Description based upon print version of record

Intro -- Copyright -- Table of Contents -- Foreword -- Preface -- Who Should Read This Book -- Why I Wrote This Book -- Navigating This Book -- Technology You Need to Understand -- Online Resources -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Part I. Building Models -- Chapter 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 -- Chapter 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 -- Chapter 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 ImageDataGenerator -- 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 -- Chapter 4. Using Public Datasets with TensorFlow 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 -- Chapter 5. Introduction to Natural Language Processing -- Encoding Language into Numbers -- Getting Started with Tokenization -- Turning Sentences into Sequences -- Removing Stopwords and Cleaning Text -- Working with Real Data Sources -- Getting Text from TensorFlow Datasets -- Getting Text from CSV Files -- Getting Text from JSON Files

Available to OhioLINK libraries

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving