What is an artificial neural network?

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What is an Artificial Neural Network (ANN)?

An Artificial Neural Network (ANN) is a computational model inspired by the human brain. It consists of layers of interconnected units called neurons (or nodes) that process information. ANNs are used to recognize patterns, make predictions, and solve complex tasks like image recognition, natural language processing, and decision-making.

Key Components of an ANN

1. Input Layer – Receives raw data (features) from the dataset.

2. Hidden Layers – Intermediate layers where computations happen; they extract patterns and features from the input.

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

4. Weights & Biases – Parameters that adjust the influence of one neuron on another.

5. Activation Functions – Functions that introduce non-linearity (like ReLU, Sigmoid), allowing the network to model complex relationships.

How It Works

1. Each neuron receives input, multiplies it by weights, adds a bias, and passes the result through an activation function.

2. The output of one layer becomes the input for the next layer.

3. The network learns by adjusting weights and biases during training to minimize the difference between predicted and actual outputs (using optimization techniques like gradient descent).

Applications

• Image and speech recognition

• Natural language processing (chatbots, translation)

• Predictive analytics and forecasting

• Autonomous systems (self-driving cars, robots)

✅ In short:
An Artificial Neural Network is a layered network of interconnected nodes that learns patterns from data to make predictions or decisions, inspired by how the human brain processes information.

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