Practical deep learning for cloud, mobile, and edge: real-world ai & computer-vision...
Описание
** Featured as a learning resource on the official Keras website **
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. If your goal is to build something creative, useful, scalable, or
just plain cool, this book is for you.
Relying on decades of combined 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 François Chollet (
Keras
,
), 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
).
Також купити книгу Practical Deep Learning for Cloud, Mobile, and Edge: Real-
World AI & Computer-Vision Projects Using Python, Keras & TensorFlow, Anirudh
Koul, Siddha Ganju, Meher Kasam, more Ви можете по посиланню
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