Neural networks are a fundamental concept in machine learning and artificial intelligence. This tutorial will provide an overview of neural networks, their components, and how they work.
Introduction
Neural networks mimic the human brain’s ability to learn from data. They are composed of interconnected nodes called neurons, which process information through layers of the network.
Neural Network Diagram
Components of a Neural Network
- Neurons: The basic building blocks of a neural network, responsible for processing data.
- Layers: A neural network consists of an input layer, one or more hidden layers, and an output layer.
- Weights and Biases: These parameters adjust the strength of the connections between neurons.
- Activation Functions: These functions determine whether a neuron should be activated or not.
Types of Neural Networks
- Feedforward Neural Networks: The simplest type of neural network, where data moves in only one direction.
- Convolutional Neural Networks (CNNs): Used primarily for image processing tasks.
- Recurrent Neural Networks (RNNs): Suitable for sequence data like text or time series.
How Neural Networks Work
Neural networks learn by adjusting the weights and biases of their neurons through a process called backpropagation. This allows the network to improve its accuracy over time.
For more detailed information on neural networks, check out our deep learning tutorial.