The code demonstrated this video can be downloaded here: lightning.ai/lightning-ai/studios/statquest-introduction-to-coding-neural-networks-with-pytorch?view=public§ion=all To learn more about Lightning: lightning.ai/ This StatQuest assumes that you are already familiar with... Neural Networks: th-cam.com/video/CqOfi41LfDw/w-d-xo.html Backpropagation: th-cam.com/video/IN2XmBhILt4/w-d-xo.html The ReLU Activation Function: th-cam.com/video/68BZ5f7P94E/w-d-xo.html Tensors: th-cam.com/video/L35fFDpwIM4/w-d-xo.html To install PyTorch see: pytorch.org/get-started/locally/ To install matplotlib, see: matplotlib.org/stable/users/getting_started/ To install seaborn, see: seaborn.pydata.org/installing.html Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Just a small comment. Any variable should not be named similar to any builtin in Python. The 'input' variable in forward should have been called something else since it is already a builtin function in Python. Otherwise, you end up overriding the builtin within that scope.
Please continue to go through every single line of code including the parameters with excruciating detail like you do. None of my professors went over each line like that cuz they always "assumed we already knew" and everyone in the class who didnt already know was afraid to ask to avoid looking stupid. Thank you.
StatQuest is the GOAT in statistics, machine learning, and deep learning! You're videos are really helping me understanding the concepts and outline of these fields! Love from Korea!
Ive used PyTorch for projects before, but I can honestly say that I never fully understood the workings of building a model. I knew that i needed the peices you mentioned, but not why I needed them. You've just explained it incredibly. Please don't stop making this series!!
Hello! Not sure if anyone's pointed this out yet, but the code on 10:14, 12:09, and 22:42 needs a small addition, `plt.show()`, or else it won't show the graph. Though, maybe 2 years ago when this video was made you didn't need that, I'm not sure, haha. Other than that, this is an awesome tutorial that quite literally takes anyone through the process step-by-step, and even tells you some neat fun facts (like the sns nickname) and explanations like how `loss.backward()` works. TRIPLE BAM indeed! Thanks for the awesome tutorials and videos you put out 👍
AMAZING video. This is exactly what beginners need to start the Pytorch journey with a semi solid footing instead of mindless copying. Yoy must have spent so much time for your AWESOME videos. GREATLY appreciate your effort. Keep up the good work.
Was looking for a pytorch resource and was disappointed when this channel didnt have one yet but then this got uploaded. Really a blessing to the people haha
I am someone who loves *SQ,* and *JS* style of teaching in byte 😅 pieces but I also hate _snakes…_ I love *JavaScript* and *TypeScript* but I’ve been learning *JavaScript* with the _strictest linting rules_ one would imagine… and given how *JavaScript* could be used without any sort of strict rules (and is very similar to *Python* in this context) it is frustrating that it makes *Python* very hard to understand despite being easier since it has not the same stricter rules I have imposed myself learning *JavaScript…* but I am also genuinely grateful that *JS* is the best instructor for this kind of topics because *JS* has a _Ukulele,_ *StatSquatch* and *Normalsaurus* which are all there to help *JS* make *SQ* awesome 🎉🎉🎉🎉 Thanks 😅😅😅❤
What a great feeling when it all clicks after learning about all these concepts in isolation. All thanks to an incredibly brilliant teacher! Triple BAM!!!
Guess who was going to start programing a neural network in python today...... God bless you Josh, becase He know how much you are blessing me with your work. And know that Jesus loves you and want to be part of your life.
Thanks for the awesome tutorial! You make the most difficult things so easy to understand, specially with the visuals and the arrows and all! The comments written on the right hand side make it so more helpful to pause and absorb. I would never miss a video of your tutorials!
It amazes me, when I see no NLP video on StatQuest! Josh your explanation are always higher than what one can expect and you have created so many series including maths and conceptual understanding. NLP has the same importance compared to computer vision and actually people are suffering to learn it by lack of content availability! I hope you would create a series or maybe a few videos on basic concepts which help people to get interested in NLP : ) Hope you are doing good in life Josh
@@statquest Yay so glad to hear this, we really need you because no one gives great explanations like you do. Also your youtube comments are the nicest I've ever seen which is a testament to how valued you are in this community.
