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🧠 TensorToIntelligence

Welcome to TensorToIntelligence — a build-in-public deep learning education series.

What You'll Learn

Each module in this series covers a first-principles implementation of a deep learning primitive:

  • Phase 1: Primitives — Linear layers, activations, loss functions, optimizers
  • Phase 2: Architectures — CNNs, ResNets, RNNs, LSTMs
  • Phase 3: Transformers — Attention, BERT, GPT, KV-cache
  • Phase 4: Generative — VAE, GAN, Diffusion models
  • Phase 5: Frontier — SSMs, MoE, LoRA, Quantization

Philosophy

Every module follows the same structure:

  1. Intuition — Why does this exist?
  2. Math — Formal derivation with LaTeX
  3. Code — Annotated Python implementation
  4. Test — Proof of parity with torch.nn
  5. Experiment — Visualizations and insights

Getting Started

Check out the Roadmap to see the full learning path, then dive into Phase 1 to begin your journey!

# The journey from tensor to intelligence starts here
import torch

x = torch.randn(32, 784)  # A batch of images
# ... and ends with models that can generate, understand, and reason.