Introduction to PyTorch 

The colors make it really hard to read the slides

This was quite a good rapid intro video. Thank you very much!

I got a “name ‘transform’ is not defined”when creating a CIFAR10 dataset on 15:14. Still wondering how to fix this.

Am I the only one that feels the display brightness is too low?

PyTorch only has 27K subscribers ?

Love how he roasts his CPU while training

7:04 it should be (torch.rand(2,2) -0.5) * 2, otherwise it works like torch.rand(2,2) -1, because of operator precedence

If PyTorch is so smart, why can't you use it to increase your video BRIGHTNESS GODD!!!

Very hard to read the slide due to unfortunate choices of font colors (no contrast)

0:00 Intro 0:52 PyTorch Installation 1:32 What is PyTorch 3:51 Tensors 7:56 Autograd 10:00 A simple model 14:00 Datasets 17:00 Training Loop 21:15 Deployment

loss.backward() does not compute at 9:42 lol

For me "images, labels = dataiter.next()" returns the following error: "AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'next'". I fixed this by changing the line to "images, labels = next(dataiter)" which I think is the correct implementation. But I am still confused, surely an official tutorial video is not mistaken.

Audio volume is a bit low.

Hello, something might be wrong there. at 11:01 we have the C1 size (28*28), and the code in the comment said "# 1 input image channel (black & white), 6 output channels, 3x3 square convolution". But apply a 3*3 window on 32*32 input will get 30*30 output. I think the graph describes the structure in the original paper which was using 5*5 window at C1.

Is there a typo ~ 15:10 when the image normalization is done? if we're seeking to achieve an average of 0, shouldn't the first tuple passed in be (0, 0, 0) instead of (0.5, 0.5, 0.5)?

Is it just my machine, or is this video kinda dark?

@11:35 LeNet model for the 32X32 one channel images. import torch import torch.nn as nn import torch.nn.functional as F class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(1, 6,3) self.conv2 = nn.Conv2d(6, 16, 3) self.fc1 = nn.Linear(16*6*6, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84,10) self.relu = F.relu def forward(self , x): x = self.conv1(x) #input shape 32X32 -> ouput shape 30X30 x = self.relu(x) x = F.max_pool2d(x ,2) #input shape 30X30 -> ouput shape 15X15 x = self.conv2(x) #input shape 15X15 -> ouput shape 13X13 x = self.relu(x) x = F.max_pool2d(x ,2) #input shape 13X13 -> ouput shape 6X6 x = torch.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x net = LeNet() input_image = torch.rand(1, 1, 32, 32) output = net(input_image) output

I like the introduction video as I could see how you can use Pytorch to train model, visualize results and deploying in production. You helped me and I just want to say thank you.

increase your browser window man. hard to read

HI, I get this error when runnung the dataset notebook. "URLError: " Can it be fixed?

WHY THE VIDEO IS SO DARK?

I think in code segment 6 the first line should be r1 = (torch.rand(2,2)-0.5) * 2 otherwise it will be equivalent to r1 = torch.rand(2,2) - 1

I found this helpful. Thanks for sharing this tutorial! I found a problem in the Autograd section, which could be corrected. I'm using Pytorch2.1.1 and it seems that `loss.backdward()` requires that the tensors get constructed with `requires_grad=True`. I checked the documentation and it seems that this is the default, but it didn't work for me until I specified the value explicitly.

@7:14 r = (torch.rand(2,2) - 0.5) * 2 # normalize values to -1. to 1. r = torch.rand(2,2) - 0.5 * 2 # generates random values in range of [-1., 0.] @8:00 Set requires_grad = True example: x = torch.rand(1, 10, requires_grad = True)

good videos but audio quality in these videos is very poor, anyway to improve it?

Obviously this is a year old and the narrator probably won't see this but a zoomed in view would make this infinitely more accessible on mobile. Great video regardless.

Where do I run these commands??? I am trying to teach myself this stuff on the fly but my wings are on fire. I only want to learn ths stuff to stop the damn cuda errors on my Stable Diffusion local install. It's driving me nuts! I don't know where to put the "torch.cuda.empty_cache()" either. Can someone please help me?

@8:00 Set requires_grad = True example: x = torch.rand(1, 10, requires_grad = True)

the video's dim, very difficult to follow

16:54 I get an error on dataiter.next() - next(dataiter) works

Does PyTorch have something equivalent to TensorFlow Probability?

Thanks for the video. If the CIFAR10 dataset isn't downloading by the code, you may for as below: import ssl ssl._create_default_https_context = ssl._create_unverified_context trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)

Slides are not readable.

o boy is the noise because of model training 😏

It is only reading the scripts! We can read them too! Unfortunately not a perfect tutorial 😒

thanks a lot. very good video. It was littleee fast i think. And best thing was there was no ad for me :)

great video. in the net class at 18.03 needs num_flat_features function without which it wont run looks like.

what is this interface that starts from 4:35 ? where am I gonna actually write this codes if iam onto building some model using Pytorch?

I dont seem to understand the AutoGrad aspect as im doing this with no prior ML/DL knowledge. Am i on the right track, or must i learn something prior to his

For God's sake please increase your text size

Loved that Video! Thank you so much

nice "how to read the text" tutorial!

1729, the hipster version of 42

Thank you for the video

Glad to see 720p back.

Is this synthetic voice?

Great introduction! Thanks

An excellent tutorial!! Thank you.

It really help a lot!😀

useful

How does torchscript compare to onnx?

Low brightness, low contrast, low readability, low volume, content is good, but I don't like the mouth sounds in between the words, very distracting, and these are pronounced because I have to increase the volume too high to listen to the content. Kya official video banayega re tu.

There is a background noise that continuously comes forth . It's a static sound that occurs in the second last module. Request you to kindly check it out .