Thanks so much for this gem John! Literally got a PyTorch project coming up and your timing is just perfect. Greatly appreciate the content, keep up the good work :)
Hi Josh, sorry to be a spoil sport, but I used exact same code and my prediction is showing 0.5 for dosage of 0.5 and it is running for all 100 epoch and final b value comes out to be -16.51 😔. But yes the concept is clear!! Great work! I always ask people whoever are interested in learning about data science or machine learning to refer you channel. Seeing your channel grow from 10-20K to a Mn is pleasure to my eyes!! You are the "El Professor"!!
Thank you very much! If you look at my actual code (follow the link), you'll see that I actually pulled a trick with the data to get it to train faster.
I love how you you visualize and synchronize the code with the maths behind it :) On top of that you are doing it step-wise which results in a really awesome and very eduSupercalifragilisticexpialidociouscational video! #ThankYou
Just watched matrix multiplication of Transformers. My mind was blown away. Same things appear so complex but when this guy explains them, it's like peanuts. Triple BAM
Wow 😮 I didn't knew I had to watch the *Neural Networks part 2* before I can watch the *The StatQuest Introduction To PyTorch* before I can watch the *Introduction to coding neural networks with PyTorch and Lightning* 🌩️ (it’s something related to the cloud I understand) I am genuinely so happy to learn about that stuff with you Josh❤ I will go watch the other videos first and then I will back propagate to this video...
Hi! This is amazing. Are you gonna continue this series? Out of ten different rabbitholes I have been to, this video has been the most helpful for me with understanding PyTorch and starting off with my project. Please continue making more complicated models. Thank you :)
That is a big leap. I need to check it several times to understand it since I am not a programmer. However, I really got a good feeling of what is happening inside the code. I actually use codeless systems such as KNIME. So if Mr. Sasquatch, get the idea of using KNIME to explain all this, It will be amazing. Thanks to be such a good teacher.
Thank you, good explanation! 16:00 Python prefers for-each-loops over index-based loops. See how this equivalent for-each loop looks much simpler. for input, label in zip(inputs, labels): output = model(input) loss = (output - label)**2 loss.backward() total_loss += float(loss)
Hi Josh, thanks for your video. I am confused why it doesn't work when I am trying to optimize any other weights and bias. five minutes later, I think I have got the answer from the comments and your reply. Thanks again!
Nice video for the introduction of LSTM using PyTorch. There is also `torch` R package that doesn't need to install python and torch. It's so nice that R also has deep learning framework aside from `tensorflow` and I recommend you to maybe try it.
Hello Josh! Thank you so much for your amazing videos! I have learned so much from your tutorials and would not have been able to advance without them! I wanted to ask whether it is possible for you to put some videos on times series analysis, including autoregression (AR), moving average (MA) and their combinations. I would be more than grateful if you can provide such a video. Thank you so much.
Hi Josh. I am a big fan of your videos. I have a question regarding this quest. In this video, we optimized only one parameter. How can we optimize all the parameters? Thanks in advance.
I show how to impute all of the parameters in this video on LSTMs in PyTorch: th-cam.com/video/RHGiXPuo_pI/w-d-xo.html (if you want to learn about the theory of LSTMs, see: th-cam.com/video/YCzL96nL7j0/w-d-xo.html
Thanks Josh, its Incredible video. Beside, recently the Bayes theorem application in fitting model (linear, logistic, random forest...) has became more and more popular in order to replace classic statistic method, could you pls take some time to explain to us some of its popular algorithm like BART, Linear regression via Bayesian Methods...
Thanks! My book, The StatQuest Illustrated Guide to Machine Learning, starts with a song! (and has a another song midway through): statquest.org/statquest-store/
MEGABAMMMMMM..... Hey josh... It's been a very long long time.... I am occupied with different subject right now.. Hope you are doing good... Will catch you soon..
