Practical deep learning for cloud, mobile, and edge : real-world AI and computer-vision projects using Python, Keras, and TensorFlow / Anirudh Koul, Siddha Ganju, and Meher Kasam.

Format:

Book

Author/Creator:

Koul, Anirudh.

Contributor:

Ganju, Siddha.

Kasam, Meher.

Language:

English

Subjects (All):

TensorFlow.

Python (Computer program language).

Artificial intelligence.

Application software.

Machine learning.

Cloud computing.

Physical Description:

1 online resource (xxvi, 588 p.) : ill.

Edition:

First edition.

Place of Publication:

Beijing : O'Reilly, 2019.

Summary:

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite; Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral; Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies; Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning; Use transfer learning to train models in minutes; Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.

Contents:

1. Exploring the Landscape of Artificial Intelligence

2. What’s in the Picture: Image Classification with Keras

3. Cats Versus Dogs: Transfer Learning in 30 Lines with Keras

4. Building a Reverse Image Search Engine: Understanding Embeddings

5. From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy

6. Maximizing Speed and Performance of TensorFlow: A Handy Checklist

7. Practical Tools, Tips, and Tricks

8. Cloud APIs for Computer Vision: Up and Running in 15 Minutes

9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow

10. AI in the Browser with TensorFlow.js and ml5.js

11. Real-Time Object Classification on iOS with Core ML

12. Not Hotdog on iOS with Core ML and Create ML

13. Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit

14. Building the Purrfect Cat Locator App with TensorFlow Object Detection API

15. Becoming a Maker: Exploring Embedded AI at the Edge

16. Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras

17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer

A Crash Course in Convolutional Neural Networks

Index.

Notes:

Includes bibliographical references and index.

ISBN:

9781492034858

1492034851

9781492034834

1492034835

9781492034810

1492034819

OCLC:

1137802295