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Great! Was able to follow the tutorial from start to finish easily. The problem is, now that I peeled some NN, I realize I have a lot more to learn. Some things like ReLU, Optimizer will need to be explained with examples for a newb like me :D. Hope this series continues and makes all the intricate details of NN easy to grasp. Great work, kudos!
3:14 Hi there. This is what I did in order to improve my loss rate: 1. Moved all tensors and the model to GPU 2. Epochs = 130 3. Implemented 3rd hidden layer with 6 neurons as a result I got 0.0169 vs 0.0172 Thanks
Hey! I found your video extremely helpful. Could you do a similar video for a Regression problem by taking a dataset like this? Most of the videos out there on regression use numerical data they specifically create. It would be great if you could do a video on regression with datasets like Abalone, Wine quality, etc.
On the topic of randomization, would it be optimal to loop through seeds to see which one is the most accurate and then use that for the model, assuming the test data is plentiful enough?
I went through the prior tutorial just great.. but that first graph shown on this video, came out a bit different for me.. it had the same general shape but had a saw-tooth pattern to the line instead of a smooth line like shown here.. what could cause that? Thanks for a great video series!
It's a tensor used to evaluate the model's performance on the test set. Basically, it uses the Model class' (instantiated as "model" here) "forward" method to go through all the layers to generate a prediction. Since there's a line with "with torch.no_grad()" before it, it means that backpropagation is not performed (i.e. the weights and bias aren't updated so the model isn't learning anything), since the model has already been trained.
I have some idea....I read about "Game of live" - John Conway. What do you think about simple project where 2 of pixel group (red and blue) live in one board. Group need eat and drink, can fight and have two sex for reproduction. It is possible to create something like this in PyTorch, or it is not please help me find a better way to do this. I just want to try something like this and in my opinion you are the best person who can help me. Thanks, greets from Poland
![Build Your First Pytorch Model In Minutes! [Tutorial + Code]](/img/1.gif)
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Bright demonstration. John is gifted to explain the whole process. Thank you for for those videos 👍👍
welcome
I am following you to learn PyTorch and I understood the whole stuff step by step. It's amazing to watch your lectures and keep doing it.
Happy to hear it!
Great! Was able to follow the tutorial from start to finish easily. The problem is, now that I peeled some NN, I realize I have a lot more to learn. Some things like ReLU, Optimizer will need to be explained with examples for a newb like me :D. Hope this series continues and makes all the intricate details of NN easy to grasp. Great work, kudos!
I'm planning to continue it, yes :-)
@@Codemycom awesome 🙂
the best crash course that I've watched in deep learning, thank you so much
Glad you enjoyed it!
3:14 Hi there.
This is what I did in order to improve my loss rate:
1. Moved all tensors and the model to GPU
2. Epochs = 130
3. Implemented 3rd hidden layer with 6 neurons
as a result I got 0.0169 vs 0.0172
Thanks
awesome
you try by random guess? randomly try to add more layer and neuron? and test your result case by case?
Hey! I found your video extremely helpful. Could you do a similar video for a Regression problem by taking a dataset like this? Most of the videos out there on regression use numerical data they specifically create. It would be great if you could do a video on regression with datasets like Abalone, Wine quality, etc.
On the topic of randomization, would it be optimal to loop through seeds to see which one is the most accurate and then use that for the model, assuming the test data is plentiful enough?
Nice.. I am going to watch an practice along.. If anything I will send feedback
Thank you sir.
Welcome!
Thank you so much sir. I have a question. What does those output represents? eg. y_val. Why we are comparing the largest to the y_test?
Hi, great series, please keep it going
It's still going, I was just on vacation this Monday
@@Codemycom yay!
I went through the prior tutorial just great.. but that first graph shown on this video, came out a bit different for me.. it had the same general shape but had a saw-tooth pattern to the line instead of a smooth line like shown here.. what could cause that? Thanks for a great video series!
You didn't seed your data the same way I did?
Where do you find the link to the code?
hi sir, may I ask how can I fix the result every time I re-run the code, training loss becomes different thanks
It's supposed to do that
what does the tensor of y_val means?
It's a tensor used to evaluate the model's performance on the test set. Basically, it uses the Model class' (instantiated as "model" here) "forward" method to go through all the layers to generate a prediction. Since there's a line with "with torch.no_grad()" before it, it means that backpropagation is not performed (i.e. the weights and bias aren't updated so the model isn't learning anything), since the model has already been trained.
I have some idea....I read about "Game of live" - John Conway.
What do you think about simple project where 2 of pixel group (red and blue) live in one board.
Group need eat and drink, can fight and have two sex for reproduction. It is possible to create something like this in PyTorch, or it is not please help me find a better way to do this. I just want to try something like this and in my opinion you are the best person who can help me.
Thanks,
greets from Poland
11:31 delete the if-elif-else that's suppose to provide the name. use a dictionary instead.
iris_name = {0:'Sentosa', 1:'Versicolor', 2:'Virginica'}