Neural Networks Explained

What are Neural Networks?

Neural networks are computing systems inspired by the human brain. They consist of interconnected neurons (nodes) organized in layers that process information and learn patterns.

Biological Inspiration

Your brain has ~86 billion neurons connected by synapses. When you learn something, these connections strengthen or weaken. Artificial neural networks work similarly — they adjust connection weights through training.

Anatomy of a Neural Network

1. Neurons (Nodes)

Each neuron:

• Receives inputs from other neurons
• Multiplies each input by a weight (importance)
• Sums them up and adds a bias
• Passes the result through an activation function

2. Layers

• Input Layer — Receives the raw data (e.g., pixels of an image)
• Hidden Layers — Process and transform information. "Deep" learning = many hidden layers
• Output Layer — Produces the final result (e.g., "cat" or "dog")

3. Weights and Biases

Weights determine how strongly neurons are connected. During training, the network adjusts weights to minimize errors.

4. Activation Functions

Activation functions decide whether a neuron "fires" (activates):

• ReLU — Most common: max(0, x)
• Sigmoid — Squashes values between 0 and 1
• Softmax — Converts outputs to probabilities

How Neural Networks Learn

The Learning Process

1. Forward Pass — Input flows through the network, producing an output
2. Calculate Loss — Compare output to the expected answer (how wrong is it?)
3. Backpropagation — Calculate how much each weight contributed to the error
4. Update Weights — Adjust weights slightly to reduce the error
5. Repeat — Do this thousands/millions of times with many examples

Types of Neural Networks

Feedforward Networks

Simple — data flows one way from input to output. Good for basic classification.

Convolutional Neural Networks (CNNs)

Specialized for image data. They detect features like edges, textures, and shapes. Used in facial recognition, medical imaging, and self-driving cars.

Recurrent Neural Networks (RNNs)

Designed for sequential data like text, speech, and time series. They have "memory" of previous inputs.

Transformers

The architecture behind modern language models (GPT, Claude). They process all inputs simultaneously using attention mechanisms — understanding context and relationships between words.