Neural Networks & Deep Learning

Neural networks are a class of machine learning models inspired by the structure of the biological brain. Deep learning refers to neural networks with many layers, enabling them to learn complex, hierarchical representations of data.

---

Biological Inspiration

The human brain contains approximately 86 billion neurons, each connected to thousands of others via synapses. Artificial neural networks borrow this concept in a simplified form:

Biological	Artificial
Neuron	Node (unit)
Synapse	Connection (weight)
Electrical signal	Numerical value
Signal strength	Weight value
Firing threshold	Activation function

Note: While inspired by biology, artificial neural networks are a significant simplification. Modern research focuses on what works mathematically rather than strict biological accuracy.

---

The Artificial Neuron (Perceptron)

The simplest neural network unit is the perceptron, introduced by Frank Rosenblatt in 1958.

Artificial Neuron:

  x1 ──w1──┐
            │
  x2 ──w2──┼──▶ [ Sum(weighted inputs) + bias ] ──▶ [ Activation f() ] ──▶ output
            │
  x3 ──w3──┘

  Inputs    Weights        Summation                  Activation         Output