An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain.
- It consists of interconnected nodes (neurons) organized in layers that process information to make predictions or classifications.
- ANNs are used in machine learning and deep learning to recognize patterns, analyze data, and make intelligent decisions.
Applications of Artificial Neural Networks
1.) Image Recognition:
- ANNs are widely used in computer vision for tasks like facial recognition and object detection.
Examples:
- Face ID in smartphones.
- Security cameras for identifying unauthorized individuals.
- Autonomous vehicles recognizing traffic signs and pedestrians.
2.) Speech Recognition:
- ANNs process and analyze audio signals to convert speech into text.
Examples:
- Voice assistants like Siri, Alexa, and Google Assistant.
- Call center automation using AI-based voice response systems.
3.) Stock Market Predictions:
- ANNs analyze historical financial data to predict stock prices based on trends and patterns.
Examples:
- AI-powered trading algorithms used by hedge funds.
- Risk assessment and fraud detection in banking.
Mathematical Model of Artificial Neural Networks:
Each neuron in an ANN performs a weighted sum of the input values and applies an activation function to produce an output.
Mathematical Formula:
Where:
- W = Weights (importance of each input).
- x = Inputs (data fed into the neuron).
- b = Bias (adjustment factor to optimize learning).
- f = Activation function (determines the neuron’s output).
- y = Output (prediction or classification result).
Activation Functions in ANNs:
An activation function in an Artificial Neural Network (ANN) is a mathematical function that determines the output of a neuron by transforming the input signal into a specific range, thereby introducing non-linearity into the model.
- This transformation helps the neural network learn and solve complex patterns and relationships in the data.
Common Types of Activation Functions
1.) Linear Activation Function:
The linear activation function outputs a value directly proportional to the input, meaning the output increases or decreases linearly with the input.
- It is used in simple regression models, where predicting a continuous value is required.
2.) Step Function:
The step activation function outputs a binary value, typically 0 or 1, depending on whether the input is above or below a certain threshold.
- It is used in basic classification tasks, such as determining whether an email is spam or not.
3.) Sigmoid Function:
The sigmoid activation function outputs a smooth, continuous value between 0 and 1, using the formula f(x) = 1 / (1 + e^(-x)).
- It is useful in probabilistic tasks, such as binary classification in logistic regression and image recognition, where the output can be interpreted as a probability.
