Is Practical-Deep-Learning-Book open source?

Yes — PracticalDL/Practical-Deep-Learning-Book is open source, released under the MIT license.

What language is Practical-Deep-Learning-Book written in?

PracticalDL/Practical-Deep-Learning-Book is primarily written in Jupyter Notebook.

How popular is Practical-Deep-Learning-Book?

PracticalDL/Practical-Deep-Learning-Book has 807 stars on GitHub.

Where can I find Practical-Deep-Learning-Book?

PracticalDL/Practical-Deep-Learning-Book is on GitHub at https://github.com/PracticalDL/Practical-Deep-Learning-Book.

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PracticalDL/Practical-Deep-Learning-Book

The Missing Link Between Training and Shipping

This is the code companion for the O'Reilly book that picks up where most tutorials end: getting trained models onto mobile devices, browsers, and edge hardware.

★807 stars Jupyter Notebook Learning

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What it does This is the official code repository for the O’Reilly book Practical Deep Learning for Cloud, Mobile, and Edge. It provides chapter-by-chapter notebooks and scripts that walk through training computer vision models with Keras and TensorFlow, then pushing them out to iOS via Core ML, Android via TensorFlow Lite, browsers via TensorFlow.js, and edge hardware like Raspberry Pi and Jetson Nano. It treats the full model lifecycle—from training to deployment—as a single continuous workflow.

The interesting bit Most educational repos abandon you at the training checkpoint. This one treats deployment and optimization as core curriculum: you’ll find chapters on TensorFlow Serving, KubeFlow scaling, benchmarking iPhone inference speed, and even A/B testing mobile models. The authors also roped in guest commentary from Keras creator François Chollet, fast.ai co-founder Jeremy Howard, and TensorFlow mobile lead Pete Warden to explain the tooling.

Key highlights

Chapter-by-chapter code covering Keras, transfer learning, embeddings, and object detection
Deployment targets include Core ML, TensorFlow Lite, TensorFlow.js, and cloud APIs
Production topics: scalable serving with TensorFlow Serving and KubeFlow, model optimization, and hardware benchmarking
Featured on the official Keras learning resources page
Includes industry case studies and the obligatory “Not Hotdog” iOS app

Verdict Grab this if you’re a developer who knows how to train a model but freezes when someone asks you to put it on a phone or behind an API. Skip it if you’re looking for a reusable framework or the latest research code; this is a tutorial repo tied to a specific book.

Frequently asked

What is PracticalDL/Practical-Deep-Learning-Book?: This is the code companion for the O'Reilly book that picks up where most tutorials end: getting trained models onto mobile devices, browsers, and edge hardware.
Is Practical-Deep-Learning-Book open source?: Yes — PracticalDL/Practical-Deep-Learning-Book is open source, released under the MIT license.
What language is Practical-Deep-Learning-Book written in?: PracticalDL/Practical-Deep-Learning-Book is primarily written in Jupyter Notebook.
How popular is Practical-Deep-Learning-Book?: PracticalDL/Practical-Deep-Learning-Book has 807 stars on GitHub.
Where can I find Practical-Deep-Learning-Book?: PracticalDL/Practical-Deep-Learning-Book is on GitHub at https://github.com/PracticalDL/Practical-Deep-Learning-Book.