Feedforward Neural Network - Deep Learning Visualizer

What is a Feedforward Neural Network?

A feedforward neural network (FNN) is the most fundamental type of artificial neural network. Information flows in one direction - from input through hidden layers to output - without cycles or loops.

Key concepts:

Input Layer: Receives raw data (features) for processing
Hidden Layers: Transform inputs through weighted connections and activation functions
Output Layer: Produces final predictions or classifications
Activation Functions: Non-linear functions (ReLU, Sigmoid, Tanh) that enable learning complex patterns
Forward Propagation: Computing outputs by passing data through the network layer by layer
Backpropagation: Training algorithm that adjusts weights by propagating errors backward

Controls

Ready to train

Network Architecture:

Current: [2, 4, 3, 1]

Activation Function:

Sample Data Pattern:

Learning Rate:

0.1

Training Controls:

Animation:

Epoch

Loss

0.00

Neurons