Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features
暫譯: 精通 PyTorch：利用進階 PyTorch 1.x 功能構建強大的神經網絡架構

Jha, Ashish Ranjan

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

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商品描述

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.

商品描述(中文翻譯)

**掌握使用 PyTorch 的深度學習進階技術和演算法，並透過實際案例進行學習**

**主要特點**

- 了解如何使用 PyTorch 1.x 建立進階神經網路模型
- 學習透過實作深度學習演算法和技術來執行各種任務
- 獲得在計算機視覺、自然語言處理 (NLP)、深度強化學習 (Deep RL)、可解釋的人工智慧 (Explainable AI) 等領域的專業知識

**書籍描述**

深度學習正在推動人工智慧革命，而 PyTorch 使得任何人都能更輕鬆地建立深度學習應用程式。本書將幫助您揭示專家技術，以充分利用您的數據並建立複雜的神經網路模型。

本書首先快速概述 PyTorch，並探討使用卷積神經網路 (CNN) 架構進行圖像分類。接著，您將使用遞迴神經網路 (RNN) 架構和變壓器進行情感分析。隨著進度的推進，您將在音樂、文本和圖像生成等不同領域應用深度學習，使用生成模型並探索生成對抗網路 (GAN) 的世界。您不僅會在 PyTorch 中建立和訓練自己的深度強化學習模型，還會使用專家提示和技術將 PyTorch 模型部署到生產環境中。最後，您將掌握如何有效地以分散式方式訓練大型模型，使用 AutoML 有效搜尋神經架構，以及使用 PyTorch 和 fast.ai 快速原型設計模型。

在本書結束時，您將能夠使用 PyTorch 執行複雜的深度學習任務，建立智能人工智慧模型。

**您將學到的內容**

- 使用 PyTorch 實作文本和音樂生成模型
- 在 PyTorch 中建立深度 Q 網路 (DQN) 模型
- 使用開放神經網路交換 (ONNX) 匯出通用的 PyTorch 模型
- 熟悉使用 PyTorch 和 fast.ai 進行快速原型設計
- 使用 AutoML 有效執行神經架構搜尋
- 輕鬆解釋用 PyTorch 編寫的機器學習 (ML) 模型，使用 Captum
- 使用 PyTorch 設計 ResNets、LSTMs、變壓器等
- 瞭解如何使用 torch.distributed API 進行分散式訓練

**本書適合誰**

本書適合數據科學家、機器學習研究人員和深度學習實踐者，尋求使用 PyTorch 1.x 實作進階深度學習範式。需要具備使用 Python 程式設計的深度學習工作知識。

作者簡介

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), his master's degree in computer science from EPFL (Switzerland), and an MBA degree from the Quantic School of Business (Washington). He received distinctions in all of his degrees. He has worked for a variety of tech companies, including Oracle and Sony, and tech start ups, such as Revolut, as a machine learning engineer.

Aside from his years of work experience, Ashish is a freelance ML consultant, an author, and a blogger (datashines). He has worked on products/projects ranging from using sensor data for predicting vehicle types to detecting fraud in insurance claims. In his spare time, Ashish works on open source ML projects and is active on StackOverflow and kaggle (arj7192).

作者簡介(中文翻譯)

Ashish Ranjan Jha 擁有印度羅爾基工程技術學院 (IIT Roorkee) 的電機工程學士學位、瑞士洛桑聯邦理工學院 (EPFL) 的計算機科學碩士學位，以及華盛頓的 Quantic 商學院的 MBA 學位。他在所有學位中均獲得優異成績。他曾在多家科技公司工作，包括 Oracle 和 Sony，以及科技新創公司如 Revolut，擔任機器學習工程師。

除了多年的工作經驗外，Ashish 還是一名自由職業的機器學習顧問、作者和部落客 (datashines)。他參與的產品/專案範圍從使用感測器數據預測車輛類型到檢測保險索賠中的詐騙。在空閒時間，Ashish 參與開源機器學習專案，並活躍於 StackOverflow 和 Kaggle (arj7192)。

目錄大綱

  1. Overview of Deep Learning Using PyTorch
  2. Combining CNNs and LSTMs
  3. Deep CNN Architectures
  4. Deep Recurrent Model Architectures
  5. Hybrid Advanced Models
  6. Music and Text Generation with PyTorch
  7. Neural Style Transfer
  8. Deep Convolutional GANs
  9. Deep Reinforcement Learning
  10. Operationalizing Pytorch Models into Production
  11. Distributed Training
  12. PyTorch and AutoML
  13. PyTorch and Explainable AI
  14. Rapid Prototyping with PyTorch

目錄大綱(中文翻譯)


  1. Overview of Deep Learning Using PyTorch

  2. Combining CNNs and LSTMs

  3. Deep CNN Architectures

  4. Deep Recurrent Model Architectures

  5. Hybrid Advanced Models

  6. Music and Text Generation with PyTorch

  7. Neural Style Transfer

  8. Deep Convolutional GANs

  9. Deep Reinforcement Learning

  10. Operationalizing Pytorch Models into Production

  11. Distributed Training

  12. PyTorch and AutoML

  13. PyTorch and Explainable AI

  14. Rapid Prototyping with PyTorch

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