Foreword by Nikhil Thorat and Daniel Smilkov.
About the technology
About the book
- Image and language processing in the browser
- Tuning ML models with client-side data
- Text and image creation with generative deep learning
- Source code samples to test and modify
About the reader
About the author
Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet.
PART 1 - MOTIVATION AND BASIC CONCEPTS
PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS
2 • Getting started: Simple linear regression in TensorFlow.js
3 • Adding nonlinearity: Beyond weighted sums
4 • Recognizing images and sounds using convnets
5 • Transfer learning: Reusing pretrained neural networks
PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS
6 • Working with data
7 • Visualizing data and models
8 • Underfitting, overfitting, and the universal workflow of machine learning
9 • Deep learning for sequences and text
10 • Generative deep learning
11 • Basics of deep reinforcement learning
PART 4 - SUMMARY AND CLOSING WORDS
12 • Testing, optimizing, and deploying models
13 • Summary, conclusions, and beyond
🗸 Author(s): Stanley Bileschi, Eric Nielsen & Shanqing Cai
🗸 Rating : from 5 stars ( reviews)
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, MacOS, PC and Amazon Kindle
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