Learning PyTorch with Examples
This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. At its core, PyTorch provides two main features: An n-dimensional Tensor, similar to numpy but can run on GPUs Automatic differentiation for building and training neural networks
Building Models with PyTorch
As a simple example, here’s a very simple model with two linear layers and an activation function. We’ll create an instance of it and ask it to report on its parameters:
PyTorch Examples — PyTorchExamples 1.11 documentation
This example demonstrates how to train a multi-layer recurrent neural network (RNN), such as Elman, GRU, or LSTM, or Transformer on a language modeling task by using the Wikitext-2 dataset. GO TO EXAMPLE
Build the Neural Network — PyTorch Tutorials 2.7.0+cu126 …
Let’s break down the layers in the FashionMNIST model. To illustrate it, we will take a sample minibatch of 3 images of size 28x28 and see what happens to it as we pass it through the network.
Training with PyTorch — PyTorch Tutorials 2.7.0+cu126 …
Once you’ve loaded the model, it’s ready for whatever you need it for - more training, inference, or analysis. Note that if your model has constructor parameters that affect model structure, you’ll need to provide them and configure the model identically to the state in which it was saved.
Training a Classifier — PyTorch Tutorials 2.7.0+cu126 documentation
Because your network is really small. Exercise: Try increasing the width of your network (argument 2 of the first nn.Conv2d, and argument 1 of the second nn.Conv2d – they need to be the same number), see what kind of speedup you get. Goals achieved: Understanding PyTorch’s Tensor library and neural networks at a high level.
Sequence Models and Long Short-Term Memory Networks
This might not be the behavior we want. Sequence models are central to NLP: they are models where there is some sort of dependence through time between your inputs. The classical example of a sequence model is the Hidden Markov Model for part-of-speech tagging. Another example is the conditional random field.
Neural Networks — PyTorch Tutorials 2.7.0+cu126 documentation
Neural networks can be constructed using the torch.nn package. Now that you had a glimpse of autograd, nn depends on autograd to define models and differentiate them. An nn.Module contains layers, and a method forward(input) that returns the output. For example, look at this network that classifies digit images:
Welcome to PyTorch Tutorials — PyTorch Tutorials 2.7.0+cu126 …
Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide.
Reinforcement Learning (DQN) Tutorial - PyTorch
This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v1 task from Gymnasium. You might find it helpful to read the original Deep Q Learning (DQN) paper