AI Research Engineer

Nine courses in sequence, from Math for AI through Core ML, Deep Learning, Transformers, Computer Vision, NLP & Speech, Generative AI, Reinforcement Learning, and Multimodal AI. Every one is implement-it-yourself: you build a mini PyTorch, then attention, then diffusion, then RLHF. The longest route here by a wide margin.

9 courses · Advanced

The path, in order

  1. Math for AI & ML — The math behind every ML algorithm, built in code rather than on paper
  2. Core ML: Algorithms from Scratch — Build every classic ML algorithm by hand, then verify against scikit-learn
  3. Deep Learning from Scratch — Build your own mini PyTorch: autograd, layers and training loops from the ground up
  4. Transformers & LLMs from Scratch — Pre-train a real 124M parameter GPT: tokenizer, data pipeline, training and evaluation
  5. Computer Vision Engineering — From convolutions to ViTs. Build models that see
  6. NLP & Speech Processing — From word embeddings to voice AI. Build models that understand language
  7. Generative AI Fundamentals — From GANs to diffusion to flow matching. Build and customize generative AI systems
  8. Reinforcement Learning — From bandits to RLHF. Build agents that learn by doing
  9. Multimodal AI Systems — From CLIP to GPT-4V. Build models that see, hear and read

Optional foundations, if you are starting from zero

The path assumes you can already write Python. If you cannot, take these first.

  1. Programming & CS Foundations — How to think like a programmer, from zero, before any AI or math content
  2. Quantitative Foundations — Algebra, functions and the math comfort you need before Math for AI & ML

What you become

An engineer who can read a paper and implement it, from attention to diffusion to RLHF, rather than only calling a library that already did.

Roles this path prepares you for

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