In this tutorial, we'll delve into the fundamentals of neural networks, covering the basic concepts, architecture, and training processes.
Basic Concepts
- Neuron: The basic building block of a neural network. It takes inputs, processes them, and produces an output.
- Layer: A group of interconnected neurons. There are three types of layers in a neural network: input, hidden, and output.
Architecture
A typical neural network architecture consists of:
- Input Layer: Receives the input data.
- Hidden Layers: Performs computations on the data received from the input layer and passes it to the output layer.
- Output Layer: Produces the final output.
Training Process
The training process of a neural network involves:
- Forward Propagation: Passing the input data through the network to generate an output.
- Backpropagation: Adjusting the weights and biases of the neurons based on the error between the predicted output and the actual output.
Neural Network Architecture
For more information on neural networks, check out our deep learning tutorials.