So I paused at 18:04 because _it blew my mind_ that we were calling backwards() on the loss variable because I thought it was defined on the line above… 😅😅😅😅 but yeah I didn’t find anything so one hour later I was just watching the rest of the video and _to be honest_ in about 33 seconds it came out that it was normal for _my mind to be blown_ 😂😂😂😂 at 18:37
Hello! I've got a bit confused: at 15:08 you mention that at each epoch we'll be running all 3 data points through the model, but wasn't the point of SDG in that we would only need a single data point per epoch, or am I misunderstanding something? Btw, despite my confusion, this is by far the best ML guide series I've seen, thank you for your work!
That's a good question. "torch.optim" doesn't have a gradient descent optimizer, just a stochastic gradient descent optimizer. So we import torch.optim.SGD and then pass it all of the residuals to get gradient descent.
That's the plan. This is just the first of many videos on how to code neural networks. The next video will be on pytorch lightning, and then we'll start to create more advanced models.
Hi Josh, thanks again for allowing me to break the ice between me and Pytorch. Everytime I see your videos, I wonder if my instructor could have taught us like this probably our lives must have been much simpler and happier. I have a small doubt here. In the example you have shown gradient training of only final bias. But in reality, all the weights will have to be trained during backpropagation. So when I try to initialise the all weights with random values and then train the model, I do not get the final weights as shown in the video. The code is as follows :- class BasicNN(nn.Module): def __init__(self): super().__init__() self.w00 = nn.Parameter(torch.randn(1), requires_grad = True) self.b00 = nn.Parameter(torch.randn(1), requires_grad = True) self.w01 = nn.Parameter(torch.randn(1), requires_grad = True) self.w10 = nn.Parameter(torch.randn(1), requires_grad = True) self.b10 = nn.Parameter(torch.randn(1), requires_grad = True) self.w11 = nn.Parameter(torch.randn(1), requires_grad = True) self.b_final = nn.Parameter(torch.randn(1), requires_grad = True) def forward(self, input): input_top_relu = input * self.w00 + self.b00 input_bottom_relu = input * self.w10 + self.b10 output_top_relu = F.relu(input_top_relu) * self.w01 output_bottom_relu = F.relu(input_bottom_relu) * self.w11 input_final_relu = output_top_relu + output_bottom_relu + self.b_final output = F.relu(input_final_relu) return output # Create an instance of the neural network model = BasicNN() # Print parameters print('Parameters before training') for name, param in model.named_parameters(): print(name, param.data) # Define inputs and corresponding labels inputs = torch.tensor([0., 0.5, 0.1]) labels = torch.tensor([0., 1.0, 0.]) # Define a loss function criterion = nn.MSELoss() # Define an optimizer optimizer = optim.SGD(model.parameters(), lr=0.01) # Number of epochs for training epochs = 1000 # Training loop for epoch in range(epochs): total_loss = 0 # Forward pass output = model(inputs) # Compute the loss loss = criterion(output, labels) total_loss += loss # Backward pass loss.backward() # Compute gradients optimizer.step() # Update weights optimizer.zero_grad() # Clear previous gradients # Print loss every 100 epochs if (epoch + 1) % 100 == 0: print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item()}") if (total_loss < 0.00001): print(f'Epoch = {epoch}') break # Print final parameters print('Parameters after training') for name, param in model.named_parameters(): print(name, param.data) # check the model performance input_doses = torch.linspace(start = 0, end = 1, steps = 11) output = model(input_doses) sns.set(style = 'whitegrid') sns.lineplot(x = input_doses, y = output.detach(), color = 'green', linewidth = 2) plt.xlabel("Input Doses") plt.ylabel("Effectiveness") plt.show() Request if you can help me with the code above.
Amazing job! I plan to donate to your patreon page. You were confused because we could use .backward on loss (or at least I was confused by this). I guess one explanation is that loss is defined in terms of output_i and output_i is an instance of the model class. So it may make sense that we can access the backward attribute of loss. But I was, for the same reason a bit surprised that we can subtract a scalar from output_i. One other question. Wouldn't it be better to take the average of total loss? Otherwise the condition that uses 0.0001 is dependent on the the number of examples in the training set.
great presentation!! thanks again for simplfying this topic! are you planning to post more on NN implementation? computer vision maybe or object detection?
