🔥Caltech Post Graduate Program In AI And Machine Learning - www.simplilearn.com/artificial-intelligence-masters-program-training-course?S9g-Zcs&Comments&RUclips 🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?S9g-Zcs&Comments&RUclips 🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?S9g-Zcs&Comments&RUclips 🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?S9g-Zcs&Comments&RUclips 🔥Caltech - AI & Machine Learning Bootcamp (US Only) - www.simplilearn.com/ai-machine-learning-bootcamp?S9g-Zcs&Comments&RUclips
Below topics are explained in this Neural Network Full Course: 1. Animated Video 00:52 2. What is A Neural Network 06:35 3. What is Deep Learning 07:40 4. What is Artificial Neural Network 09:00 5. How Does Neural Network Works 10:37 6. Advantages of Neural Network 13:39 7. Applications of Neural Network 14:59 8. Future of Neural Network 17:03 9. How Does Neural Network Works 19:10 10. Types of Artificial Neural Network 29:27 11. Use Case-Problem Statement 34:57 12. Use Case-Implementation 36:17 13. Backpropagation & Gradient Descent 01:06:00 14. Loss Fubction 01:10:26 15. Gradient Descent 01:11:26 16. Backpropagation 01:13:07 17. Convolutional Neural Network 01:17:54 18. How Image recognition Works 01:17:58 19. Introduction to CNN 01:20:25 20. What is Convolutional Neural Network 01:20:51 21. How CNN recognize Images 01:25:34 22. Layers in Convolutional Neural Network 01:26:19 23. Use Case implementation using CNN 01:39:21 24. What is a Neural Network 02:21:24 25. Popular Neural Network 02:23:08 26. Why Recurrent Neural Network 02:24:19 27. Applications of Recurrent Neural Network 02:25:32 28. how does a RNN works 02:28:42 29. vanishing And Exploding Gradient Problem 02:31:02 30. Long short term Memory 02:35:54 31. use case implementation of LSTM 02:44:32 Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!
14. Loss *Function, small mistake here in the comment I've only watched the first part, so far it seems clear and useful, tomorrow I'll watch the rest. But please next time provide the second guy a better microphone or edit his audio afterwards! It sounds super weird and crackles, almost like an old radio. Thanks!
I have coin images which i have taken through the process of canny edge detection and now I have images with edges, how can I input them into a neural network for training purposes. Thanks
"Hi Kamau, Yo input image to a neural network, you need to put the image values into one vector and feed this vector into the network. This should already work. By first extracting features (e.g., edges) from the image and then using the network on those features, you could perhaps increase the speed of learning and also make the detection more robust."
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community and don't forget to finish this tutorial!
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every day on all your favorite topics Have a good day!
Hey Vitthal, 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 :)
Your tutorials are great, really explanatory and are one of the best, but why do they generally have lots of echo. The echo reduces concentration.Thanks
Thank you for your review. We are sorry to hear you had such a frustrating experience, but we really appreciate you bringing this issue to our attention
Your video quality is so good. I expect better sound quality while describing a lesson! This will be great if the sound quality is better. Thank you for the awesome courses.
We are glad you found our video helpful, Gokul. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Hey Miguel, 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 :)
Hello Kashyap, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Can a smart person answer me on question? Is there a tutorial of making a NN from scatch without using any librarys? I know this is much more work and maybe not so efficient, but I just want to learn every part of deep learning by doing and I also want my code not to be dependent from other code, which may be deleted in future so all my work won't work anymore or other versions are not working with my code or something like that. I want my code just to be independend from any librarys.
I was very unclear after watching the first part of the video. You point the code at 2 data sets, a learning data set and a testing dataset. How do you specify in these sets what images in the set are cats, and what images in the set are dogs?
"Hi , There is no significant difference between the two. Neural Network in Python is a keyword that specifies building neural network models using Python and its libraries. "
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hey Neeraj, thank you for watching our video. We will definitely look into your suggestions and try to make a new video on that. Do subscribe and stay tuned for updates on our channel. Cheers :)
We are sorry to say that we don't share the slides for copyright issues. However, we will share the data-sets as soon as you share your email ID. Thanks.
