in python programming, u can use numpy.random to do this.. as your neural net process the inputs and give and output when training it, it will check how far is it from what the output supposed to be (loss value).. based on this, your neural net will adjust the weights and biases of each neurons in the hidden layer until the loss value nears zero or becomes static or the iterations assigned is completed.. depending on how well your model perform, u may have to manually changes some parameters such as number of neurons, learning rate, number of hidden layers, activation function etc.. im not sure if u can print the value of the final weight and biases of the neuron tho.. hope that helps
"Hi Faris, Here are two blogs that will help you understand how weights and biases work in a neural network: hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491 medium.com/coinmonks/the-mathematics-of-neural-network-60a112dd3e05"
Hi Luis, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
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All your videos are Awesome, you made my college exam's easier, these videos give clear cut meaning and understanding in very short time, So please do more videos
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
So this is an old video so I doubt I'll get a reply, but could someone please explain why the weights vary and how the value of the weigh s are assigned? Thank you
abdullah ihsan the weight is essentially the probability each neuron will give the network that the pattern is what it is looking for and the weight is measured out of 1 So in this case if a square is input, and if neuron x1 is absolutely sure that the pixel it is looking at belongs to a square, it would give it a weighting of 1. If it is somewhat sure for example it would give it a weighting of 0.5. Over time, the better the neurons and network layers get and the more training the network gets to identify patterns correctly from input images/data, the more accurate the output results
"Hi Abdullah, Here is an article that will help you understand how weights are assigned to a neural network. towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78"
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
I don't understand what the values of x_i would be. Is it an intensity value for pixel brightness and so you're trying to detect if some pixel is bright or not? then couldn't you just investigate the edges of the image (i.e. where there's a gradient between pixels)? I don't understand.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Great video! Two questions I have... Q1- is there an error in the formula when the weight etc is being discussed? The formula reads "(X1 * 0.8 + X3 * 0.2) + B1" should it not be (X1 * 0.8 + X2 * 0.2) + B1? and Q2 - does the neural network add each of the inputs in one formula? I.e. "(X1 * 0.8 + X3 * 0.2 + X3 * 0.1...) + B1 + B2 + B3..." and so on? Thanks!
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
Hi Satyajit, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Long, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Nayan, we are sorry about that and we didn't get any response from you either. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10.
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We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sana, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Tharun, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Tharun, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Nikhil, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Nayan, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Sandeep, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sandeep, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Sahitya, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Sameed, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sameed, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The Possible answer is B: Error is calculated at each layer of neural network. But ....... During process lot of energy is used to solve this problem but problem is still not solved for Neural Networking..
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Avijeet, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi Ahmad, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
the statement that does not hold the true is letter B - Error is calculated By each layer of neural network. it is clear on the explanation that error is compared on the last neurons
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Simon, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
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Very informative and explained in just 5 mins - Answer is B for Quiz as the "Error is always calculated at output layer and then weight are adjusted to provide accurate results next time as application trains by itself"
Hi Shraddha, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
A 2+ hours lecture simplified in just 5 mins. This is a great resource. I will make use of it in my marching learning assignment
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Hi everyone, exactly a week ago, we conducted a quiz contest in this video. The answer to the quiz is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
We are pleased to announce the 3 lucky winners who got the right answer for our quiz:
1. Nayan Agarwal
2. Sahitya Reddy
3. Luis Mo
Congratulations to all the winners! They've won an Amazon voucher worth INR 500 / $10.
Hi Jorge, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
B
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
The Answer is :- B
Hi Subhadip, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Tomorrow Is my ai exam
I didn't understood anything in class or by books
But this video got me whole concept explain in hardly five minutes
Thank you so much 😊
It saved my hours of useless attempts of my own
All the best
A is true as the activation function uses the threshold to determine whether is the neuron should be activated and in turn propagate data through the network. B is false as error is only calculated when the neural network makes a prediction, thus error is only calculated after the output layer. C is true as both forward and backward propagation are iterative processes during the training process. D is true as most data is processed at the hidden layers(usually one or more), most classification of the features takes place here. Answer is B
You're right! The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
The answer is B. since the error is validated and cross-checked in the output layer after a prediction has been determined and not in every layer.
I just subscribed to your channel because of this video and the blockchain one. I’m pretty sure I’ll dive deeper in to your channel since you make complex concepts seem easy. Thank you SimpliLearn!! Sending love from the Philippines!
Hi Joshua, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your constant love and support. You can dive deeper to become an AI engineer: www.simplilearn.com/artificial-intelligence-masters-program-training-course.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Very informative, this explanation is really easily understandable - Answer for the quiz question is "B" (Because error is calculated at the output layer to adjust the weight to get accurate result, not in every layer).
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer is B: Error is calculated at each layer of neural network
Hi Shweta, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Shweta, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
You explained it better than a course in my language which I paid for😂😂
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
why is this true😂
Use google translate
you deserve many millions of followers, short and sweet explanation well enough to understand the concept
Thank you for your kind word and for watching!
