Welcome to the Neural Networks documentation section. Here, you will find comprehensive information about neural networks, their architecture, and applications. Neural networks are a fundamental component of artificial intelligence and machine learning, enabling computers to learn from data and make decisions.
Overview
Neural networks are inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, that work together to process information. Each neuron receives input, processes it, and passes the output to other neurons.
Key Components
- Neurons: The basic building blocks of neural networks.
- Layers: A collection of neurons that perform a specific task.
- Weights and Biases: Parameters that determine the strength of connections between neurons.
- Activation Functions: Determine whether a neuron should be activated or not.
Types of Neural Networks
- Feedforward Neural Networks: The simplest type of neural network, where the data moves in only one direction.
- Convolutional Neural Networks (CNNs): Excellent for image recognition and processing.
- Recurrent Neural Networks (RNNs): Suited for sequential data like time series or natural language.
- Generative Adversarial Networks (GANs): Used for generating new data that resembles real data.
Applications
Neural networks have a wide range of applications, including:
- Image and Video Recognition
- Natural Language Processing
- Medical Diagnosis
- Financial Modeling
Resources
For further reading, we recommend the following resources:
Neural Network Architecture