面向程序员的AI与机器学习指南(影印版)(英文版)

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。

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 4.Using Public Datasets with TensorFIow Datasets Getting Started with TFDS Using TFDS with Keras Models Loading Specific Versions Using Mapping Functions for Augmentation Using TensorFlow Addons Using Custom Splits Understanding TFRecord The ETL Process for Managing Data in TensorFlow Optimizing the Load Phase Parallelizing ETL to Improve Training Performance Summary 5.Introduction to Natural Language Processing Encoding Language into Numbers Getting Started with T0kenization Turning Sentences into Sequences Removing Stopwords and Cleaning Text Working with Real Data Sources …… Part Ⅱ Using Models Index