Neural networks are a fundamental concept in machine learning, serving as the core component of various algorithms. They mimic the human brain's ability to learn from experience and make decisions.
Basic Components of a Neural Network
A neural network consists of three main components:
- Neurons: The basic units that perform computations and transmit information.
- Weights: Numbers that represent the strength of connections between neurons.
- Biases: Numbers that are added to the sum of inputs to a neuron before it is passed through an activation function.
Types of Neural Networks
There are several types of neural networks, each with its unique characteristics:
- Feedforward Neural Networks: The simplest form of neural network, where data moves in only one direction.
- Convolutional Neural Networks (CNNs): Widely used for image recognition tasks due to their ability to capture spatial hierarchy.
- Recurrent Neural Networks (RNNs): Suited for sequential data like time series or natural language.
How Neural Networks Learn
Neural networks learn through a process called backpropagation. Here's a brief overview:
- Forward Propagation: Input data is fed through the network, and the output is generated.
- Loss Calculation: The difference between the predicted output and the actual output is calculated.
- Backpropagation: The loss is propagated back through the network, and the weights and biases are adjusted to minimize the loss.
Resources
For more in-depth learning, check out our Machine Learning Documentation.