book

Deep Learning with PyTorch Lightning

Name: Deep Learning with PyTorch Lightning
ISBN: 9781800561618

by Kunal Sawarkar, Dheeraj Arremsetty

April 2022

Intermediate to advanced

366 pages

7h 43m

English

Packt Publishing

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Deep Learning with PyTorch Lightning
ContributorsAbout the authorsAcknowledgementsAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the colour imagesConventions usedGet in touchShare Your Thoughts
Section 1: Kickstarting with PyTorch Lightning
Chapter 1: PyTorch Lightning Adventure
What makes PyTorch Lightning so special?The first one….So many frameworks? PyTorch versus TensorFlowA golden mean – PyTorch Lightning <pip install> – My Lightning adventure Understanding the key components of PyTorch Lightning DL pipelinePyTorch Lightning abstraction layersCrafting AI applications using PyTorch LightningImage recognition modelsTransfer learningNLP Transformer modelsLightning FlashTime series models with LSTMGenerative Adversarial Networks with AutoencodersSelf-Supervised models combining CNN and RNNSelf-Supervised models for contrastive learningDeploying and scoring modelsScaling models and productivity tipsFurther readingSummary
Chapter 2: Getting off the Ground with the First Deep Learning Model
Technical requirementsGetting started with Neural NetworksWhy Neural Networks?About the XOR operatorMLP architectureBuilding a Hello World MLP modelImporting librariesPreparing the dataConfiguring the modelTraining the modelLoading the modelMaking predictionsBuilding our first Deep Learning modelSo, what makes it deep?CNN architectureBuilding a CNN model for image recognitionImporting the packagesCollecting the dataPreparing the dataBuilding the modelTraining the modelEvaluating the accuracy of the modelModel improvement exercisesSummary
Chapter 3: Transfer Learning Using Pre-Trained Models
Technical requirementsGetting started with transfer learningAn image classifier using a pre-trained ResNet-50 architecturePreparing the data Extracting the datasetPre-processing the datasetLoading the datasetBuilding the modelTraining the modelEvaluating the accuracy of the model Text classification using BERT transformersCollecting the dataPreparing the datasetSetting up the DataLoader instancesBuilding the modelSetting up model training and testingTraining the modelEvaluating the modelSummary
Chapter 4: Ready-to-Cook Models from Lightning Flash
Technical requirementsGetting started with Lightning FlashFlash is as simple as 1-2-3Video classification using FlashSlow and SlowFast architecture Importing librariesLoading the datasetConfiguring the backbone Fine-tuning the modelMaking predictions using the modelAutomatic speech recognition using FlashInstalling Libraries Importing librariesLoading the datasetConfiguring the backbone Fine-tuning the modelSpeech prediction using the modelFurther learningSummary
Section 2: Solving using PyTorch Lightning
Chapter 5: Time Series Models
Technical requirementsIntroduction to time seriesTime series forecasting using Deep LearningGetting started with time series modelsTraffic volume forecasting using the LSTM time series modelDataset analysisFeature engineeringCreating a custom datasetConfiguring the LSTM model using PyTorch Lightning Setting up the optimizerTraining the modelMeasuring the training lossLoading the modelA prediction on the test datasetThe next stepsSummary
Chapter 6: Deep Generative Models
Technical requirementsGetting started with GAN modelsWhat is a GAN? Creating new food items using a GANLoading the datasetFeature engineering utility functionsThe discriminator modelThe generator modelThe generative adversarial modelTraining the GAN modelThe model output showing fake imagesCreating new butterfly species using a GANGAN training challengesCreating images using DCGAN Summary

Chapter 7: Semi-Supervised Learning
Technical requirementsGetting started with semi-supervised learningGoing through the CNN–RNN architectureGenerating captions for imagesDownloading the datasetAssembling the dataTraining the modelGenerating the captionNext stepsSummary
Chapter 8: Self-Supervised Learning
Technical requirementsGetting started with Self-Supervised LearningSo, what does it mean to be Self-Supervised?What is Contrastive Learning?SimCLR architectureHow does SimCLR work?SimCLR model for image recognitionCollecting the datasetSetting up data augmentationLoading the datasetTraining configuration Model trainingModel evaluationNext stepsSummary
Section 3: Advanced Topics
Chapter 9: Deploying and Scoring Models
Technical requirementsDeploying and scoring a Deep Learning model nativelyThe pickle (.PKL) model file formatDeploying our Deep Learning model Saving and loading model checkpointsDeploying and scoring a model using FlaskDeploying and scoring inter-portable modelsWhat is the ONNX format? Why does it matter?Saving and loading the ONNX modelDeploying and scoring the ONNX model using FlaskNext stepsFurther readingSummary
Chapter 10: Scaling and Managing Training
Technical RequirementsManaging training Saving model hyperparametersEfficient debugging Monitoring the training loss using TensorBoardScaling up trainingSpeeding up model training using a number of workersGPU/TPU trainingMixed precision training/16-bit trainingControlling trainingSaving model checkpoints when using the cloudChanging the default behavior of the checkpointing featureResuming training from a saved checkpointSaving downloaded and assembled data when using the cloudFurther readingSummary
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Content preview from Deep Learning with PyTorch Lightning

Chapter 8: Self-Supervised Learning

Since the dawn of Machine Learning, the field has been neatly divided into two camps: supervised learning and unsupervised learning. In supervised learning, there should be a labeled dataset available, and if that is not the case, then the only option left is unsupervised learning. While unsupervised learning may sound great as it can work without labels, in practice, the applications of unsupervised methods such as clustering are quite limited. There is also no easy option to evaluate the accuracy of unsupervised methods or to deploy them.

The most practical Machine Learning applications tend to be supervised learning applications (for example, recognizing objects in images, predicting future stock prices ...

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Deep Learning with PyTorch Lightning

by Kunal Sawarkar, Dheeraj Arremsetty

Chapter 8: Self-Supervised Learning

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