The code demonstrated this video can be downloaded here: lightning.ai/lightning-ai/studios/statquest-introduction-to-coding-neural-networks-with-pytorch?view=public§ion=all
To learn more about Lightning: lightning.ai/
This StatQuest assumes that you are already familiar with...
Neural Networks: th-cam.com/video/CqOfi41LfDw/w-d-xo.html
Backpropagation: th-cam.com/video/IN2XmBhILt4/w-d-xo.html
The ReLU Activation Function: th-cam.com/video/68BZ5f7P94E/w-d-xo.html
Tensors: th-cam.com/video/L35fFDpwIM4/w-d-xo.html
To install PyTorch see: pytorch.org/get-started/locally/
To install matplotlib, see: matplotlib.org/stable/users/getting_started/
To install seaborn, see: seaborn.pydata.org/installing.html
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
REALLY Hope you can continue with this PyTorch tutorial.
@@yongjiewang9686 Will do!
Do we have video talking about transformer? Thanks.
@@shichengguo8064 Not yet, but soon.
Just a small comment. Any variable should not be named similar to any builtin in Python. The 'input' variable in forward should have been called something else since it is already a builtin function in Python. Otherwise, you end up overriding the builtin within that scope.
Favorite teacher with my favorite Deep learning framework. Lucky to have you. Thanks brother🙏
Wow, thanks
Please continue to go through every single line of code including the parameters with excruciating detail like you do.
None of my professors went over each line like that cuz they always "assumed we already knew" and everyone in the class who didnt already know was afraid to ask to avoid looking stupid. Thank you.
Thanks! Will do!
StatQuest is the GOAT in statistics, machine learning, and deep learning! You're videos are really helping me understanding the concepts and outline of these fields! Love from Korea!
Thank you!
Ive used PyTorch for projects before, but I can honestly say that I never fully understood the workings of building a model. I knew that i needed the peices you mentioned, but not why I needed them. You've just explained it incredibly. Please don't stop making this series!!
Thank you very much! :)
Hello! Not sure if anyone's pointed this out yet, but the code on 10:14, 12:09, and 22:42 needs a small addition, `plt.show()`, or else it won't show the graph. Though, maybe 2 years ago when this video was made you didn't need that, I'm not sure, haha.
Other than that, this is an awesome tutorial that quite literally takes anyone through the process step-by-step, and even tells you some neat fun facts (like the sns nickname) and explanations like how `loss.backward()` works.
TRIPLE BAM indeed! Thanks for the awesome tutorials and videos you put out 👍
Thanks! Did you run my code or type it in yourself? I keep the jupyter notebook updated.
plt.show() helped me get the visualizations in Ubuntu under WSL on Windows 11. Thank you for the comment.
The style of storytelling is just so unique and friendly
What a blessing this is. You are indeed the Richard Feynman of Data Science.
Thank you!
Thanks for the best content you bring. I hope you continue to make a full pytorch playlist
That's the plan!
@@statquest Thank you so much
Thank you sir. You have no idea how valuable and helpful your videos are. Keep this good work running
Thanks, will do!
AMAZING video. This is exactly what beginners need to start the Pytorch journey with a semi solid footing instead of mindless copying.
Yoy must have spent so much time for your AWESOME videos.
GREATLY appreciate your effort. Keep up the good work.
Thank you very much! :)
Was looking for a pytorch resource and was disappointed when this channel didnt have one yet but then this got uploaded. Really a blessing to the people haha
Thanks!
YOU ARE THE BEST TEACHER EVER JOSHH!! I wish you can feel the raw feeling we feel when we watch your videos
Thank you?
Man, you are love. I started my neural net journey from your videos and it's the best decision I made. Thank you
Hooray!
I have lived long enough to watch videos and understand nothing about ML stuffs, until I saw your videos. I truly wish your well being
Thank you!
