PyTorch-ML

PyTorch model building essentials

PyTorch has four (give or take) essential modules you can use to create almost any kind of neural network you can imagine. They are torch.nn, torch.optim, torch.utils.data.Dataset and torch.utils.data.DataLoader. For now, we'll focus on the first two and get to the other two later (though you may be able to guess what they do).

PyTorch module	What does it do?
torch.nn	Contains all of the building blocks for computational graphs (essentially a series of computations executed in a particular way).
torch.nn.Parameter	Stores tensors that can be used with nn.Module. If requires_grad=True gradients (used for updating model parameters via gradient descent) are calculated automatically, this is often referred to as "autograd".
torch.nn.Module	The base class for all neural network modules, all the building blocks for neural networks are subclasses. If you're building a neural network in PyTorch, your models should subclass nn.Module. Requires a forward() method be implemented.
torch.optim	Contains various optimization algorithms (these tell the model parameters stored in nn.Parameter how to best change to improve gradient descent and in turn reduce the loss).
def forward()	All nn.Module subclasses require a forward() method, this defines the computation that will take place on the data passed to the particular nn.Module (e.g. the linear regression formula above).

If the above sounds complex, think of like this, almost everything in a PyTorch neural network comes from torch.nn,

nn.Module contains the larger building blocks (layers)
nn.Parameter contains the smaller parameters like weights and biases (put these together to make nn.Module(s))
forward() tells the larger blocks how to make calculations on inputs (tensors full of data) within nn.Module(s)
torch.optim contains optimization methods on how to improve the parameters within nn.Parameter to better represent input data

Books:

Name		Name	Last commit message	Last commit date
Latest commit History 78 Commits
Basics		Basics
CNN		CNN
Deepchecks		Deepchecks
Images		Images
Inside-Deep-Learning		Inside-Deep-Learning
LLM-Torch		LLM-Torch
TPU-pytorch		TPU-pytorch
Transfer-Learning		Transfer-Learning
notebooks		notebooks
pytorch_lightning		pytorch_lightning
01_pytorch_workflow.ipynb		01_pytorch_workflow.ipynb
DataParallel.md		DataParallel.md
Face_swapping.ipynb		Face_swapping.ipynb
LSTMs_in_PyTorch_🔥.ipynb		LSTMs_in_PyTorch_🔥.ipynb
LinearRegression_PyTorch.ipynb		LinearRegression_PyTorch.ipynb
PyTorch_course_1.ipynb		PyTorch_course_1.ipynb
Pytorch_1.ipynb		Pytorch_1.ipynb
README.md		README.md
classification_pytorch.ipynb		classification_pytorch.ipynb
cool-repos.md		cool-repos.md
imagenet_classes.txt		imagenet_classes.txt
torchgeo.md		torchgeo.md

Provide feedback