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
one question , I have noticed while learning from this session , line "In [10]" :train_datagen.flow_from_directory should be test_datagen.flow_from_directory as that line is preparing test set data ?
Could someone please share the sequence in which i shall begin simplelearn tutorial ,i am a beginner ,what all shall i know before coming to this video
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
Hello Parzivi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Could someone please share the sequence in which i shall begin simplelearn tutorial ,i am a beginner ,what all shall i know before coming to this video
Hi Grishma, you can start with Machine learning before jumping into neural networks. For machine learning videos, click here: ruclips.net/video/ukzFI9rgwfU/видео.html
Thank you for letting us know know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
Yes, it is totally possible. The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. To start with, you can go through our AI playlist here: ruclips.net/video/15PK38MUEPM/видео.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
"Hi Kirti, we are sorry to say that you got the wrong answer. 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, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
"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. "
Yes, it is totally possible. The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. To start with, you can go through our AI playlist here: ruclips.net/video/15PK38MUEPM/видео.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
"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. "
"Sorry, you got the wrong answer. The explanation for the answer is given below: The first step to developing software would be to create a strategy such that the software satisfies all its requirements. We then write the code for the functionalities to be provided by the software. This is the ""Plan and Code"" stage The code is then automatically fetched from the repositories and packaged into executable applications. This is the ""Build"" stage. The applications are tested to catch any bugs. After the required changes are incorporated, the software is deployed on the client system. This falls under the Deploy and Operate state. The software is continuously monitored and any feedback is sent back to the planning stage. This loop will ensure that the product is up to date and providing maximum efficiency. I hope that helps!"
"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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🔥Caltech Post Graduate Program In AI And Machine Learning - www.simplilearn.com/artificial-intelligence-masters-program-training-course?S9g-Zcs&Comments&RUclips
🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?S9g-Zcs&Comments&RUclips
🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?S9g-Zcs&Comments&RUclips
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?S9g-Zcs&Comments&RUclips
🔥Caltech - AI & Machine Learning Bootcamp (US Only) - www.simplilearn.com/ai-machine-learning-bootcamp?S9g-Zcs&Comments&RUclips
Below topics are explained in this Neural Network Full Course:
1. Animated Video 00:52
2. What is A Neural Network 06:35
3. What is Deep Learning 07:40
4. What is Artificial Neural Network 09:00
5. How Does Neural Network Works 10:37
6. Advantages of Neural Network 13:39
7. Applications of Neural Network 14:59
8. Future of Neural Network 17:03
9. How Does Neural Network Works 19:10
10. Types of Artificial Neural Network 29:27
11. Use Case-Problem Statement 34:57
12. Use Case-Implementation 36:17
13. Backpropagation & Gradient Descent 01:06:00
14. Loss Fubction 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
17. Convolutional Neural Network 01:17:54
18. How Image recognition Works 01:17:58
19. Introduction to CNN 01:20:25
20. What is Convolutional Neural Network 01:20:51
21. How CNN recognize Images 01:25:34
22. Layers in Convolutional Neural Network 01:26:19
23. Use Case implementation using CNN 01:39:21
24. What is a Neural Network 02:21:24
25. Popular Neural Network 02:23:08
26. Why Recurrent Neural Network 02:24:19
27. Applications of Recurrent Neural Network 02:25:32
28. how does a RNN works 02:28:42
29. vanishing And Exploding Gradient Problem 02:31:02
30. Long short term Memory 02:35:54
31. use case implementation of LSTM 02:44:32
Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!
14. Loss *Function, small mistake here in the comment
I've only watched the first part, so far it seems clear and useful, tomorrow I'll watch the rest. But please next time provide the second guy a better microphone or edit his audio afterwards! It sounds super weird and crackles, almost like an old radio. Thanks!
