Neural Network Algorithm
What is a Neural Network?
A Neural Network is a machine learning model inspired by the structure of the human brain. It consists of interconnected layers of artificial neurons (nodes) that process information and learn complex patterns from data.
Architecture
Layers
- Input Layer: Receives the data features
- Hidden Layers: Process and transform data
- Output Layer: Produces predictions
Components
- Neurons: Processing units that compute weighted sums
- Weights: Learnable parameters connecting neurons
- Activation Functions: Non-linear transformations (ReLU, sigmoid, tanh)
- Bias: Offset term for each neuron
How It Works
- Forward Pass: Data flows through layers, applying weights and activations
- Loss Calculation: Compare prediction to actual value
- Backpropagation: Calculate gradients of the loss
- Weight Update: Adjust weights using gradient descent
- Repeat: Iterate until convergence
Types of Neural Networks
- MLP (Multi-Layer Perceptron): Standard feedforward network
- CNN (Convolutional): For image data
- RNN/LSTM: For sequential data
- Autoencoder: For dimensionality reduction
Advantages
- Complex Patterns: Captures non-linear relationships
- Versatile: Works with various data types
- Scalable: Performance improves with more data
- Feature Learning: Automatically extracts features
Disadvantages
- Data Hungry: Requires large datasets
- Computationally Expensive: Slow to train
- Black Box: Less interpretable than other models
- Hyperparameter Sensitive: Many parameters to tune
Related Terms
- Deep Learning: Neural networks with many layers
- Feature Selection: Less critical for neural networks
- Standard Scaling: Important preprocessing step