What are artificial neural networks (ANNs)?

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Artificial Neural Networks (ANNs)

• An Artificial Neural Network (ANN) is a computational model inspired by the human brain.

• It consists of interconnected nodes (called neurons) arranged in layers that process information.

• ANNs are widely used in machine learning and deep learning for tasks like image recognition, speech processing, and natural language understanding.

Structure of ANN

1. Input Layer – Receives raw data (features).

2. Hidden Layers – Perform computations by applying weights, biases, and activation functions.

3. Output Layer – Produces the final prediction or classification.

How It Works (Simplified)

1. Each neuron receives inputs.

2. Inputs are multiplied by weights (importance of features).

3. A bias is added.

4. The result passes through an activation function (e.g., sigmoid, ReLU) to introduce non-linearity.

5. Output is passed to the next layer until the final prediction.

Key Features of ANNs

• Learning ability: Adjusts weights using training data.

• Backpropagation: Algorithm used to minimize error by updating weights.

• Non-linear modeling: Can handle complex relationships.

• Generalization: Can work on unseen data after training.

Example Use Cases

• Image & face recognition

• Fraud detection

• Medical diagnosis

• Self-driving cars

• Natural language processing (chatbots, translation)

✅ In short:
An ANN is a machine learning model that mimics how the brain works, using layers of artificial neurons to learn patterns and make predictions.

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