🧩 Key Concepts

1. Neural Networks
   • Feynman style: A neural net is just a giant function approximator that learns the relationship between inputs (x) and outputs (y) by adjusting weights.
   • Analogy: Like tuning guitar strings until the chords sound right.
2. Optimization & Backpropagation
   • Gradient Descent: The “compass” that always points downhill in the error landscape.
   • Inversion Thinking: What happens if gradients vanish or explode? → Your model doesn’t learn. Solutions: ReLU activations, normalization, careful initialization.
3. Generative Models
   • Discriminative vs. Generative: Discriminative says “this is a cat”, Generative says “here’s a new cat picture you’ve never seen”.
   • Generative models build probability distributions and can sample new datadevelopers.google.com.
4. Transformers & Attention
   • Attention = spotlight mechanism. Each word looks at other words to decide importance.
   • Solve sequential dependencies without recurrent loops.
   • Why it matters: This is the architecture behind GPT, LLaMA, DeepSeek

Feynman Explaination:

• We teach a parrot (the model) to guess the next word by seeing lots of text.
• The parrot “looks around” inside the sentence using attention to decide what matters.
• We speed it up by only waking up the pieces we need (Flash/SDPA, KV-cache).

DeepSeek optimization intuition:

• SDPA/FlashAttention: compute attention efficiently inside CUDA kernels.

• Mixed precision (bf16/fp16): same accuracy, fewer bytes, faster math.

• KV-cache: remember previous keys/values so decoding is O(1) per token, not O(n).

  (MoE, progressive distillation, routing come later.)

Google Colab Hands on Learning goals

• Build a tiny GPT in PyTorch you can train on a toy corpus.
• Understand embeddings, attention, MLP, residuals, LayerNorm.