- Paperback: 450 pages
- Publisher: WOW! eBook (February 12, 2021)
- Language: English
- ISBN-10: 1789614384
- ISBN-13: 978-1789614381
Mastering PyTorch: Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
The Mastering PyTorch book starts with a quick overview of PyTorch and explores using convolutional neural networks (CNNs) architectures for image classification. You’ll then work with recurrent neural networks (RNNs) architectures 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 and explore the world of generative adversarial networks (GANs). You’ll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you’ll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.
- Implement text and music generating models using PyTorch
- Build a deep Q-network (DQN) model in PyTorch
- Export universal PyTorch models using Open Neural Network Exchange (ONNX)
- Become well-versed with rapid prototyping using PyTorch with fast.ai
- Perform neural architecture search effectively using AutoML
- Easily interpret machine learning (ML) models written in PyTorch using Captum
- Design ResNets, LSTMs, Transformers, and more using PyTorch
- Find out how to use PyTorch for distributed training using the torch.distributed API
By the end of this PyTorch book, you’ll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.