Last update: August 2025

Eclectic Learning & Inspiration List

A non-exhaustive but well-tested set of books, courses, blogs, and talks that have shaped my thinking in science, AI, and beyond

Core Reading & Viewing

Nobel Lectures

• Frances Arnold – 2018 Chemistry Nobel — How evolution can be engineered.
• Jennifer Doudna – 2020 Chemistry Nobel — The CRISPR story and why it matters.

Talks

• Andrew Ng – How AI Could Empower Any Business — AI as systematic application, not magic.

Books

• Three Hour Chef — A manual for learning anything fast, disguised as a cookbook
• Thinking, Fast and Slow
• Stumbling on to Happiness
• Loonshots
• Demon Haunted World
• Fooled by Randomness
• A Random Walk Down Wall Street
• The Way to Love
• Houston, We Have a Narrative - Great book on how to improve scientific communication
• Sapiens

Favorite Blogs

• Venture Capital for Life Scientists
• Studying the Studies — Nice overview for understanding statistical studies and how they are reported in peer-reviews, complement this by How to Lie with Statistics
• The AI Revolution – Wait But Why
• Biotech Platforms and Taste — Why “taste” matters in science.

Machine Learning & Data Science Essentials

80/20 rule: get the core idea, apply it, then fill gaps as you hit them.

Data science & ML

• Data Science from Scratch
• Introduction to Statistical Learning
• The 100 Page ML Book — Key ML concepts boiled to the essentials.

AI / ML

• Hands-On Machine Learning with Scikit-Learn & TensorFlow (GitHub) — Still the best structured ML intro.
• Practical Deep Learning for Coders – fast.ai

LLMs

• Build A Reasoning Model (From Scratch) - Everything by Sebastian Raska is a gem. This book is no different.

• Hands-on LLMs - Actionable lessons with practical math and algorithm background

• AI Engineering - Chip Huyen

Data Engineering

• Designing Data Intensive Applications

Seminal Papers

• Word2Vec — Words as vectors, meaning as geometry.
• Attention Is All You Need — The Transformer blueprint.
• BERT — Pretraining changes everything.
• ReACT — LLMs that think and act.

Perspectives, Reviews & Commentaries

Area Reviews

• A Survey of Deep Learning for Scientific Discovery — How deep learning is reshaping science.
• The Discipline of Machine Learning — ML’s core principles from one of its founders.

Best Practices & Pitfalls

• How to Avoid Machine Learning Pitfalls — The mistakes researchers keep making.
• Scikit-learn – Common Pitfalls — Debugging bad ML habits.
• Three Pitfalls to Avoid in Machine Learning — Shortlist of costly errors.
• A Few Useful Things to Know About Machine Learning — Timeless, hard-earned lessons.

Commentaries

• Statistical Modeling: The Two Cultures — Why stats and ML often talk past each other.
• The Hardware Lottery — How progress gets stuck on the wrong tools.
• Why Is AI Harder Than We Think? — The gap between perception and reality.

In Chemical Sciences

• Machine Learning for Materials Scientists – Best Practices — What works (and what doesn’t) in materials ML.
• Machine Learning in Synthetic Chemistry — Principles and promising directions.

Graph Networks

• Graph Networks: Relational Inductive Biases — The foundations of graph ML.
• How to Get Started with Graph Machine Learning — Beginner’s map to the field.
• Demystifying Graph Deep Learning — Making graphs intuitive.

Courses Worth Finishing

Machine Learning

• MIT Intro to Deep Learning
• Google ML Crash Course — Quick, pragmatic entry point
• Stanford CS231n — Computer vision’s modern foundation.
• NYU PyTorch Deep Learning — Great for PyTorch fluency

Data Science & Computation

• MIT Computational Thinking
• Harvard CS109 Data Science

Python code resources

Learning

• Automate the Boring Stuff — Coding utility from day one.
• Python Data Science Handbook — Essential Pandas, NumPy, Matplotlib.
• Visual Guide to NumPy — Arrays explained visually.

Projects & Practice

• Project Euler — Math puzzles that teach coding fluency.
• Calmcode — Bite-sized Python tips.

Writing Better Code

• Corey Schafer – Tips — Pragmatic code hygiene.

Data, Viz & Stats

Stats

• Think Stats — Stats for hackers.
• Telling Stories with Data — Numbers need a plot.

Visualization

• Fundamentals of Data Visualization — Clear thinking via clear charts.
• Python Graph Gallery — Examples by type.

Videos & Lecture Series

Science & AI

• Medicinal Chemistry Lecture Series
• MIT Deep Learning Series
• Andrej Karpathy’s Lectures — Deep learning taught by a leading industry expert
• 3Blue1Brown — Beautiful videos on foundational ML/DS concepts

Concept Explainers

• StatQuest — Stats explained like you’re five.

Blogs & Writing

• Paul Graham Essays — Thinking about thinking.
• Farnam Street — Tools for better decisions.
• Wait But Why — Long-form deep dives.

Software Developer Blogs

• Simon Willison — Tinkering in public; the best running commentary on LLM tooling.
• Luciano Ooijen — Thoughtful takes on software craft and AI-assisted development.
• Nicholas Carlini — Deep, adversarial research writing on ML security and evaluation.
• Randal Olson — Data science and machine learning, applied with taste.
• Sean Goedecke — Clear-eyed essays on how software actually gets built.
• DHH — Strong opinions on software, tools, and the business of building products.
• Nisarg Adhikari (notes) — Cybersecurity researcher, CTF player, and ethical hacker who writes proudly and widely.

Good articles that focus on better writing

• Paul Graham – Writing Usefully
• Draft No. 4 — John McPhee on the architecture of writing
• This Is Your Mind on Plants — Michael Pollan very nicely written account of plants effect on altered states and culture
• Smart Words – Linking Words