Introduction to Neural Networks
Neural networks are computing systems inspired by biological neural networks that constitute animal brains. They are a fundamental building block of modern artificial intelligence and machine learning systems.
What are Neural Networks?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Key Components
- Neurons (Nodes): The basic processing units
- Weights: Parameters that transform input data
- Activation Functions: Functions that determine if a neuron should be activated
- Layers: Groups of neurons that process data sequentially
How They Work
Neural networks process information through interconnected nodes, where each connection has an associated weight. The network learns by adjusting these weights based on training data.
Common Applications
- Image recognition
- Natural language processing
- Speech recognition
- Recommendation systems
- Autonomous vehicles
Next Steps
Once you understand the basics, explore:
- Different types of neural networks (CNN, RNN, LSTM)
- Training algorithms (backpropagation)
- Deep learning frameworks (TensorFlow, PyTorch)