I am someone who loves *SQ,* and *JS* style of teaching in byte 😅 pieces but I also hate _snakes…_ I love *JavaScript* and *TypeScript* but I’ve been learning *JavaScript* with the _strictest linting rules_ one would imagine… and given how *JavaScript* could be used without any sort of strict rules (and is very similar to *Python* in this context) it is frustrating that it makes *Python* very hard to understand despite being easier since it has not the same stricter rules I have imposed myself learning *JavaScript…* but I am also genuinely grateful that *JS* is the best instructor for this kind of topics because *JS* has a _Ukulele,_ *StatSquatch* and *Normalsaurus* which are all there to help *JS* make *SQ* awesome 🎉🎉🎉🎉 Thanks 😅😅😅❤
bam!
What a great feeling when it all clicks after learning about all these concepts in isolation. All thanks to an incredibly brilliant teacher! Triple BAM!!!
Hooray!!! Thank you!
That's really cool explanation! Please continue this PyTorch series, we really need it. BAM!
Will do!
Guess who was going to start programing a neural network in python today......
God bless you Josh, becase He know how much you are blessing me with your work.
And know that Jesus loves you and want to be part of your life.
Thanks!
Thanks for the awesome tutorial! You make the most difficult things so easy to understand, specially with the visuals and the arrows and all! The comments written on the right hand side make it so more helpful to pause and absorb. I would never miss a video of your tutorials!
Hooray! I'm glad you like my videos. :)
Thank you Josh, pls continue this series of pytorch!
Will do!
BIG LIKE before watching 👍🏻 please continue the pytorch series
Will do! :)
finally! some simple to understand content on how to make an AI model using pytourch!!! TRIPLE BAM!!!!
Hooray!
It amazes me, when I see no NLP video on StatQuest! Josh your explanation are always higher than what one can expect and you have created so many series including maths and conceptual understanding. NLP has the same importance compared to computer vision and actually people are suffering to learn it by lack of content availability! I hope you would create a series or maybe a few videos on basic concepts which help people to get interested in NLP : ) Hope you are doing good in life Josh
I'm working on NLP.
@@statquest Yay so glad to hear this, we really need you because no one gives great explanations like you do. Also your youtube comments are the nicest I've ever seen which is a testament to how valued you are in this community.
@@vans4lyf2013 Thank you very much!
Thanks so much for this gem John! Literally got a PyTorch project coming up and your timing is just perfect. Greatly appreciate the content, keep up the good work :)
Thank you!
This series about neural networks and deep learning is very well explained. Thank you soooooooo much.
Thank you!
Wonderful !!! Cant wait your pytorch lightning code for NN. Always thanks alot !!
bam! :)
Amazing content, as always. Before I was a bit afraid to start closing in torch, so thank you to encourage le to do that!
bam! You can do it! :)
Thank you very much! I am new to Deep Learning. I can say that just in one week i learned a lot of things from your tutorials!
Happy to hear that!
Thank you so much, Josh. I have been learning PyTorch and deep learning. This video helps me a lot!
Great to hear!
Man the content keeps getting better
Thank you!
Great explanation as always! Thanks for making content like this, which complements the theoretical concepts.
Glad you liked it!
I love you Josh. God bless you. You're my favorite teacher.
Thank you! 😃!
Enjoying this series on machine learning. By the way there is no shame in self promotion, you deserve it 😁
Thanks 😅
The tutorial we all needed 🙂
Hooray!
Hi Josh, sorry to be a spoil sport, but I used exact same code and my prediction is showing 0.5 for dosage of 0.5 and it is running for all 100 epoch and final b value comes out to be -16.51 😔. But yes the concept is clear!! Great work! I always ask people whoever are interested in learning about data science or machine learning to refer you channel. Seeing your channel grow from 10-20K to a Mn is pleasure to my eyes!! You are the "El Professor"!!
Thank you very much! If you look at my actual code (follow the link), you'll see that I actually pulled a trick with the data to get it to train faster.
