Artificial Neural Networks

TL;DR

• A machine learning model composed of interconnected processing nodes (neurons) organized in layers that learn patterns from data.
• Neurons use nonlinear activation functions and weighted connections; weights are adjusted during training to reduce prediction error.
• Useful for tasks that benefit from automatic feature learning from raw data.

Definition

An artificial neural network is a computational model that is inspired by the way biological neurons work in the human brain. It is a machine learning algorithm that is designed to recognize patterns in data and make predictions or take actions based on that data.

Explanation

At the core of an artificial neural network is a network of simple processing nodes, called neurons. These neurons are connected to each other through weighted edges that represent the strength of the connection between the neurons. The network is typically arranged in layers, with the input layer receiving the raw data, one or more hidden layers processing the data, and the output layer producing the final output of the network.

Each neuron receives input from other neurons in the previous layer, processes this input using a non-linear activation function, and then passes the processed output to the next layer of neurons. The activation function determines the output of a neuron based on the input it receives from other neurons.

The weights on the edges between the neurons are learned through a process called training. During training, the network is presented with a set of inputs and the corresponding correct outputs, and the weights on the edges are adjusted to minimize the error between the predicted output and the correct output. This process is repeated for many different inputs, and over time the weights are adjusted to produce more accurate predictions.

One key advantage of artificial neural networks is their ability to automatically learn and extract useful features from raw data without requiring human intervention. This enables their application across a wide variety of problems.

Examples

Predicting house prices

A simple artificial neural network might be used to predict the price of a house based on its size, location, and other factors. The input layer receives raw data about the house (size, location, features). This data is passed through one or more hidden layers to extract features and patterns. The output layer produces a prediction of the house price.

Customer churn prediction

An artificial neural network might analyze a large dataset of customer transactions to identify patterns and predict which customers are likely to churn. The input layer receives raw transaction data (products purchased, amount spent). Hidden layers automatically learn and extract useful features from this data. The output layer predicts which customers are likely to churn based on learned patterns.

Self-driving car

For a self-driving car, the input layer receives data from sensors on the car (cameras, radar, lidar). Hidden layers process this sensor data to identify objects in the environment (other cars, pedestrians, traffic signs). The output layer produces actions for the car (steering, accelerating, braking) based on the processed data from the hidden layers.

Use cases

• Image recognition
• Speech recognition
• Natural language processing
• Predictive modeling

Related terms

• Neuron
• Layers (input layer, hidden layers, output layer)
• Weights / weighted edges
• Activation function
• Training
• Features
• Sensors