Thanks for the excellent explanation in a visual form. Since I teach Machine Learning, Kindly let me know how you create these animated videos. I think this may help my students to understand the concept in easy manner.
This is a scribe video. You can make use of this software to create the videos www.videoscribe.co/en"
Really liked the way you explained this topic.
Hey Ganesh, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
I'm in 9th Grade
its very useful for me
very well explained sir
And the Answer is OPTION B
Greetings from India !
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
@@SimplilearnOfficial Thx !
Awesome video, it explains very clearly and in a simple way how the NNs work for the beginners. Thank you!
Glad it was helpful!
Just a bunch of if/else nothing serious
Very informative and interesting video, made it really easy for me to learn neural networks. Thank you
The correct answer is B- Error is always calculated at the output layer.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Great video!!!! it was very well explained and the voice and tone is clear. THANK YOU!!!
Glad it was helpful!
"Error is calculated at each layer of the neural network" does not hold true.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
It's difficult to make a video but thank you so much for making this and for clearly explaining it for us to understand! :)
Thanks for your Video
The Answer is “B/ error is calculated at each layer”
It’s not calculate the error
"Hello, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer is B. Error is calculated at each layer of the neural network.
This statement is wrong since errors could only be rectified using back-propagation that occurs during the training of the neural network.
Thank You Simplilearn.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
The way u explained is so neat and clean, no loopholes. Thankssss
You're welcome! Thank you for watching!
The answer to the quiz question is B.
Thanks for this straightforward explanation of how neural networks operate.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Nice video sis/bro
Also can a neural network can be used to find a best combination of parameters from multiple parameters
Like if parameters (1,2,3,4,5,6,7,8) are fed as input , can it identify the best combination( pair of parameters) like (1&2, 1&3, 1&7, 2&8, 3&5, 7&1) for efficient performance of a system
I was given this project for fuel cell performance estimation by inputing its operating and design parameters and finding the best combination which influences the performance most
Can it be done in MATLAB?
Pls show some light
B: Error is calculated at each layer of neural network
Hi Hrushikesh, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
the false is because the Error is calculated in output layer
and great job to simpil this consept
Good evening sir, Thanks to Mr.Simplilearn for your teachings on neural network.
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
Great explanation -- if you already understand the subject. Otherwise, not so much...
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Great video!!!! it was very well explained and the voice and tone is clear. THANK YOU!!!
Glad it was helpful!
I have a question, How the weights are calculated? Also, how to know the value if bias for each neuron in the hidden layer? thank you.
Im no expert but let me try give it a shot.. initially, weights and biases are set at random..in python programming, u can use numpy.random to do this.. as your neural net process the inputs and give and output when training it, it will check how far is it from what the output supposed to be (loss value).. based on this, your neural net will adjust the weights and biases of each neurons in the hidden layer until the loss value nears zero or becomes static or the iterations assigned is completed.. depending on how well your model perform, u may have to manually changes some parameters such as number of neurons, learning rate, number of hidden layers, activation function etc.. im not sure if u can print the value of the final weight and biases of the neuron tho.. hope that helps
@@edu1113 Thanks for your valuable input!
"Hi Faris,
Here are two blogs that will help you understand how weights and biases work in a neural network:
hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491
medium.com/coinmonks/the-mathematics-of-neural-network-60a112dd3e05"
@@SimplilearnOfficial thank you for the video, the question and your response to the question. Much appreciated!
The answer is B because the error is calculated at the output layer after the output values and expected values are compared.
Hi Luis, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs500/ $10. The answer to the question is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
@@SimplilearnOfficial What a great new, it's awesome that you reward your followers, thank you very much.
@@SimplilearnOfficial djandroide97
You are very welcome. Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
All your videos are Awesome, you made my college exam's easier, these videos give clear cut meaning and understanding in very short time, So please do more videos
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
Crazy to think that we’re a natural neural network thats learning how to make artificial neural networks.
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
correct option is B, as an erroe is calculated at the end in the output layer and if its layer the information is sent and processed again
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Do one for how chatgpt works.
I think that is how chatGPT works
B is the answer after the forward propagation we are suppose check the error by the submisson of 1/2 (taget ouput - actual ouput ) ^2 .
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
So this is an old video so I doubt I'll get a reply, but could someone please explain why the weights vary and how the value of the weigh s are assigned? Thank you
abdullah ihsan the weight is essentially the probability each neuron will give the network that the pattern is what it is looking for and the weight is measured out of 1 So in this case if a square is input, and if neuron x1 is absolutely sure that the pixel it is looking at belongs to a square, it would give it a weighting of 1. If it is somewhat sure for example it would give it a weighting of 0.5. Over time, the better the neurons and network layers get and the more training the network gets to identify patterns correctly from input images/data, the more accurate the output results
"Hi Abdullah,
Here is an article that will help you understand how weights are assigned to a neural network.
towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78"
The answer is B because error gets calculated only after comparing the predicted output with the actual output in the training process.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
You yes you who thought this video thanks a lot god bless you 😁
You're welcome 😊
I don't understand what the values of x_i would be. Is it an intensity value for pixel brightness and so you're trying to detect if some pixel is bright or not? then couldn't you just investigate the edges of the image (i.e. where there's a gradient between pixels)? I don't understand.
