deep-learning-with-python-notebooks provides interactive Jupyter notebooks containing the runnable code samples from the book Deep Learning with Python, covering fundamental to advanced deep learning topics using Keras 3.
Project overview
The notebooks are designed for hands-on learning and can run on Google Colab's free tier GPU runtime, making deep learning examples accessible to readers without local hardware investment.
Project type
Infrastructure · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Readers of Deep Learning with Python who want runnable companion code to practice concepts from fundamentals through advanced topics
Key capabilities
Provides interactive Jupyter notebooks containing the runnable code samples from the book.
Limitations and risks
Notebooks only contain runnable code blocks and section titles, omitting text paragraphs, figures, and pseudocode.
Getting started
Open a notebook link in Colab, which provides a hosted runtime with all dependencies needed.
Evidence and sources
README: This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python, third edition (2025)
README: We recommend running these notebooks on Colab, which provides a hosted runtime with all the dependencies you will need. You can also, run these notebooks locally, either by settin…
README: The code for third edition is written using Keras 3. As such, it can be run with JAX, TensorFlow or PyTorch as a backend.
README: This book uses datasets and model weights provided by Kaggle, an online Machine Learning community and platform. You will need to create a Kaggle login to run Kaggle code in this…