Looking forward to seeing your following videos! Excellent explanation!
Awesome, thank you!
I love how you you visualize and synchronize the code with the maths behind it :) On top of that you are doing it step-wise which results in a really awesome and very eduSupercalifragilisticexpialidociouscational video! #ThankYou
I love it. Thank you very much! :)
Just watched matrix multiplication of Transformers. My mind was blown away. Same things appear so complex but when this guy explains them, it's like peanuts.
Triple BAM
Bam! :)
Another charming, fully informative masterpiece.
Thank you very much! BAM! :)
Wow 😮 I didn't knew I had to watch the *Neural Networks part 2* before I can watch the *The StatQuest Introduction To PyTorch* before I can watch the *Introduction to coding neural networks with PyTorch and Lightning* 🌩️ (it’s something related to the cloud I understand)
I am genuinely so happy to learn about that stuff with you Josh❤ I will go watch the other videos first and then I will back propagate to this video...
Warmer...
Quality educational content! It's so cool to see your channel grow. Been here since ~90k subs! Very well earned.
Wow! Thank you very much!!! BAM! :)
honestly wish I had this a year ago when I was struggling, still watching now tho!
Bam! :)
thanks Josh, you really make understanding Neural Networks concepts a great process!
Thank you! :)
Woo! Been waiting for this sort of a tutorial!!!
bam!
Josh explaining the code is far better than any programmer
Thank you!
Nice video, looking forward to the next ones on Pytorch Lightning !
Me too! BAM! :)
Hi! This is amazing. Are you gonna continue this series? Out of ten different rabbitholes I have been to, this video has been the most helpful for me with understanding PyTorch and starting off with my project. Please continue making more complicated models. Thank you :)
That's the plan!
I am also learning Deep Learning, and want to apply it to make good projects,
This is going to be great.
bam!
That is a big leap. I need to check it several times to understand it since I am not a programmer. However, I really got a good feeling of what is happening inside the code. I actually use codeless systems such as KNIME. So if Mr. Sasquatch, get the idea of using KNIME to explain all this, It will be amazing. Thanks to be such a good teacher.
I'll keep that in mind.
Another excellent video, one humble request please provide video on Stable Diffusion Models.
I'll keep that in mind.
Absolutely brilliant!
Thank you! :)
Thank you, good explanation!
16:00 Python prefers for-each-loops over index-based loops. See how this equivalent for-each loop looks much simpler.
for input, label in zip(inputs, labels):
output = model(input)
loss = (output - label)**2
loss.backward()
total_loss += float(loss)
Great tip!
better than MIT or any university slides
Thank you!
Awesome vid by the legend!
Thank you!
bless josh and this channel
Thank you!
Please make an entire tutorial about the ins and outs of PyTorch!
I've made several PyTorch videos and will continue to make more. You can find the others here: statquest.org/video-index/
Great explanation. Thank you very much
Thanks!
Thank you very much Mr Josh Starmer
Thanks!
very well explained, thank you for providing quality content, it's very helpful
Glad it was helpful!
it's great that you are making videos on coding as well.
Thank you!
Hey Josh!
Guess what just arrived in the mail....
My new statquest mug!!!!!
Hooray!!!
BAM!!! Thank you so much for supporting StatQuest!!!
Hi Josh, thanks for your video. I am confused why it doesn't work when I am trying to optimize any other weights and bias.
five minutes later, I think I have got the answer from the comments and your reply. Thanks again!
bam
Nice video for the introduction of LSTM using PyTorch. There is also `torch` R package that doesn't need to install python and torch. It's so nice that R also has deep learning framework aside from `tensorflow` and I recommend you to maybe try it.
Thanks for the info!
@@statquest i strongly recommend it because it is so nice that R has own deep learning frameworks, besides h2o
Great series.
Thank you!
great video and explanation! me have been struggling in pytorch coding
Bam! :)
Thanks for this amazing walk through.
Thanks!
Hello Josh! Thank you so much for your amazing videos! I have learned so much from your tutorials and would not have been able to advance without them!