Damn, the quality of explanation is awesome.
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You explain concepts better than experts like Geofrey Hinton
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The Answer is “B/ error is calculated at each layer”
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
You are Amazing sir
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Great video!
Two questions I have...
Q1- is there an error in the formula when the weight etc is being discussed? The formula reads "(X1 * 0.8 + X3 * 0.2) + B1" should it not be (X1 * 0.8 + X2 * 0.2) + B1?
and
Q2 - does the neural network add each of the inputs in one formula? I.e. "(X1 * 0.8 + X3 * 0.2 + X3 * 0.1...) + B1 + B2 + B3..." and so on?
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@@SimplilearnOfficial bot ans...hahahha
Answer B - Error is always calculated at Output Layer
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
It's funny that it's been three years and now I'm watching this video myself
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B is the answer as the error is calculated at the output layer and based on the errors the backpropagation takes place to adjust the weights
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer of quiz is 'B'
Hi, you got the right answer. Kudos.
Great video man, in 5 minutes you just explained everything in such a simple way
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B as the error is calculated at the end of the NN, as error is original value - predicted value.
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Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks sir you are genius
Most welcome
which software is used to make such a interactive animation? thank you.
Hi Long, we use Scribe and Aftereffects to make these animations. Thanks.
Hi Long, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hii Long, have you got the reward from simplilearn. Bcz i was also selected for the prize but have not got that yet.
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nice video ,,,,please upload video of PSO-CNN Hybridization
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the answer is B
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B. Error is calculated at the very end, right before any backward propagation takes place.
Ans is b
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
BB! Error is calculated at the output layer of the neural network!
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Ans is B
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
How can i claim this winning voucher??
Thank u for selecting me as one of three winners
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b) is wrong alternative ( Errors are calculated once at the end of forward progression, to initiate backward progression)
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
its absolutely B....
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Really good video with great graphics, narration and animation. Thanks!
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The Answer to the question is
: B
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Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
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B. Error is calculated at each layer of neural n/w
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer is D option.
Lol. Really dude?
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thank you so much Sir for your video...
I think I can write this answer in my tomorrow paper...
And Answer Is B.
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Option B
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
none could explain it any better!
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Option (B)
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The Possible answer is B: Error is calculated at each layer of neural network. But ....... During process lot of energy is used to solve this problem but problem is still not solved for Neural Networking..
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
C is correct
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Hi Avijeet, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
@@SimplilearnOfficial ANSWER IS B
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
You r absolutely marvelous Sir!!!
Good explanation 👍👍
Thanks a ton!
Answer of the quiz is - B
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Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Dude your video was great 👍
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Error is calculated at each layer of neural network. OptionB
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Better than an hour lecture!
A is the answer of that quiz, thanks .
"Hi Ahmad, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
ans:B
because error does not calculate at each layer it calculated only on last in output layer
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
B. Error is calculated at each layer of the neural network. Because in fact, it's at the end that we see the error.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer is B: Error is calculated at each layer of neural network. It is done at output layer.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
It is very understanding but I would suggest that take clear pic ☺️🙂
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The answer is B right. Is this forward and backward propagation of neural networks follows order?
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Thanks a lot, clear explanation!!
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Excellent explanation!! Thanks a lot! :D
Glad it was helpful!
Thanks a lot for this explaination.This is really awesome...
You are most welcome
Definitely B !! Thanks for the Video!! Liked, shared and now following!
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Super Explanation.....🥳🥳🎊🎉🔥🔥🔥🔥👏👏👏👏👏Me expecting more and more animated videos....like this ......
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the statement that does not hold the true is letter B - Error is calculated By each layer of neural network. it is clear on the explanation that error is compared on the last neurons
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Wow its a good video thankyou the answer of the quiz is B
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Good one. A lovely real life application of Math.
Glad you liked it!
The answer is B!!!
(Can I have a voucher please?)
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
C is the answer,Both forward and backward propagation does not take place during the training process; only forward does .
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The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
When we're solving re-captchas we're feeding in data to the neural networks.
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According to me!! Error is calculated at each layer of the neural network
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B; Errors are calculated in output layer because data is back propagated then to input layers and weight are readjusted.
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The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
I have one problem. is every ML model has a neural network? If not please give me a summary of the ML Model with ANN & ML Model without ANN
Thanks for making this video. I don’t remember what professor was teaching in my class😀
option b is wrong as error doesn't calculated for each and every layer while training it. It is always calculated at the end on the output side.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
B is the correct option for the quiz answer. Error is calculated every time after the output processed in each propagation.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Amazing video explained in 5 minutes! Love it
Glad you liked it!