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tf2_course provides the notebooks for the “Deep Learning with TensorFlow 2 and Keras” course authored by the same author, Aurélien Géron. It is structured as a teaching toolkit: you’ll find notebooks covering neural networks with Keras, lower-level TensorFlow APIs, data loading & preprocessing, convolutional and recurrent networks, and deployment/distribution of models. The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding of deep learning frameworks and practices. The repo supports experimentation: you can run code, tweak hyperparameters, and follow guided exercises that strengthen practical mastery. Rather than being book-based, it is course-based, meaning the flow, examples and structure lean toward interactive teaching and incremental builds. It’s well-suited for those who want a focused, deep-learning path rather than a broad ML textbook.
Features
- Jupyter notebooks covering deep learning topics (NNs, CNNs, RNNs) with TensorFlow 2/Keras
- Separate modules for data loading, preprocessing, and model deployment/distribution
- Exercises and solutions for active learning and experimentation
- Environment specification for reproducibility and ease of setup
- Focused teaching flow for deep learning (rather than full ML curriculum)
- Clear separation of conceptual versus implementation content
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