Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features
PDF, ePUB
  • eBook:
    Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features
  • Author:
    Ashish Ranjan Jha, Dr. Gopinath Pillai
  • Edition:
    1 edition
  • Categories:
  • Data:
    February 12, 2021
  • ISBN:
    1789614384
  • ISBN-13:
    9781789614381
  • Language:
    English
  • Pages:
    450 pages
  • Format:
    PDF, ePUB

Book Description
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples

Key Features

  • Understand how to use PyTorch 1.x to build advanced neural network models
  • Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques
  • Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more

Book Description

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 book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) 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.
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.

What you will learn

  • 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

Who this book is for

This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

Content

Section 1: PyTorch Overview

Chapter 1: Overview of Deep Learning using PyTorch
Chapter 2: Combining CNNs and LSTMs

Section 2: Working with Advanced Neural Network Architectures
Chapter 3: Deep CNN Architectures
Chapter 4: Deep Recurrent Model Architectures
Chapter 5: Hybrid Advanced Models

Section 3: Generative Models and Deep Reinforcement Learning
Chapter 6: Music and Text Generation with PyTorch
Chapter 7: Neural Style Transfer
Chapter 8: Deep Convolutional GANs
Chapter 9: Deep Reinforcement Learning

Section 4: PyTorch in Production Systems
Chapter 10: Operationalizing PyTorch Models into Production
Chapter 11: Distributed Training
Chapter 12: PyTorch and AutoML
Chapter 13: PyTorch and Explainable AI
Chapter 14: Rapid Prototyping with PyTorch

Download Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features PDF or ePUB format free


Free sample

Download in .PDF format



Download in .ePUB format


Add comments
Прокомментировать
Введите код с картинки:*
Кликните на изображение чтобы обновить код, если он неразборчив
Copyright © 2019