Mastering Pytorch : Create and Deploy Deep Learning Models from CNNs to Multimodal Models, LLMs, and Beyond / Ashish Ranjan Jha.

Format:

Book

Author/Creator:

Jha, Ashish Ranjan, author.

Series:

Expert insight.

Expert insight

Language:

English

Subjects (All):

Neural networks (Computer science).

Python (Computer program language).

Artificial intelligence.

Physical Description:

1 online resource (559 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2024]

Summary:

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Chapter 1: Overview of Deep Learning Using PyTorch

A refresher on deep learning

Activation functions

Optimization schedule

Exploring the PyTorch library in contrast to TensorFlow

Tensor modules

PyTorch modules

torch.nn

torch.optim

torch.utils.data

Training a neural network using PyTorch

Summary

Reference list

Chapter 2: Deep CNN Architectures

Why are CNNs so powerful?

Evolution of CNN architectures

Developing LeNet from scratch

Using PyTorch to build LeNet

Training LeNet

Testing LeNet

Fine-tuning the AlexNet model

Using PyTorch to fine-tune AlexNet

Running a pretrained VGG model

Exploring GoogLeNet and Inception v3

Inception modules

1x1 convolutions

Global average pooling

Auxiliary classifiers

Inception v3

Discussing ResNet and DenseNet architectures

ResNet

DenseNet

Understanding EfficientNets and the future of CNN architectures

References

Chapter 3: Combining CNNs and LSTMs

Building a neural network with CNNs and LSTMs

Text encoding demo

Building an image caption generator using PyTorch

Downloading the image captioning datasets

Preprocessing caption (text) data

Preprocessing image data

Defining the image captioning data loader

Defining the CNN-LSTM model

Training the CNN-LSTM model

Generating image captions using the trained model

Chapter 4: Deep Recurrent Model Architectures

Exploring the evolution of recurrent networks

Types of recurrent neural networks

RNNs

Bidirectional RNNs

LSTMs

Extended and bidirectional LSTMs

Multi-dimensional RNNs

Stacked LSTMs

GRUs

Grid LSTMs

Gated orthogonal recurrent units

Training RNNs for sentiment analysis.

Loading and preprocessing the text dataset

Instantiating and training the model

Building a bidirectional LSTM

Loading and preprocessing the text dataset

Instantiating and training the LSTM model

Discussing GRUs and attention-based models

GRUs and PyTorch

Attention-based models

Chapter 5: Advanced Hybrid Models

Building a transformer model for language modeling

Reviewing language modeling

Understanding the transformer model architecture

Defining a transformer model in PyTorch

Loading and processing the dataset

Training the transformer model

Developing a RandWireNN model from scratch

Understanding RandWireNNs

Developing RandWireNNs using PyTorch

Defining a training routine and loading data

Defining the randomly wired graph

Defining RandWireNN model modules

Transforming a random graph into a neural network

Training the RandWireNN model

Evaluating and visualizing the RandWireNN model

Chapter 6: Graph Neural Networks

Introduction to GNNs

Understanding the intuition behind GNNs

Using regular NNs on graph data - a thought experiment

Understanding the power of GNNs with computational graphs

Types of graph learning tasks

Understanding node-level tasks

Understanding edge-level tasks

Understanding graph-level tasks

Reviewing prominent GNN models

Understanding graph convolutions with GCNs

Using attention in graphs with GAT

Performing graph sampling with GraphSAGE

Building a GCN model using PyTorch Geometric

Loading and exploring the citation networks dataset

Building a simple NN-based node classifier

Building a GCN model for node classification

Training a GAT model with PyTorch Geometric

Chapter 7: Music and Text Generation with PyTorch.

