Exploring the Feasibility of Forward Forward Algorithm in Neural Networks

This research explores the Forward-Forward Algorithm, proposed as an alternative to Backpropagation, in the context of Convolutional and Recurrent Neural Networks for multiclass classification. The authors' investigation aims to evaluate the algorithm's efficiency, advantages, and disadvantages for real-world applications. Results offer insights into the untapped potential and nuances of this approach, guiding researchers in optimizing neural network performance and potentially advancing a more human-like approach in Deep Learning.