NOTE: this course is current being developed by Eureka Labs. In the meantime I feel it's usefull to follow along the chapters as conceptualized

What I cannot create, I do not understand. -Richard Feynman

"In this course we will build a Storyteller AI Large Language Model (LLM). Hand in hand, you'll be able to create, refine and illustrate little stories with the AI. We are going to build everything end-to-end from basics to a functioning web app similar to ChatGPT, from scratch in Python, C and CUDA, and with minimal computer science prerequisites. By the end you should have a relatively deep understanding of AI, LLMs, and deep learning more generally."

I am probably not going to build a storyteller because I don't vibe with the idea. I looking to have the chapters as a central guide on what to do, then on a exploratory basis of what I feel is okay, study till I feel it's time to move on to the next chapter.

just so that I don't go off tracks and waste time on a niche:
we have 17 chapters - I feel 3 months is an okay time
so 17/3 = 6 chapters per month => 30/6 = 5 days for a chapter.
feels tight (I'll adjust as needed)

Syllabus

• Chapter 01 Bigram Language Model (language modeling)
  start - 18/12/25
• Chapter 02 Micrograd (machine learning, backpropagation)
• Chapter 03 N-gram model (multi-layer perceptron, matmul, gelu)
• Chapter 04 Attention (attention, softmax, positional encoder)
• Chapter 05 Transformer (transformer, residual, layernorm, GPT-2)
• Chapter 06 Tokenization (minBPE, byte pair encoding)
• Chapter 07 Optimization (initialization, optimization, AdamW)
• Chapter 08 Need for Speed I: Device (device, CPU, GPU, ...)
• Chapter 09 Need for Speed II: Precision (mixed precision training, fp16, bf16, fp8, ...)
• Chapter 10 Need for Speed III: Distributed (distributed optimization, DDP, ZeRO)
• Chapter 11 Datasets (datasets, data loading, synthetic data generation)
• Chapter 12 Inference I: kv-cache (kv-cache)
• Chapter 13 Inference II: Quantization (quantization)
• Chapter 14 Finetuning I: SFT (supervised finetuning SFT, PEFT, LoRA, chat)
• Chapter 15 Finetuning II: RL (reinforcement learning, RLHF, PPO, DPO)
• Chapter 16 Deployment (API, web app)
• Chapter 17 Multimodal (VQVAE, diffusion transformer)

Appendix

Further topics to work into the progression above:

• Programming languages: Assembly, C, Python
• Data types: Integer, Float, String (ASCII, Unicode, UTF-8)
• Tensor: shapes, views, strides, contiguous, ...
• Deep Learning frameworks: PyTorch, JAX
• Neural Net Architecture: GPT (1,2,3,4), Llama (RoPE, RMSNorm, GQA), MoE, ...
• Multimodal: Images, Audio, Video, VQVAE, VQGAN, diffusion