Screen LibraryPractical deep learning for cloud, mobile, and edge : real-world AI and computer-vision projects using Python, Keras, and 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 ap...
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| Main Authors: | |
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| Group Author: | ; |
| Published: |
O'Reilly Media, Inc.,
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| Publisher Address: | Sebastopol, CA : |
| Publication Dates: |
2019. ©2020 |
| Literature type: | Book |
| Language: | English |
| Edition: | First edition. |
| Subjects: | |
| 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."--Page 4 of cover. |
| Item Description: | Includes index. |
| Carrier Form: | xxvi, 588 pages : illustrations, forms ; 24 cm |
| ISBN: |
9781492034865 (paperback) : 149203486X (paperback) |
| Index Number: | QA76 |
| CLC: | TP18 |
| Call Number: | TP18/K881-1 |
| Contents: | 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 accurcy -- 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 mI5.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 |