About Feedforward Neural Network
A feedforward neural network passes data through multiple layers of neurons. Each layer applies a linear transformation (weights × inputs + bias) followed by a non-linear activation function. This visualization shows the forward propagation process layer by layer, revealing how raw inputs are transformed into predictions through successive non-linear transformations.
Complexity Analysis
- Time Complexity
- O(Σ n_l × n_{l-1})
- Space Complexity
- O(Σ n_l)
- Difficulty
- intermediate
Key Concepts
Layer-by-Layer Transformation
Each layer takes the previous layer's output, applies W×a+b then activation.
Hidden Representations
Hidden layers learn internal representations of the input data.
Universal Approximation
A network with at least one hidden layer can approximate any continuous function.
Common Pitfalls
Depth vs Width
More layers allow hierarchical features; more neurons increase per-layer capacity.