Neural Networks (NN)
It is the building blocks of Deep Learning.
Neural Networks are computational models inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (or neurons), each of which performs simple calculations. By combining many of these neurons, neural networks are able to approximate complex functions and model intricate patterns in data.
Structure and Operation:
- Input Layer: Receives the raw data (e.g., pixel values in an image).
- Hidden Layers: One or more layers that transform the input data through learned weights and biases. Each hidden layer applies an activation function—such as ReLU, sigmoid, or tanh—to introduce non-linearity into the model.
- Output Layer: Produces the final prediction or classification.