Forward-Forward Learning

This skill provides a comprehensive implementation of the Forward-Forward (FF) algorithm and its modern extensions, including Self-Contrastive and Distance-Forward learning. By replacing traditional backpropagation with two forward passes—positive and negative—it enables memory-efficient training where each layer optimizes its own 'goodness' function independently. This approach is particularly valuable for neuromorphic hardware, on-chip learning, and scenarios requiring high parallelism or reduced memory overhead, offering a biologically-inspired alternative to standard gradient descent workflows.

Key Features

Use Cases