Phase 1 — Python & Math

April 18, 2026

Get the learning environment set up and the roadmap locked in

What I Did

Researched and finalized the Python & AI/ML learning roadmap
Decided on PyTorch over TensorFlow as the primary deep learning framework
Selected courses to use: Mathematics for ML, ML Specialization, and Deep Learning Specialization (all deeplearning.ai) — dropped the IBM courses
Picked books: Python Crash Course, Hands-On ML (Géron), Deep Learning (Goodfellow et al.), Mathematics for ML (Deisenroth et al. — free PDF)
Set up Logseq as learning journal, connected to a private GitHub repo for version control
Configured the Git plugin for auto-commit and sync
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PyTorch is the dominant framework in research and modern ML — TensorFlow has lost the research community
The Hands-On ML book is still worth using despite the TensorFlow title — Keras is now framework-agnostic and the sklearn chapters alone are worth it
Privacy-preserving ML (concrete-ml, PySyft) is a natural extension of existing ZK/crypto background — treat it as an expansion track after core ML foundations are solid
Logseq works on plain markdown files, so GitHub is a cleaner sync solution than Google Drive for a developer workflow
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What a tensor actually is and why it's the core ML data structure — need to sit with this before Phase 1 starts
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