NEURAL NETWORKS

Overview

Neural Networks are computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information using weighted connections and activation functions to learn complex patterns.

Architecture

›Input Layer - Receives data
›Hidden Layers - Process information
›Output Layer - Produces results
›Weights & Biases - Learnable parameters

Forward Pass

def forward_pass(x, weights, biases):
  for layer in range(num_layers):
    x = activation(
      np.dot(x, weights[layer]) 
      + biases[layer]
    )
  return x

# Common activations:
# ReLU: max(0, x)
# Sigmoid: 1/(1 + e^(-x))
# Tanh: (e^x - e^(-x))/(e^x + e^(-x))

Types

Feedforward Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Transformers