如果你想从程序员转型为人工智能专家，这里是一个理想的起点。基于LaurenceMoroney极其成功的人工智能课程，这本入门书提供了一种面向实践、代码优先的方法，帮助你在学习关键主题的同时建立信心。你所需要的只是Python的使用经验，了解其处理数据和数组的写法。
　　你将学习如何实现机器学习中非常常见的场景，包括计算机视觉、自然语言处理（NLP）以及用于Web、移动、云端、嵌入式运行时的序列建模。大多数与机器学习相关的书开篇就是令人生畏的高级数学知识。这本指南提供了实用的课程，你可以直接同代码打交道。
　　通过使用代码示例了解机器学习的基础知识
　　使用TensorFlow为各种场景建模
　　使用仅包含一个神经元的神经网络建模
　　实现包括图像特征检测在内的计算机视觉
　　使用NLP标记和序列化单词与句子
　　将你的模型嵌入安卓和iOS设备
　　通过TensorFlowServing在Web和云端提供模型

　　Laurence Moroney，在Google负责AI倡导工作，教授软件开发人员如何通过机器学习构建AI系统。他是TensorFlowYouTube频道的常客，公认的全球主题演讲者，也是一位多产的作家。

　　★“本书出色地介绍了如何使用TensorFlow来理解和实践机器学习与人工智能建模。”
　　——Jialin Huang，博士
　　
　　★数据与应用科学家，微软公司“Laurence Moroney一直是将TensorFlow打造为全球领先的AI框架的主力。我很荣幸通过deeplearning.ai和Coursera协助他教授TensorFlow。希望你在学习TensorFlow的过程中一切顺利。有Laurence这位老师，你将展开一场伟大的冒险。”
　　——Andrew Ng，deeplearning.ai创始人

Foreword
Preface

Part I Building Models
1． Introduction to TensorFlow
What Is Machine Learning?
Limitations of Traditional Programming
From Programming to Learning
What Is TensorFlow?
Using TensorFlow
Installing TensorFlow in Python
Using TensorFlow in PyCharm
Using TensorFlow in Google Colab
Getting Started with Machine Learning
Seeing What the Network Learned
Summary
2． Introduction to Computer Vision
Recognizing Clothing Items
The Data： Fashion MNIST
Neurons for Vision
Designing the Neural Network
The Complete Code
Training the Neural Network
Exploring the Model Output
Training for Longer-Discovering Overfitting
Stopping Training
Summary
3． Going Beyond the Basics： Detecting Features in Images
Convolutions
Pooling
Implementing Convolutional Neural Networks
Exploring the Convolutional Network
Building a CNN to Distinguish Between Horses and Humans
The Horses or Humans Dataset
The Keras Image Data Generator
CNN Architecture for Horses or Humans
Adding Validation to the Horses or Humans Dataset
Testing Horse or Human Images
Image Augmentation
Transfer Learning
Multiclass Classification
Dropout Regularization
Summary
……

Part II Using Models

Index

　　Dear Reader，
　　AI is poised to transform every industry， but almost every AI application needs to be customized for its particular use. A system for reading medical records is different from one for finding defects in a factory， which is different from a product recommendation engine. For AI to reach its full potential， engineers need tools that can help them adapt the amazing capabilities available to the nullions of concrete problems we wish to solve.
　　When I led the Google Brain team， we started to build the C++ precursor to TensorFlow called DistBelief. We were excited about the potential of harnessing thousands of CPUs to train a neural network （for instance， using 16，000 CPUs to train a cat detector on unlabeled YouTube videos）. How far deep learning has come since then！ What was once cutting-edge can now be done for around $3，000 of cloud computing credits， and Google routinely trains neural networks using TPUs and GPUs at a scale that was unimaginable just years ago.
　　TensorFlow， too， has come a long way. It is far more usable than what we had in the early days， and has rich features ranging from modeling， to using pretrained models， to deploying on low-compute edge devices. It is today empowering hundreds of thousands of developers to build their own deep learning models.
　　Laurence Moroney， as Google's lead AI Advocate， has been a major force in building TensorFlow into one of the world's leading AI frameworks. I was privileged to support his teaching TensorFlow with deeplearning.ai and Coursera. These courses have reached over 80，000 learners and received numerous glowing reviews.
　　One unexpected aspect of friendship with Laurence is that he is also a free source of Irish poetry.