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
Why do you have it ordered this way, its mixed up a bit..
I have coin images which i have taken through the process of canny edge detection and now I have images with edges, how can I input them into a neural network for training purposes. Thanks
"Hi Kamau,
Yo input image to a neural network, you need to put the image values into one vector and feed this vector into the network. This should already work.
By first extracting features (e.g., edges) from the image and then using the network on those features, you could perhaps increase the speed of learning and also make the detection more robust."
13. Backpropagation & Gradient Descent 01:06:00
14. Loss Function 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )
Getting deep idea about deep learning and ANN.
Thanks to you simplilearn!
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Great class.
Keep up the good work.
Thank You,
Natasha Samuel
Thank you! 😃
Haven't completed yet.. but really impressive so far.. will update after watching complete video..
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community and don't forget to finish this tutorial!
This is the simplest explanation, i had heard before :-)
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Thank for the lecture it was wunnerful
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every day on all your favorite topics Have a good day!
Thanks to simplilearn ...
You are welcome!
Great project. I like it very much ❤❤
Such a clean content. Kudos to you guys♥️
Hey Vitthal, 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 :)
Your tutorials are great, really explanatory and are one of the best, but why do they generally have lots of echo. The echo reduces concentration.Thanks
Thank you for your review. We are sorry to hear you had such a frustrating experience, but we really appreciate you bringing this issue to our attention
thanks for such an amazing new year gift
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
Nice one !
Thanks a lot! Do subscribe to our channel and stay tuned!
Your video quality is so good. I expect better sound quality while describing a lesson! This will be great if the sound quality is better. Thank you for the awesome courses.
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
Great video. But sound quality is very poor. In upcoming videos if you improve sound quality. It will be very useful for us. 👍
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
Thank you, this is amazing! 🙏
You are welcome!
Very good course. Can Richard's recording can be made sound more natural rather than a flight pilot? Please consider as it hurts ears.
Thank you for bringing this to our attention. We’re sorry you had a bad experience. We’ll strive to do better
Great work 👌 Thanks a lot
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
Nice tutorial ♥️ need more like this
We are glad you found our video helpful, Gokul. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Good.....
Thank you! Cheers!
nice tutorial
Thank you! Cheers!
Well put together course, are the slides available anywhere?
We don't have any pdf of this particular video. For slides, you can check out our slideshare profile: www.slideshare.net/Simplilearn"
@@SimplilearnOfficial Thank you!
Osmmmm great suppperb informative video
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
Very easy to flow tutorial please share datasets
Hi Samuel, we are glad you found our video helpful. Please like and subscribe to our channel and click on the bell icon to get new video updates.
Great course
Hey Miguel, 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 :)
great❤❤❤
Thanks a lot for sharing the video, the way of teaching is very nice.
If possible could you send me the exercise datasets.
Hello Kashyap, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial gohainkashyap3@gmail.com
We have sent the requested dataset to your mail ID. Do subscribe to our channel and stay tuned.
Thank you simplilearn
Its really awesome explanation.... Can i have data set along with ipynb files
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Can a smart person answer me on question? Is there a tutorial of making a NN from scatch without using any librarys? I know this is much more work and maybe not so efficient, but I just want to learn every part of deep learning by doing and I also want my code not to be dependent from other code, which may be deleted in future so all my work won't work anymore or other versions are not working with my code or something like that. I want my code just to be independend from any librarys.
There is a video on js i found which built its own library not in pthon
I was very unclear after watching the first part of the video. You point the code at 2 data sets, a learning data set and a testing dataset. How do you specify in these sets what images in the set are cats, and what images in the set are dogs?
same doubt
@@prettylilnerdy6802 same simple learn not even given the data sets
3:00:57, I though dropout is when 30% if neurons get randomly dropped as a regularization technique?
I have a doubt, what is the difference between neural network and neural network in python?
Thank you Simplilearn!😄
"Hi ,
There is no significant difference between the two. Neural Network in Python is a keyword that specifies building neural network models using Python and its libraries. "
Can we apply this on matlab too?
