Practical Deep Learning for Cloud and Mobile

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition

Description:
Whether you’re a software engineer aspiring to break into deep learning, a seasoned data scientist, or simply a hobbyist with dreams of creating the next viral AI app, Practical Deep Learning for Cloud, Mobile, and Edge by Anirudh Koul, Siddha Ganju, and Meher Kasam is the perfect guide for you. This comprehensive book takes you through the practical steps needed to build real-world AI applications for various platforms, including the cloud, mobile devices, browsers, and edge devices. With decades of combined industry experience, the authors expertly bridge the gap between deep learning research and building scalable, user-friendly applications.

With projects ranging from computer vision models to creating apps like “Not Hotdog,” and building miniature self-driving cars, the book provides hands-on learning and 50+ practical tips for maximizing model accuracy and speed. Whether you’re working with Keras, TensorFlow, Core ML, or TensorFlow Lite, this guide gives you all the tools to bring your AI ideas to life.

Key Features:

• Train and Deploy Models: Use Keras, TensorFlow, and TensorFlow Lite to build models for cloud, mobile, and edge devices.
• Embedded AI for Makers: Explore projects like developing AI applications on Raspberry Pi, Jetson Nano, and Google Coral.
• AI in Mobile & Browser Apps: Build real-time object classification apps for iOS and Android using Core ML and TensorFlow Lite.
• Fun, Real-World Projects: From the viral “Not Hotdog” app to creating a mini autonomous car, get hands-on experience in real-world AI.
• Optimize Model Performance: Discover 50+ tips for debugging, optimizing, and scaling models to millions of users.

Table of Contents (Short Version)

1. Exploring AI: Introduction to deep learning and AI applications.
2. Image Classification with Keras: Basics of building models using Keras.
3. Transfer Learning: Build AI models in just 30 lines of code.
4. Reverse Image Search Engine: Learn about embeddings and image search.
5. CNN Accuracy: Maximize the accuracy of your Convolutional Neural Networks.
6. Optimizing TensorFlow: A checklist for speeding up TensorFlow.
7. Cloud APIs for Vision: Use cloud APIs for computer vision projects in just 15 minutes.
8. AI in Browsers and Mobile: Learn TensorFlow.js for browsers, and Core ML for iOS.
9. Building the Not Hotdog App: Create fun AI apps like Not Hotdog and Shazam for Food.
10. Edge AI for Makers: Use AI on edge devices like Raspberry Pi and Google Coral.
11. Self-Driving Car Simulation: Build and simulate autonomous cars using deep learning.

What Is Practical Deep Learning for Cloud, Mobile, and Edge?

“Practical Deep Learning for Cloud, Mobile, and Edge” refers to hands-on techniques for developing AI and deep learning models for deployment across various platforms such as cloud, mobile devices, and edge devices. By focusing on real-world applications, developers can ensure their AI solutions are scalable, efficient, and user-friendly. This book demonstrates how to apply deep learning frameworks like TensorFlow and Keras to practical projects, from creating computer vision models to building embedded AI systems for the Internet of Things (IoT).

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Product Details:

• Publisher: O’Reilly Media; 1st edition (October 14, 2019)
• ISBN-13: 9781492034865
• Language: English
• File Size: 101170 KB
• Print Length: 901 pages
• Format Type: PDF