Building a transformer-based text generator with PyTorch

Training the transformer-based language model

Saving and loading the language model

Using the language model to generate text

Using GPT models as text generators

Out-of-the-box text generation with GPT-2

Text generation strategies using PyTorch

Greedy search

Beam search

Top-k and top-p sampling

Text generation with GPT-3

Generating MIDI music with LSTMs using PyTorch

Loading the MIDI music data

Defining the LSTM model and training routine

Training and testing the music generation model

Chapter 8: Neural Style Transfer

Understanding how to transfer style between images

Implementing neural style transfer using PyTorch

Loading the content and style images

Loading and trimming the pretrained VGG19 model

Building the neural style transfer model

Training the style transfer model

Experimenting with the style transfer system

Chapter 9: Deep Convolutional GANs

Defining the generator and discriminator networks

Understanding the DCGAN generator and discriminator

Training a DCGAN using PyTorch

Defining the generator

Defining the discriminator

Loading the image dataset

Training loops for DCGANs

Using GANs for style transfer

Understanding the pix2pix architecture

Exploring the pix2pix generator

Exploring the pix2pix discriminator

Chapter 10: Image Generation Using Diffusion

Understanding image generation using diffusion

Understanding how diffusion works

Training a forward diffusion model

Performing reverse diffusion or denoising

Training a diffusion model for image generation

Loading the dataset using Hugging Face datasets

Processing the dataset using torchvision transforms.

Adding noise to images using diffusers

Defining the UNet model

Training the UNet model

Defining the optimizer and learning schedule

Using Hugging Face Accelerate to accelerate training

Running the model training loop

Generating realistic anime images using (reverse) diffusion

Understanding text-to-image generation using diffusion

Encoding text input into an embedding vector

Ingesting additional text data in the (conditional) UNet model

Using the Stable Diffusion model to generate images from text

Chapter 11: Deep Reinforcement Learning

Reviewing RL concepts

Types of RL algorithms

Model-based

Model-Free

Discussing Q-learning

Understanding deep Q-learning

Using two separate DNNs

Experience replay buffer

Building a DQN model in PyTorch

Initializing the main and target CNN models

Defining the experience replay buffer

Setting up the environment

Defining the CNN optimization function

Managing and running episodes

Training the DQN model to learn Pong

Chapter 12: Model Training Optimizations

Distributed training with PyTorch

Training the MNIST model in a regular fashion

Training the MNIST model in a distributed fashion

Distributed training on GPUs with CUDA

Automatic mixed precision training

Regular model training on a GPU

Mixed precision training on a GPU

Chapter 13: Operationalizing PyTorch Models into Production

Model serving in PyTorch

Creating a PyTorch model inference pipeline

Saving and loading a trained model

Building the inference pipeline

Building a basic model server

Writing a basic app using Flask

Using Flask to build our model server

Setting up model inference for Flask serving

Building a Flask app to serve model.

Using a Flask server to run predictions

Creating a model microservice

Serving a PyTorch model using TorchServe

Installing TorchServe

Launching and using a TorchServe server

Exporting universal PyTorch models using TorchScript and ONNX

Understanding the utility of TorchScript

Model tracing with TorchScript

Model scripting with TorchScript

Running a PyTorch model in C++

Using ONNX to export PyTorch models

Serving PyTorch models in the cloud

Using PyTorch with AWS

Serving a PyTorch model using an AWS instance

Using TorchServe with Amazon SageMaker

Serving PyTorch models on Google Cloud

Serving PyTorch models with Azure

Working with Azure's DSVMs

Discussing Azure Machine Learning Service

Chapter 14: PyTorch on Mobile Devices

Deploying a PyTorch model on Android

Converting the PyTorch model to a mobile-friendly format

Setting up the Android app development environment

Using the phone camera in the Android app to capture images

Enabling the camera during app startup

Handling camera permissions in Android

Opening the camera for image capture

Capturing images using the phone camera

Running ML model inference on camera-captured images

Validating the ML model binary path

Performing image classification on camera-captured images

Launching the app on an Android mobile device

Building PyTorch apps on iOS

Setting up the iOS development environment

Using a phone camera in the iOS app to capture images

Chapter 15: Rapid Prototyping with PyTorch

Using fastai to set up model training in a few minutes

Setting up fastai and loading data

Training an MNIST model using fastai

Evaluating and interpreting the model using fastai.

Training models on any hardware using PyTorch Lightning.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: Jha, Ashish Ranjan Mastering Pytorch

ISBN:

9781801079969

OCLC:

1438929463