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Neural Networks

PART 4 — Neural Networks

  • Neuron: A tiny mathematical function that takes inputs, multiplies them by weights, adds a bias, and passes them through an activation function.
  • Layer: A collection of neurons working side-by-side.
  • Forward pass: The process of pushing data through the layers from input to output (this is inference).
Input (Token ID)
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Neural Network (Billions of parameters spread across dozens of layers)
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Output (Probability of the next token)

Why does an HLD engineer care?

You do not need to code a forward pass. However, you must understand that pushing data through billions of parameters requires massive amounts of parallel matrix multiplication. CPUs are too slow for this; hence, AI infrastructure is fundamentally bound to GPUs.