Deep Learning with JavaScript

Download free course Deep Learning with JavaScript, pdf file on 560 pages by Shanqing Cai, Stanley Bileschi, Eric D. Nielsen, Francois Chollet.

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.

In Deep Learning with JavaScript, you'll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you'll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.

Table of contents

  • Deep learning and JavaScript
  • Getting started: Simple linear regression in TensorFlow.js
  • Adding nonlinearity: Beyond weighted sums
  • Recognizing images and sounds using convnets
  • Transfer learning: Reusing pretrained neural networks
  • Working with data
  • Visualizing data and models
  • Underfitting, overfitting, and the universal workflow of machine learning
  • Deep learning for sequences and text
  • Generative deep learning
  • Basics of deep reinforcement learning
  • Testing, optimizing, and deploying models
  • Summary, conclusions, and beyond
  • Installing tfjs-node-gpu and its dependencies
  • A quick tutorial of tensors and operations in TensorFlow.js
Pages : 560
Size :
File type : PDF
Downloads: 167
Created: 2022-02-02
License: All rights reserved
Author(s): Shanqing Cai, Stanley Bileschi, Eric D. Nielsen, Francois Chollet
Deep Learning with JavaScript

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