Neural Networks

Neural Networks are computational models inspired by the human brain's interconnected neuron structure, designed to recognize patterns and relationships in data.

• Neurons (Nodes): Basic processing units analogous to neurons in the brain.
• Layers:
  • Input Layer: Receiving data input, each neuron represents a feature of the input data.
  • Hidden Layers: Intermediate layers that transform inputs through weights and activation functions.
  • Output Layer: Produces the final prediction or output of the model.

• Weights and Biases: Parameters that are learned and adjusted during training to minimize error.
• Activation Functions: Introduce non-linearity into the model (e.g., ReLU, Sigmoid, Tanh).
• Backpropagation: Training algorithm that updates weights and biases to minimize the loss function.
• Loss Function: A metric to quantify how well the model's predictions match the target values.

• Computational Costs: High resource requirements for training, especially with large datasets.