Neural Networks Fundamentals

Introduction

Neural networks are inspired by biological neurons and form the basis of deep learning.

Basic Components

• Neurons/Perceptrons: Basic computational units
• Layers: Input, hidden, and output layers
• Weights and Biases: Learnable parameters
• Activation Functions: ReLU, Sigmoid, Tanh

Forward Propagation

The process of passing input data through the network to get predictions.

Backpropagation

The algorithm used to compute gradients and update weights.

Loss Functions

• Mean Squared Error (MSE)
• Cross-Entropy Loss
• Binary Cross-Entropy

Optimization

• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Adam, RMSprop

Neural Network Architectures

Convolutional Neural Networks (CNN)

Designed for image processing and computer vision tasks.

Recurrent Neural Networks (RNN)

Suited for sequential data and time series.

Long Short-Term Memory (LSTM)

Solves vanishing gradient problem in RNNs.

Gated Recurrent Units (GRU)

Simplified version of LSTM with fewer parameters.

Residual Networks (ResNet)

Skip connections for training very deep networks.

Autoencoders

Unsupervised learning for dimensionality reduction and feature learning.

Generative Models

• Variational Autoencoders (VAE)
• Generative Adversarial Networks (GAN)