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ageron/handson-ml2 ★ 29954 13158 DATA_RESEARCH

This repository is the companion code for the second edition of Aurélien Géron's 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'. It consists of 19 Jupyter notebooks that teach a wide range of machine learning and deep learning topics, including classification, regression, neural networks, CNNs, RNNs, NLP, reinforcement learning, and scalable deployment. The code is well-structured and annotated, designed for interactive learning. While not a production application, the notebooks contain several high-quality, reusable components such as custom Keras layers, training loops, and learning rate schedulers. The repository also includes Docker configurations and environment files to ensure reproducibility. However, the second edition is explicitly outdated, with libraries pinned to versions from 2021 (TensorFlow 2.6, Scikit-Learn 1.0), and the README directs users to the third edition and a PyTorch version. The data sources are standard benchmark datasets bundled as static files or downloaded from URLs that may change.

RATING: 7.6/10 REPRO: 7.0/10 MAINT: stale DRIFT: medium
#ml-education #tensorflow #scikit-learn #jupyter-notebooks #deep-learning #reinforcement-learning #nlp #keras
▸ 5 reusable components found
ageron/handson-ml ★ 25597 12731 DATA_RESEARCH

This repository is the companion code for the first edition of 'Hands-on Machine Learning with Scikit-Learn and TensorFlow' (2017). It contains a collection of Jupyter notebooks teaching fundamental and advanced machine learning concepts, covering classification, SVMs, decision trees, ensemble methods, dimensionality reduction, and deep learning with TensorFlow 1.x (including CNNs, RNNs, autoencoders, and reinforcement learning). The code is educational, with step-by-step implementations, inline explanations, and exercise solutions. It is not a production application but a self-contained learning environment, supplemented with datasets (California housing, OECD life satisfaction, MNIST) and a Docker setup for reproducible execution. Significant technical drift exists: TensorFlow 1.x APIs, deprecated scikit-learn imports, and Python 2/3 compatibility shims make the notebooks incompatible with modern environments without substantial rewriting. The README explicitly directs users to the second edition repository for up-to-date code.

RATING: 6.2/10 REPRO: 7.0/10 MAINT: stale DRIFT: high
#machine-learning #tensorflow1 #scikit-learn #jupyter-notebooks #deep-learning #ml-education #data-science #tensorflow #python #neural-network #ml #distributed #jupyter-notebook #deprecated
▸ 3 reusable components found
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