donnemartin/data-science-ipython-notebooks

Organized as interactive Jupyter notebooks with progressive complexity—from foundational concepts (linear regression, basic operations) to advanced architectures (LSTMs, AlexNet, multi-GPU training)—enabling hands-on learning across the full data science stack. Covers practical implementation patterns including data curation workflows, regularization techniques, and GPU-accelerated computation, bridging theory and production-ready code. Serves as a comprehensive reference implementation guide integrated with curated external tutorial repositories for deeper exploration of specific frameworks.

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