Inside Model.fit(): Dissecting gradient descent from example 1 50 The intuitions behind gradient-descent optimization 50 Backpropagation: Inside gradient descent 56
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35 Getting started: Simple linear regression in TensorFlow.js 2.1Įxample 1: Predicting the duration of a download using TensorFlow.js 38 Project overview: Duration prediction 38 A note on code listings and console interactions 39 Creating and formatting the data 40 Defining a simple model 43 Fitting the model to the training data 46 Using our trained model to make predictions 48 Summary of our first example 49 ■ Why combine JavaScript and machine learning? Deep learning with Node.jsĪ brief history of TensorFlow, Keras, and TensorFlow.js 27 Why TensorFlow.js: A brief comparison with similar libraries 31 How is TensorFlow.js being used by the world? 31 What this book will and will not teach you about TensorFlow.js 32 ■Ī GENTLE INTRODUCTION TO TENSORFLOW.JS.
1 Deep learning and JavaScript 1.1Īrtificial intelligence, machine learning, neural networks, and deep learning 6 Artificial intelligence 6 Machine learning: How it differs from traditional programming 7 Neural networks and deep learning 12 Why deep learning? Why now? 16 ■ Testing, optimizing, and deploying models Summary, conclusions, and beyond 453Ĭontents foreword xiii preface xv acknowledgments xvii about this book xix about the authors xxii about the cover illustration Working with data 201 Visualizing data and models 246 Underfitting, overfitting, and the universal workflow of machine learning 273 Deep learning for sequences and text 292 Generative deep learning 334 Basics of deep reinforcement learning 371 PART 3 ADVANCED DEEP LEARNING WITH TENSORFLOW.JS. Getting started: Simple linear regression in TensorFlow.js 37 Adding nonlinearity: Beyond weighted sums 79 Recognizing images and sounds using convnets 117 Transfer learning: Reusing pretrained neural networks 152 PART 2 A GENTLE INTRODUCTION TO TENSORFLOW.JS. Jenny Stout Marc-Phillipe Huget Ivan Martinovicˇ Lori Weidert Rebecca Deuel-Gallegos Jason Everett Karsten Strøbæck Dottie Marsico Marija Tudorīrief contents PART 1 MOTIVATION AND BASIC CONCEPTS. ISBN 9781617296178 Printed in the United States of America 20 Baldwin Road PO Box 761 Shelter Island, NY 11964ĭevelopment editor: Technical development editor: Review editor: Project editor: Copy editor: Proofreader: Technical proofreader: Typesetter: Cover designer: Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964 Email: For more information, please contact Special Sales Department Manning Publications Co. NIELSEN WITH FRANÇOIS CHOLLET FOREWORD BY NIKHIL THORAT DANIEL SMILKOVįor online information and ordering of this and other Manning books, please visit The publisher offers discounts on this book when ordered in quantity. Reinforcement learning (training an agent to interact with the environment)ĭeep Learning with JavaScript NEURAL NETWORKS IN TENSORFLOW.JS SHANQING CAI STANLEY BILESCHI ERIC D. Generative learning (generating new examples based on training data) Transfer learning (applying a pretrained model to new data)
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Multi-class classification (deciding among multiple classes)ĬategoricalCrossentropy Accuracy, confusion matrixĪ mix of the above (for example, numbers plus classes) 3.1, 3.2, 9.2 Accuracy, precision, recall, sensitivity, TPR, FPR, ROC, AUC Model building 2: Choosing last-layer activation, loss, and metric functions Task type (What are you predicting?)īinary classification (making a binary decision)
Images or data that can be represented as images (e.g., audio, game board) Numerical data (without sequential order) Model building 1: Choosing key layer types based on your data Input data type MANNING Working with data Ingest data Sect. Nielsen François Chollet Foreword by Nikhil Thorat and Daniel Smilkov
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