Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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Description
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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
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.
List of Chapters
Exploring the Landscape of Artificial Intelligence
What's in the Picture: Image Classification with Keras
Cats Versus Dogs: Transfer Learning in 30 Lines with Keras
Building a Reverse Image Search Engine: Understanding Embeddings
From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy
Maximizing Speed and Performance of TensorFlow: A Handy Checklist
Practical Tools, Tips, and Tricks
Cloud APIs for Computer Vision: Up and Running in 15 Minutes
Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
AI in the Browser with TensorFlow.js and ml5.js
Real-Time Object Classification on iOS with Core ML
Not Hotdog on iOS with Core ML and Create ML
Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit
Building the Purrfect Cat Locator App with TensorFlow Object Detection API
Becoming a Maker: Exploring Embedded AI at the Edge
Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras
Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer
Guest-contributed Content
The book features chapters from the following industry experts:
Sunil Mallya (Amazon AWS DeepRacer)
Aditya Sharma and Mitchell Spryn (Microsoft Autonomous Driving Cookbook)
Sam Sterckval (Edgise)
Zaid Alyafeai (TensorFlow.js)
The book also features content contributed by several industry veterans including FranÃ§ois Chollet (Keras, Google), Jeremy Howard (Fast.ai), Pete Warden (TensorFlow Mobile), Anima Anandkumar (NVIDIA), Chris Anderson (3D Robotics), Shanqing Cai (TensorFlow.js), Daniel Smilkov (TensorFlow.js), Cristobal Valenzuela (ml5.js), Daniel Shiffman (ml5.js), Hart Woolery (CV 2020), Dan Abdinoor (Fritz), Chitoku Yato (NVIDIA Jetson Nano), John Welsh (NVIDIA Jetson Nano), and Danny Atsmon (Cognata).
Specifications
Country USA
Brand O'Reilly Media
Manufacturer O'Reilly Media
Binding Paperback
ReleaseDate 2019-11-05
UnitCount 1
Format Illustrated
EANs 9781492034865

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