What is a neural network?

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A neural network is a machine learning model inspired by the structure and function of the human brain. It is designed to recognize patterns, learn relationships in data, and make predictions or decisions. Neural networks form the foundation of deep learning and are widely used in tasks like image recognition, natural language processing, speech recognition, and recommendation systems.

Structure of a Neural Network:

• Neurons (Nodes): Basic units that receive inputs, process them, and pass outputs forward.

• Layers:

  • Input layer: Takes in the raw data (features).

  • Hidden layers: Process data through weighted connections and activation functions.

  • Output layer: Produces the final prediction or classification.

• Weights & Biases: Parameters adjusted during training to improve accuracy.

• Activation Functions: Decide whether a neuron should be activated, introducing non-linearity (e.g., ReLU, Sigmoid, Tanh).

How it Works:

1. Data is fed into the input layer.

2. Each connection multiplies the input by a weight and adds a bias.

3. The result passes through an activation function.

4. The process continues layer by layer until reaching the output.

5. During training, errors are measured and weights are adjusted using backpropagation and gradient descent.

Key Advantages:

• Can learn complex, non-linear relationships.

• Adaptable across diverse domains.

• Capable of automatic feature extraction (especially in deep networks).

👉 In short, a neural network is a system of interconnected nodes that mimics how the brain processes information, enabling machines to learn from data and make intelligent predictions.

🔑Read More:

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