[人工智能]人工智能书籍集锦

本博客总结人工智能中常用的书籍，旨在帮助了解和研究更加强大的AI技术。主要总结以下几个主要领域的书籍：

1. AI Books
2. Reinforcement Learning
3. Machine Learning
4. Deep Learning
5. Neural Network
6. Natural Language Processing
7. Data Science
8. Computer Vision
9. Robotics

AI Books

Python Artificial Intelligence Projects for Beginners (2018)

Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.

人工智能 (AI) 是各种企业、行业和部门采用的最新技术。 面向初学者的 Python 人工智能项目演示了 Python 中的 AI 项目，涵盖了构成人工智能世界的现代技术。

Artificial Intelligence for Big Data (2018)

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.

在这个大数据时代，公司拥有比以往任何时候都多的消费者数据，远远超过当前技术所希望跟上的。 然而，人工智能通过超越人类的限制来分析数据来缩小差距。

Hands-On Intelligent Agents with OpenAI Gym (2018)

Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.

许多现实世界的问题可以分解为需要做出一系列决定或采取行动的任务。 在没有机器编程的情况下解决此类任务的能力需要机器具有人工智能并且能够学习适应。 本书是一本易于理解的指南，用于实现机器软件代理的学习算法，以解决离散或连续的顺序决策和控制任务。

Reinforcement Learning

Hands-On Reinforcement Learning with Python (2018)

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

强化学习（RL）是人工智能的趋势和最有前途的分支。 使用 Python 进行动手强化学习不仅可以帮助您掌握基本的强化学习算法，还可以帮助您掌握先进的深度强化学习算法。

Deep Reinforcement Learning Hands-On (2018)

Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.

强化学习 (RL) 的最新发展与深度学习 (DL) 相结合，在训练智能体以类似人类的方式解决复杂问题方面取得了前所未有的进展。 谷歌使用算法来玩和击败著名的 Atari 街机游戏，推动了该领域的突出，研究人员正在快速产生新的想法。

Machine Learning

Scala Machine Learning Projects (2018)

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development.

机器学习通过将数据转化为可操作的信息，对学术界和工业界产生了巨大影响。 在过去几年中，Scala 的采用率稳步上升，尤其是在数据科学和分析领域。 本书面向具有复杂数值计算背景并希望了解更多动手机器学习应用程序开发的数据科学家、数据工程师和深度学习爱好者。

Machine Learning with Swift (2018)

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language.

机器学习作为一个领域有望通过帮助我们有效地学习和分析信息并发现人类无法发现的某些模式来为软件带来更高的智能。 当您使用流行的 Swift 语言踏上机器学习的激动人心的旅程时，本书将成为您的指南。

Deep Learning

Hands-On Deep Learning for Images with TensorFlow (2018)

Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.

使用 TensorFlow 进行图像深度学习的动手实践向您展示了实际项目的实际实现，教您如何利用 TensorFlow 的功能，利用深度学习的力量执行高效的图像处理。 在本书的帮助下，您将掌握执行深度学习的不同范例，例如深度神经网络和卷积神经网络，然后了解如何使用 TensorFlow 实现它们。

Apache Spark Deep Learning Cookbook (2018)

With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed.

随着深度学习在现代行业中迅速获得主流采用，组织正在寻找将流行的大数据工具与高效的深度学习库结合起来的方法。 因此，这将有助于深度学习模型以更高的效率和速度进行训练。

Neural Network

Neural Network Programming with Tensorflow (2017)

If you’re aware of the buzz surrounding the terms such as “machine learning,” “artificial intelligence,” or “deep learning,” you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.

如果您知道围绕“机器学习”、“人工智能”或“深度学习”等术语的嗡嗡声，您可能知道什么是神经网络。 有没有想过它们如何帮助有效地解决复杂的计算问题，或者如何训练高效的神经网络？ 这本书将教你这一点。

Practical Convolutional Neural Networks (2018)

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.

卷积神经网络 (CNN) 正在彻底改变多个应用领域，例如视觉识别系统、自动驾驶汽车、医学发现、创新电子商务等。您将学习围绕图像和视频分析创建创新解决方案，以解决复杂的机器学习和计算机视觉 相关问题并实现现实生活中的 CNN 模型。

Natural Language Processing

Hands-On Natural Language Processing with Python (2018)

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.

自然语言处理 (NLP) 已在各个领域得到应用，例如网络搜索、广告和客户服务，借助深度学习，我们可以提高其在这些领域的表现。 使用 Python 进行自然语言处理的动手实践教您如何利用深度学习模型来执行各种 NLP 任务，以及应对当今 NLP 挑战的最佳实践。

Natural Language Processing with TensorFlow (2018)

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.

自然语言处理 (NLP) 为深度学习应用程序提供了大部分可用数据，而 TensorFlow 是目前可用的最重要的深度学习框架。 使用 TensorFlow 进行自然语言处理将 TensorFlow 和 NLP 结合在一起，为您提供宝贵的工具来处理当今数据流中的大量非结构化数据，并将这些工具应用于特定的 NLP 任务。

Data Science

Thoughtful Data Science (2018)

Thoughtful Data Science brings new strategies and a carefully crafted programmer’s toolset to work with modern, cutting-edge data analysis. This new approach is designed specifically to give developers more efficiency and power to create cutting-edge data analysis and artificial intelligence insights.

周到的数据科学带来了新的策略和精心设计的程序员工具集，可用于现代、尖端的数据分析。 这种新方法专门设计用于为开发人员提供更高的效率和能力，以创建尖端的数据分析和人工智能洞察力。

Computer Vision

Hands-On Algorithms for Computer Vision (2018)

An arena that has been positively impacted by the advancements in processing power and performance is the field of Computer Vision. It’s only natural that over time, more and more algorithms are introduced to perform Computer Vision tasks more efficiently. Hands-On Algorithms for Computer Vision is a starting point for anyone who is interested in the field of Computer Vision and wants to explore the most practical algorithms used by professional Computer Vision developers. The book starts with the basics and builds up over the course of the chapters with hands-on examples for each algorithm.

受到处理能力和性能进步的积极影响的领域是计算机视觉领域。 随着时间的推移，自然会引入越来越多的算法来更有效地执行计算机视觉任务。 计算机视觉的动手算法是任何对计算机视觉领域感兴趣并希望探索专业计算机视觉开发人员使用的最实用算法的人的起点。 本书从基础开始，并在各章的课程中为每种算法提供动手示例。

Robotics

Learning Robotics using Python, 2nd Edition (2018)

Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch.

机器人操作系统 (ROS) 是研究和工业中最流行的机器人软件框架之一。 它具有用于在机器人中实现不同功能的各种功能，而无需从头开始实现它们。

参考资源

[1] aibooks.github.io