Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

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商品描述

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications across computer vision and NLP
  • Learn how a computer can navigate in complex environments with reinforcement learning

Book Description

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.

This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.

By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.

What you will learn

  • Grasp the mathematical theory behind neural networks and deep learning processes
  • Investigate and resolve computer vision challenges using convolutional networks and capsule networks
  • Solve generative tasks using variational autoencoders and Generative Adversarial Networks
  • Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
  • Explore reinforcement learning and understand how agents behave in a complex environment
  • Get up to date with applications of deep learning in autonomous vehicles

Who this book is for

This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Table of Contents

  1. Machine Learning – An Introduction
  2. Neural Networks
  3. Deep Learning Fundamentals
  4. Computer Vision With Convolutional Networks
  5. Advanced Computer Vision
  6. Generating images with GANs and Variational Autoencoders
  7. Recurrent Neural Networks and Language Models
  8. Reinforcement Learning Theory
  9. Deep Reinforcement Learning for Games
  10. Deep Learning in Autonomous Vehicles

商品描述(中文翻譯)

學習使用流行的Python庫進行先進的深度學習技術及其應用

主要特點:
- 使用Python庫建立神經網絡和深度學習的堅實基礎
- 探索計算機視覺和自然語言處理等領域的先進深度學習技術及其應用
- 學習計算機如何通過強化學習在複雜環境中導航

書籍描述:
隨著人工智能在滿足商業和消費需求的應用中的激增,深度學習對於滿足當前和未來市場需求變得比以往更加重要。通過本書,您將探索深度學習,並學習如何在項目中應用機器學習。

《Python深度學習》第二版將使您熟悉深度學習、深度神經網絡以及如何使用高性能算法和流行的Python框架對其進行訓練。您將了解不同的神經網絡架構,如卷積網絡、循環神經網絡、長短期記憶(LSTM)網絡和膠囊網絡。您還將學習如何解決計算機視覺、自然語言處理(NLP)和語音識別等領域的問題。您將研究生成模型方法,如變分自編碼器和生成對抗網絡(GAN),以生成圖像。當您深入研究強化學習的新興領域時,您將了解到是什麼構成了流行遊戲Go、Atari和Dota的主要組成部分的最新算法。

通過閱讀本書,您將對深度學習的理論及其實際應用有深入了解。

您將學到什麼:
- 掌握神經網絡和深度學習過程背後的數學理論
- 使用卷積網絡和膠囊網絡解決計算機視覺挑戰
- 使用變分自編碼器和生成對抗網絡解決生成任務
- 使用循環網絡(LSTM和GRU)和注意力模型實現複雜的NLP任務
- 探索強化學習,了解代理在複雜環境中的行為
- 瞭解深度學習在自動駕駛汽車中的應用

本書適合對深度學習感興趣的數據科學從業者、機器學習工程師以及具備機器學習基礎和一些Python編程經驗的讀者。具備數學背景和對微積分和統計學的概念理解將有助於您從本書中獲得最大的收益。

目錄:
1. 機器學習 - 簡介
2. 神經網絡
3. 深度學習基礎
4. 使用卷積網絡的計算機視覺
5. 高級計算機視覺
6. 使用GAN和變分自編碼器生成圖像
7. 循環神經網絡和語言模型
8. 強化學習理論
9. 遊戲中的深度強化學習
10. 自動駕駛汽車中的深度學習