This repository provides the official code for the O'Reilly book "Practical Deep Learning for Cloud, Mobile, and Edge." It targets software engineers, data scientists, and hobbyists, offering a hands-on guide to building practical deep learning applications across diverse platforms, from cloud servers to embedded devices. The benefit lies in translating deep learning concepts into real-world, deployable AI solutions.

How It Works

The project's approach is deeply practical, guiding users through building and deploying computer vision models using frameworks like Keras, TensorFlow, Core ML, and TensorFlow Lite. It emphasizes a hands-on methodology, covering the entire pipeline from idea to deployment on various hardware, including Raspberry Pi, Jetson Nano, and Google Coral. This practical, cross-platform focus aims to demystify the process of creating real-world AI applications.

Quick Start & Requirements

• Primary Install: Set up a Python virtual environment (e.g., virtualenv) and install dependencies using the provided requirements.txt file. Detailed instructions for virtualenv are available in the FAQ document.
• Prerequisites: Google Colab is frequently used, requiring access to Google Drive for data handling.
• Resources: Links to the book's online versions (Safari, Amazon, Google Books) and website are available for further context.

Highlighted Details

• Featured as an official learning resource on the Keras website.
• Covers deployment across cloud, mobile (iOS/Android), browser (TensorFlow.js), and edge devices (Raspberry Pi, Jetson Nano, Google Coral).
• Explores diverse AI projects, including image classification, reverse image search, object detection, and autonomous vehicle simulation.
• Includes practical tips for maximizing model accuracy and speed, debugging, and scaling, alongside insights from industry leaders like François Chollet and Jeremy Howard.

Maintenance & Community

The repository is associated with a published O'Reilly book authored by recognized AI experts Anirudh Koul, Siddha Ganju, and Meher Kasam, who have extensive industry experience and notable contributions to AI research and applications. No specific community channels (e.g., Discord, Slack) or active maintenance signals beyond the book's publication are detailed in the README.

Licensing & Compatibility

The README does not specify a software license for the code within this repository. As it is the official code for a published book, users should exercise caution regarding its use in commercial or closed-source projects without explicit clarification.

Limitations & Caveats

This repository serves as educational material accompanying a book, rather than a standalone, production-ready library. The code examples are tailored to specific book chapters and projects, and may require adaptation for different use cases or environments. No specific bugs or deprecation notices are mentioned.