Understanding Neural Networks: From Neurons to LLMs

Table of Contents

1. Neuron: The Basic Unit
2. Layer: Many Neurons Working Together
3. Block: Layers Grouped into Units
4. Network: The Complete Model
5. Why Blocks Matter in Modern AI
6. Evolution Toward Large Language Models

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1. Neuron: The Basic Unit

At the smallest scale, a neuron takes numbers as input and produces one number as output.

• Inputs: array of values from previous layer
• Weights: one weight per input
• Bias: a trainable constant
• Activation function: shapes the final output

Formula:

output = f( Σ (w_i * a_i) + b )

A neuron is nothing more than a weighted sum plus bias, passed through an activation function.

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2. Layer: Many Neurons Working Together

A layer is just a group of neurons running in parallel.

Example: input vector of size 4, output layer of 3 neurons.

• Weights: matrix of shape (4X3)
• Bias: array of size 3
• Output: array of size 3

Computation:

z = a^(L-1) * W + b
a^(L) = f(z)

So a layer transforms an input array into a new array.

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3. Block: Layers Grouped into Units

A block is several layers packaged together. Blocks are used because the same pattern of layers often repeats.

Example block:

• Input: size 20
• Layer 1: 20 → 12
• Layer 2: 12 → 6
• Layer 3: 6 → 4
• Output: size 4

At a glance:

20 → 12 → 6 → 4

The whole block is just “input 20, output 4.”

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4. Network: The Complete Model

A neural network is the full stack of blocks that solves a task.

• A small network may be one block.
• Larger networks are many blocks stacked together.
• The scope is the difference:
  • Block = part of the network
  • Network = the entire model

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5. Why Blocks Matter in Modern AI

• CNNs (for images) use convolutional blocks.
• ResNets use residual blocks.
• Transformers (used in LLMs) use transformer blocks: each block has self-attention, feedforward layers, normalization, and residual connections.
• By repeating the same block many times, networks scale to billions of parameters.

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6. Evolution Toward Large Language Models

• Start: single neurons → learn simple mappings.
• Next: layers → capture richer transformations.
• Then: blocks → reusable patterns that go deeper.
• Finally: networks made of hundreds of blocks → capable of handling language, vision, and more.

Large Language Models (LLMs) are just very large stacks of transformer blocks. The principle is the same as the tiny neuron: weighted sum + bias → activation.

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Takeaway

Neural networks scale by abstraction:

Neuron → Layer → Block → Network → LLM