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Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

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This section contains specialized tutorials focused on applying PyTorch to specific application areas. These guides demonstrate how to use domain-specific libraries like torchvision, torchaudio, and others. This section is for developers looking to implement PyTorch in particular fields of deep learning.

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TorchVision Object Detection Finetuning Tutorial

Finetune a pre-trained Mask R-CNN model.

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Transfer Learning for Computer Vision Tutorial

Train a convolutional neural network for image classification using transfer learning.

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Adversarial Example Generation

Train a convolutional neural network for image classification using transfer learning.

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DCGAN Tutorial

Train a generative adversarial network (GAN) to generate new celebrities.

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Spatial Transformer Networks Tutorial

Learn how to augment your network using a visual attention mechanism.

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Semi-Supervised Learning Tutorial Based on USB

Learn how to train semi-supervised learning algorithms (on custom data) using USB and PyTorch.

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Reinforcement Learning (DQN)

Learn how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym.

Reinforcement-Learning

Reinforcement Learning (PPO) with TorchRL

Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the Inverted Pendulum task from Gym.

Reinforcement-Learning

Train a Mario-playing RL Agent

Use PyTorch to train a Double Q-learning agent to play Mario.

Reinforcement-Learning

Recurrent DQN

Use TorchRL to train recurrent policies

Reinforcement-Learning

Code a DDPG Loss

Use TorchRL to code a DDPG Loss

Reinforcement-Learning

Writing your environment and transforms

Use TorchRL to code a Pendulum

Reinforcement-Learning