Lightning-Sandbox documentation

---

Tutorial 1: Introduction to PyTorch

This 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 series on Deep...

GPU/TPU,UvA-DL-Course

Tutorial 2: Activation Functions

In this tutorial, we will take a closer look at (popular) activation functions and investigate their effect on optimization properties in neural networks. Activation functions...

GPU/TPU,UvA-DL-Course

Tutorial 3: Initialization and Optimization

In this tutorial, we will review techniques for optimization and initialization of neural networks. When increasing the depth of neural networks, there are various challenges...

Image,Initialization,Optimizers,GPU/TPU,UvA-DL-Course

Tutorial 4: Inception, ResNet and DenseNet

In this tutorial, we will implement and discuss variants of modern CNN architectures. There have been many different architectures been proposed over the past few years. Some...

Image,GPU/TPU,UvA-DL-Course

Tutorial 5: Transformers and Multi-Head Attention

In 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 Need by Vaswani et...

Text,GPU/TPU,UvA-DL-Course

Tutorial 6: Basics of Graph Neural Networks

In this tutorial, we will discuss the application of neural networks on graphs. Graph Neural Networks (GNNs) have recently gained increasing popularity in both applications...

Graph,GPU/TPU,UvA-DL-Course

Tutorial 7: Deep Energy-Based Generative Models

In 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 tool before the...

Image,GPU/TPU,UvA-DL-Course

Tutorial 8: Deep Autoencoders

In 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 vector, and afterward,...

Image,GPU/TPU,UvA-DL-Course

Tutorial 9: Normalizing Flows for Image Modeling

In 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 datasets of...

Image,GPU/TPU,UvA-DL-Course

Tutorial 10: Autoregressive Image Modeling

In this tutorial, we implement an autoregressive likelihood model for the task of image modeling. Autoregressive models are naturally strong generative models that constitute...

Image,GPU/TPU,UvA-DL-Course

Tutorial 11: Vision Transformers

In this tutorial, we will take a closer look at a recent new trend: Transformers for Computer Vision. Since [Alexey Dosovitskiy et...

Image,GPU/TPU,UvA-DL-Course

Electricity Price Forecasting with N-BEATS

This tutorial covers using Lightning Flash and it's integration with PyTorch Forecasting to train an autoregressive model (N-BEATS) on hourly electricity pricing data. We show...

Tabular,Forecasting,Timeseries,GPU/TPU,Kaggle

Image Classification on Hymenoptera Dataset

In this tutorial, we'll go over the basics of lightning Flash by finetuning/predictin with an ImageClassifier on [Hymenoptera...

Image-Classification,Image,GPU/TPU,Kaggle

Tabular Classification on Titanic Dataset

In this notebook, we'll go over the basics of lightning Flash by training a TabularClassifier on [Titanic Dataset](https://www.kaggle.com/c/titanic).

Tabular-Classification,Tabular,GPU/TPU,Kaggle

Finetuning a Text Classifier on IMDB Dataset

In this notebook, we'll go over the basics of lightning Flash by finetunig a TextClassifier on IMDB Dataset.

Text-Classification,Text,GPU/TPU,Kaggle

PyTorch Lightning CIFAR10 ~94% Baseline Tutorial

Train a Resnet to 94% accuracy on Cifar10!

Image,GPU/TPU,Lightning-Examples

PyTorch Lightning DataModules

This 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 called...

GPU/TPU,Lightning-Examples

Fine-Tuning Scheduler

This notebook introduces the [Fine-Tuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension and demonstrates the use of it to fine-tune a...

Fine-Tuning,GPU/TPU,Lightning-Examples

Multi-agent Reinforcement Learning With WarpDrive

This notebook introduces multi-agent reinforcement learning (MARL) with WarpDrive (Lan et al. https://arxiv.org/abs/2108.13976). WarpDrive is a flexible, lightweight, and...

Reinforcement-Learning,Multi-agent,GPU,GPU/TPU,Lightning-Examples

Simple image classification with Lightning Flash

This is a template to show simple image classification case if for some reason accelerator is required.

Image,GPU/TPU,templates

How to write a PyTorch Lightning tutorial

This is a template to show how to contribute a tutorial.

GPU/TPU,templates

Solving Titanic dataset with Lightning Flash

This is a template to show how to contribute a tutorial.

GPU/TPU,templates

Start here

• Tutorial 1: Introduction to PyTorch
• Tutorial 2: Activation Functions
• Tutorial 3: Initialization and Optimization
• Tutorial 4: Inception, ResNet and DenseNet
• Tutorial 5: Transformers and Multi-Head Attention
• Tutorial 6: Basics of Graph Neural Networks
• Tutorial 7: Deep Energy-Based Generative Models
• Tutorial 8: Deep Autoencoders
• Tutorial 9: Normalizing Flows for Image Modeling
• Tutorial 10: Autoregressive Image Modeling
• Tutorial 11: Vision Transformers
• Electricity Price Forecasting with N-BEATS
• Image Classification on Hymenoptera Dataset
• Finetuning
• Tabular Classification on Titanic Dataset
• Training
• Predicting
• Finetuning a Text Classifier on IMDB Dataset
• Finetuning
• PyTorch Lightning CIFAR10 ~94% Baseline Tutorial
• PyTorch Lightning DataModules
• Fine-Tuning Scheduler
• Multi-agent Reinforcement Learning With WarpDrive
• Simple image classification with Lightning Flash
• How to write a PyTorch Lightning tutorial
• Solving Titanic dataset with Lightning Flash

Indices and tables

• Index

• Module Index

• Search Page