I wanted to ask whether it is possible for you to put some videos on times series analysis, including autoregression (AR), moving average (MA) and their combinations. I would be more than grateful if you can provide such a video. Thank you so much.
I'll keep those topics in mind!
Your teaching video is awesome
Thank you!
@@statquest Do you have intro to lightning ? I kind of remember you mentioned in the video you seemed to have one?
@@ぶらえんぴん That's going to be the next video in this series. It will come out in a few weeks.
This was great... I hope you can simplify Tensorflow the same way... big big thank you.
Thanks!
Thank you for perfect lecture~!!!
Thank you!
Thank you Josh!
Bam!
That is how teaching should be.. Triple Bam
Thanks!
Best tutorial like usual! would be nice to see more advanced examples of in pytorch, like CNN for image classification :)
I'm working on them.
Thanks a lot, beg for Pytorch Series playlist.
Soon!
Hi Josh. I am a big fan of your videos. I have a question regarding this quest. In this video, we optimized only one parameter. How can we optimize all the parameters? Thanks in advance.
I show how to impute all of the parameters in this video on LSTMs in PyTorch: th-cam.com/video/RHGiXPuo_pI/w-d-xo.html (if you want to learn about the theory of LSTMs, see: th-cam.com/video/YCzL96nL7j0/w-d-xo.html
Thanks Josh, its Incredible video. Beside, recently the Bayes theorem application in fitting model (linear, logistic, random forest...) has became more and more popular in order to replace classic statistic method, could you pls take some time to explain to us some of its popular algorithm like BART, Linear regression via Bayesian Methods...
I'm planning on doing a whole series on Bayesian stuff as soon as I finish this series on neural networks.
@@statquest that's great news for today, thanks Josh, Im looking forward to see it soon
omg! I have really wanted this! awesome!!! :) :) :)
Hooray! :)
That Was Nice ! Thank You
Glad you liked it!
I would buy anything that you promote with the songs at the beginning.
Thanks! My book, The StatQuest Illustrated Guide to Machine Learning, starts with a song! (and has a another song midway through): statquest.org/statquest-store/
Double bam new way to teach coding
yes! :)
MEGABAMMMMMM.....
Hey josh... It's been a very long long time.... I am occupied with different subject right now..
Hope you are doing good... Will catch you soon..
Welcome back!
So I paused at 18:04 because _it blew my mind_ that we were calling backwards() on the loss variable because I thought it was defined on the line above… 😅😅😅😅 but yeah I didn’t find anything so one hour later I was just watching the rest of the video and _to be honest_ in about 33 seconds it came out that it was normal for _my mind to be blown_ 😂😂😂😂 at 18:37
Totally! I was like, "what?!?!?" when I first saw that.
Sir, Please make videos on the time-series analysis, it's hard to find the videos with clear explaination.
I'll keep that in mind.
wow.. super excited
Hooray!
Soooooooo thankful!
bam! :)
Hello! I've got a bit confused: at 15:08 you mention that at each epoch we'll be running all 3 data points through the model, but wasn't the point of SDG in that we would only need a single data point per epoch, or am I misunderstanding something? Btw, despite my confusion, this is by far the best ML guide series I've seen, thank you for your work!
That's a good question. "torch.optim" doesn't have a gradient descent optimizer, just a stochastic gradient descent optimizer. So we import torch.optim.SGD and then pass it all of the residuals to get gradient descent.
@@statquest Makes sense, thank you for the clarification!
Tensorflow developer is turning into PyTorch… bam! 💥
bam! :)
great video! very well explained!!!👍👍
Thank you!
Hey Josh!
Amazing videos, thanks a lot.
Would be great if you could cover Time Series Data and algorithms like ARIMA and HOLTS WINTER
Thanks😊
I'll keep those topics in mind.
this video is gold
Thank you!
I would be appreciated if you can teach more an deeper in pytorch. Thank you so much!
That's the plan. This is just the first of many videos on how to code neural networks. The next video will be on pytorch lightning, and then we'll start to create more advanced models.
