Introduction to Neural Networks

Neural networks are computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) organized in layers:

Input Layer: Receives the initial data
Hidden Layers: Process and transform the data through weighted connections
Output Layer: Produces the final prediction or classification

Key Concepts:
- Activation Functions: Determine neuron output (ReLU, Sigmoid, Tanh)
- Backpropagation: Algorithm for training by adjusting weights
- Gradient Descent: Optimization method to minimize loss
- Epochs: Complete passes through the training dataset

Common Architectures:
1. Feedforward Networks: Information flows in one direction
2. Convolutional Neural Networks (CNNs): Excellent for image processing
3. Recurrent Neural Networks (RNNs): Handle sequential data
4. Transformers: State-of-the-art for NLP tasks

Training Process:
- Initialize weights randomly
- Forward pass: compute predictions
- Calculate loss using loss function
- Backward pass: compute gradients
- Update weights using optimizer
- Repeat until convergence

Applications span computer vision, natural language processing, speech recognition, and game playing.