Practical-Deep-Learning-Book

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Practical-Deep-Learning-Book is an open-source code repository for the O'Reilly book, "Practical Deep Learning for Cloud, Mobile & Edge." It provides practical examples and implementations of deep learning techniques for various platforms.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Practical-Deep-Learning-Book?

Practical-Deep-Learning-Book is the official code repository for the O'Reilly Publication, "Practical Deep Learning for Cloud, Mobile & Edge." This resource offers a hands-on approach to building practical deep learning applications for cloud, mobile, browser, and edge devices. It guides users through converting ideas into real-world applications, covering topics like training, tuning, and deploying computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite. The repository includes code for developing AI for devices such as Raspberry Pi, Jetson Nano, and Google Coral, and explores projects from object detection to autonomous car simulation. It also provides practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.

Best used for

Ideal for professors and teachers who need to provide practical deep learning examples, guide students through real-world AI application development, and demonstrate model deployment on various platforms. Especially valuable for those teaching computer vision, edge AI, and scalable inference serving.

Common actions

learn deep learning

implement deep learning models

deploy AI applications

explore computer vision

"AI Agents"github copilotlow-code/no-codecollaborationopen-sourcedeepfakeface swappingautomated workflowworkflows

Capabilities

Key features

Computer vision model deployment
AI for edge devices
Transfer learning techniques
Model accuracy maximization
Scalable inference serving
Real-time object classification

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and frameworks are used in the code examples?

The book and its code repository primarily utilize Python with deep learning frameworks such as Keras, TensorFlow, Core ML, and TensorFlow Lite. It also covers tools like TensorFlow.js and ml5.js for browser-based AI, and Core ML for iOS development.

Can this resource help with deploying AI models to mobile and edge devices?

Yes, a significant portion of the book and code focuses on deploying AI to mobile and edge devices. It includes chapters on iOS with Core ML, Android with TensorFlow Lite and ML Kit, and embedded AI on Raspberry Pi, Jetson Nano, and Google Coral.

Are there any practical projects or case studies included?

The repository features numerous practical projects and over 40 industry case studies. Examples include building a reverse image search engine, simulating an autonomous car, creating a 'Not Hotdog' app, and developing a cat locator app.

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