Neural network architecture refers to the structure and design of a neural network, including the number and arrangement of layers, the number of neurons in each layer, and the connections between neurons. The architecture significantly influences a network’s capacity to learn and represent complex patterns in data. Here’s a comprehensive overview of neural network architecture:
I. Key Components of Neural Network Architecture:
1. Layers:
- Input Layer: Receives input features and does not perform any computation.
- Hidden Layers: Intermediate layers between the input and output layers where computation and learning occur.
- Output Layer: Produces the network’s final output, often the prediction for a specific task.
2. Neurons (Nodes):
- Neurons in a Layer: The fundamental processing units that receive inputs, apply weights, perform a computation, and produce an output.
- Activation Function: Neurons often apply an activation function to introduce non-linearity into the network.
3. Connections (Weights and Biases):
- Weights: Parameters that determine the strength of…
