Definition
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process data in layers to recognize patterns and solve complex problems.
- Neural networks are a fundamental component of artificial intelligence (AI), designed to mimic the way the human brain processes information.
- They are particularly powerful for tasks such as image recognition , natural language processing , and predictive analytics.
Structure of Neural Networks
The basic structure of a neural network consists of three types of layers:
- Input Layer
- Hidden Layers
- Output Layer
Input Layer
- The input layer is where the neural network receives data.
- Each node (or neuron) in this layer represents a feature of the input data.
Example
In an image recognition task, each node might represent the pixel value of an image.
Hidden Layers
- Hidden layers are the core of the neural network, where most of the computation happens.
- These layers are called "hidden" because they are not directly exposed to the input or output.
- Each neuron in a hidden layer receives inputs from the previous layer, processes them, and passes the result to the next layer.
Note
- The number of hidden layers and the number of neurons in each layer can vary depending on the complexity of the task.
- Networks with many hidden layers are known as deep neural networks.
Output Layer
- The output layer produces the final result of the network.
- The number of neurons in this layer depends on the specific task.
Example
For binary classification, there might be a single output neuron representing the probability of a positive class.
How Neural Networks Work
- Neural networks operate through a process called forward propagation, where data moves from the input layer to the output layer.
- It also performs backpropagation, where the network learns by adjusting its weights based on errors.
