Examples
Tutorial 1: Introduction to PyTorchThis tutorial will give a short introduction to PyTorch basics, and get you setup for writing your own neural networks. This notebook is part of a lecture se…Tutorial 2: Activation FunctionsIn this tutorial, we will take a closer look at (popular) activation functions and investigate their effect on optimization properties in neural networks. Ac…Tutorial 3: Initialization and OptimizationIn this tutorial, we will review techniques for optimization and initialization of neural networks. When increasing the depth of neural networks, there are v…Tutorial 4: Inception, ResNet and DenseNetIn this tutorial, we will implement and discuss variants of modern CNN architectures. There have been many different architectures been proposed over the pas…Tutorial 5: Transformers and Multi-Head AttentionIn this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model. Since the paper Attention Is All You Ne…Tutorial 6: Basics of Graph Neural NetworksIn this tutorial, we will discuss the application of neural networks on graphs. Graph Neural Networks (GNNs) have recently gained increasing popularity in bo…Tutorial 7: Deep Energy-Based Generative ModelsIn this tutorial, we will look at energy-based deep learning models, and focus on their application as generative models. Energy models have been a popular t…Tutorial 8: Deep AutoencodersIn this tutorial, we will take a closer look at autoencoders (AE). Autoencoders are trained on encoding input data such as images into a smaller feature vect…Tutorial 9: Normalizing Flows for Image ModelingIn this tutorial, we will take a closer look at complex, deep normalizing flows. The most popular, current application of deep normalizing flows is to model …Tutorial 10: Autoregressive Image ModelingIn this tutorial, we implement an autoregressive likelihood model for the task of image modeling. Autoregressive models are naturally strong generative model…Tutorial 11: Vision TransformersIn this tutorial, we will take a closer look at a recent new trend: Transformers for Computer Vision. Since [Alexey Dosovitskiy et al.](https://openreview.ne…Tutorial 12: Meta-Learning - Learning to LearnIn this tutorial, we will discuss algorithms that learn models which can quickly adapt to new classes and/or tasks with few samples. This area of machine lea…Tutorial 13: Self-Supervised Contrastive Learning with SimCLRIn this tutorial, we will take a closer look at self-supervised contrastive learning. Self-supervised learning, or also sometimes called unsupervised learnin…GPU and batched data augmentation with Kornia and PyTorch-LightningIn this tutorial we will show how to combine both Kornia and PyTorch Lightning to perform efficient data augmentation to train a simple model using the GPU i…Barlow Twins TutorialThis notebook describes the self-supervised learning method Barlow Twins. Barlow Twins differs from other recently proposed algorithms as it doesn't fall und…PyTorch Lightning Basic GAN TutorialHow to train a GAN! Main takeaways: 1. Generator and discriminator are arbitrary PyTorch modules. 2. training_step does both the generator and discriminator …PyTorch Lightning CIFAR10 ~94% Baseline TutorialTrain a Resnet to 94% accuracy on Cifar10!PyTorch Lightning DataModulesThis notebook will walk you through how to start using Datamodules. With the release of `pytorch-lightning` version 0.9.0, we have included a new class calle…Fine-Tuning SchedulerThis notebook introduces the [Fine-Tuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension and demonstrates the use of …Introduction to PyTorch LightningIn this notebook, we'll go over the basics of lightning by preparing models to train on the [MNIST Handwritten Digits dataset](https://en.wikipedia.org/wiki/…TPU training with PyTorch LightningIn this notebook, we'll train a model on TPUs. Updating one Trainer flag is all you need for that. The most up to documentation related to TPU training can b…How to train a Deep Q NetworkMain takeaways: 1. RL has the same flow as previous models we have seen, with a few additions 2. Handle unsupervised learning by using an IterableDataset whe…Finetune Transformers Models with PyTorch LightningThis notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. Then, we write a class to perform text…How to write a PyTorch Lightning tutorialThis is a template to show how to contribute a tutorial.
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