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
Thanks for the course video on Neural Network, can you please send me the codes and the data set, it will help me try my self.
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Thank you dscience.m@gmail.com
Hello Team ,can you please explain how to create custom dataset and how to use in model train (tensorflow). Thanks in advance
Hey Neeraj, thank you for watching our video. We will definitely look into your suggestions and try to make a new video on that. Do subscribe and stay tuned for updates on our channel. Cheers :)
love the course but it is costly in comparison to others
Hi Harendra, you can pay in easy EMIs for online courses: www.zestmoney.in/partner/emi-for-courses-on-simplilearn/.
Where can I get the slides and the codes? Thanks
We are sorry to say that we don't share the slides for copyright issues. However, we will share the data-sets as soon as you share your email ID. Thanks.
@@SimplilearnOfficial Pease send me data sets Email: achintha.sanith@gmail.com
please share the data set to shubhamkumarpnb@gmail.com
can you provide the dataset and sourcecode?
Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial aabhijith187@gmail.com
I like atom as a python editor
That is great too.
I am view the video of this course. I am really interested in watching & learning from you. Pls, what do I do? I do need your help.
You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
one question , I have noticed while learning from this session , line "In [10]" :train_datagen.flow_from_directory should be test_datagen.flow_from_directory as that line is preparing test set data ?
Could someone please share the sequence in which i shall begin simplelearn tutorial ,i am a beginner ,what all shall i know before coming to this video
NICE :)))))))))))
Thank you! Cheers!
amazing video can you please send me the dataset
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
i didn't received the data, can you please send it to bidhuri.arunesh@gmail.com
Hi Arunesh, we have sent the dataset again.
Thanks i received it. Really appreciate your prompt response
Sir
How to get the pictures for training set?
Hello Parzivi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial yea sure its ParzivL1o1@gmail.com
And my question is still that where didi u get those 800 images for training set do i have to seprately download it!?
Could someone please share the sequence in which i shall begin simplelearn tutorial ,i am a beginner ,what all shall i know before coming to this video
Hi Grishma, you can start with Machine learning before jumping into neural networks. For machine learning videos, click here: ruclips.net/video/ukzFI9rgwfU/видео.html
An error in slides when u were explaining rnn where you said its many to many on a slide of many to one 2.30.30
Thank you for letting us know know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
Please share the datasets
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
I have one doubt. How do u come to know the output shape of convolutional layer 1 and maxpooling layer 1 as 32x64? Help me out please.
Same doubt
Expecting Reply from Simplilearn.
can i have the datasets and code??
jshubhra308@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
b option
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
Commerce student kaise artifical intelligence courses Kare
Yes, it is totally possible.
The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning.
To start with, you can go through our AI playlist here: ruclips.net/video/15PK38MUEPM/видео.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
How to use neural network for face recognition
You don't explain why the parameters are setup with these values especially Conv2D parameters why you should explain why to do it
A IS NOT TRUE ACTIVATION FUNCTION ARE THRESHOLD FUNCTION
"Hi Kirti, we are sorry to say that you got the wrong answer.
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.
"
please send the data set
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
B doesn't hold true
"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.
"
Please reply
Yes, it is totally possible.
The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning.
To start with, you can go through our AI playlist here: ruclips.net/video/15PK38MUEPM/видео.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
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.
"
D
"Sorry, you got the wrong answer. The explanation for the answer is given below:
The first step to developing software would be to create a strategy such that the software satisfies all its requirements. We then write the code for the functionalities to be provided by the software. This is the ""Plan and Code"" stage The code is then automatically fetched from the repositories and packaged into executable applications. This is the ""Build"" stage.
The applications are tested to catch any bugs.
After the required changes are incorporated, the software is deployed on the client system. This falls under the Deploy and Operate state. The software is continuously monitored and any feedback is sent back to the planning stage. This loop will ensure that the product is up to date and providing maximum efficiency. I hope that helps!"
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.
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