Deep Learning with Python Notebooks is a collection of Jupyter notebooks that implements the code samples from the book Deep Learning with Python.
The notebooks serve as executable companions to the book, containing only the runnable code blocks and section titles while omitting explanatory text, figures, and pseudocode. The README explicitly recommends reading the notebooks alongside the book itself for full understanding. The code is written using Keras 3, which allows it to run with JAX, TensorFlow, or PyTorch as interchangeable backends. Users can select their preferred backend at the start of each notebook session. The notebooks are designed to run on Google Colab's free tier GPU runtime, though later chapters benefit from faster hardware available through paid subscriptions.
These notebooks suit developers learning deep learning through the book who want to experiment with and modify the code samples interactively. They work best for those comfortable with Jupyter environments and willing to reference the book text for context. The project covers material across multiple editions, with notebooks available for the third edition alongside legacy versions for earlier editions. Some chapters require datasets from Kaggle, which necessitates creating a Kaggle account and configuring authentication either per session or once through Colab secrets.
The project maintains notebooks across multiple book editions and supports three different deep learning backends, indicating ongoing maintenance to keep pace with evolving frameworks. The README provides detailed setup instructions for both cloud and local execution environments, including specific guidance for Kaggle integration and backend configuration.