Hi Josh, thanks again for allowing me to break the ice between me and Pytorch. Everytime I see your videos, I wonder if my instructor could have taught us like this probably our lives must have been much simpler and happier. I have a small doubt here. In the example you have shown gradient training of only final bias. But in reality, all the weights will have to be trained during backpropagation. So when I try to initialise the all weights with random values and then train the model, I do not get the final weights as shown in the video. The code is as follows :-
class BasicNN(nn.Module):
def __init__(self):
super().__init__()
self.w00 = nn.Parameter(torch.randn(1), requires_grad = True)
self.b00 = nn.Parameter(torch.randn(1), requires_grad = True)
self.w01 = nn.Parameter(torch.randn(1), requires_grad = True)
self.w10 = nn.Parameter(torch.randn(1), requires_grad = True)
self.b10 = nn.Parameter(torch.randn(1), requires_grad = True)
self.w11 = nn.Parameter(torch.randn(1), requires_grad = True)
self.b_final = nn.Parameter(torch.randn(1), requires_grad = True)
def forward(self, input):
input_top_relu = input * self.w00 + self.b00
input_bottom_relu = input * self.w10 + self.b10
output_top_relu = F.relu(input_top_relu) * self.w01
output_bottom_relu = F.relu(input_bottom_relu) * self.w11
input_final_relu = output_top_relu + output_bottom_relu + self.b_final
output = F.relu(input_final_relu)
return output
# Create an instance of the neural network
model = BasicNN()
# Print parameters
print('Parameters before training')
for name, param in model.named_parameters():
print(name, param.data)
# Define inputs and corresponding labels
inputs = torch.tensor([0., 0.5, 0.1])
labels = torch.tensor([0., 1.0, 0.])
# Define a loss function
criterion = nn.MSELoss()
# Define an optimizer
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Number of epochs for training
epochs = 1000
# Training loop
for epoch in range(epochs):
total_loss = 0
# Forward pass
output = model(inputs)
# Compute the loss
loss = criterion(output, labels)
total_loss += loss
# Backward pass
loss.backward() # Compute gradients
optimizer.step() # Update weights
optimizer.zero_grad() # Clear previous gradients
# Print loss every 100 epochs
if (epoch + 1) % 100 == 0:
print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item()}")
if (total_loss < 0.00001):
print(f'Epoch = {epoch}')
break
# Print final parameters
print('Parameters after training')
for name, param in model.named_parameters():
print(name, param.data)
# check the model performance
input_doses = torch.linspace(start = 0, end = 1, steps = 11)
output = model(input_doses)
sns.set(style = 'whitegrid')
sns.lineplot(x = input_doses, y = output.detach(), color = 'green', linewidth = 2)
plt.xlabel("Input Doses")
plt.ylabel("Effectiveness")
plt.show()
Request if you can help me with the code above.
This example only works to optimize the final bias term.
Amazing job! I plan to donate to your patreon page. You were confused because we could use .backward on loss (or at least I was confused by this). I guess one explanation is that loss is defined in terms of output_i and output_i is an instance of the model class. So it may make sense that we can access the backward attribute of loss. But I was, for the same reason a bit surprised that we can subtract a scalar from output_i. One other question. Wouldn't it be better to take the average of total loss? Otherwise the condition that uses 0.0001 is dependent on the the number of examples in the training set.
Taking the average is really common, but it doesn't change anything.
No, I know it doesn't change things but it means that your criteria has to be adjusted depending on the size of the dataset doesn't it?
@@pauledam2174 If we wanted to compare the loss among different datasets, then the average would be helpful.
great presentation!! thanks again for simplfying this topic! are you planning to post more on NN implementation? computer vision maybe or object detection?
Yes, there will be many more videos on how to implement NNs.
Really Awesome
Thanks 🤗
So interesting videos and good explanations. I am wondering which software you use to make these cool visualizations?
I share all my secrets here: th-cam.com/video/crLXJG-EAhk/w-d-xo.html
Triple bam!
BAM! Thank you very much for supporting StatQuest!!!!
Thanks for this
:)
Thank you :)
You're welcome!