Forward Forward Algorithm Implementation

This repository contains implementations of Geoffrey Hinton's Forward Forward Algorithm. The algorithm is a fundamental component in deep learning, particularly in training neural networks.

Notebooks:

1. ForwardForwardDependenceonParams.ipynb:

   • This notebook explores the dependency of the Forward Forward Algorithm on learning rate. It provides insights into how different learning rates affect the convergence and performance of the algorithm.

2. ForwardForwardKeraswithHyperOpt.ipynb:

   • Implemented the Forward Forward Algorithm using Keras, a high-level neural networks API. Additionally, this notebook incorporates the functionality of hyperparameter tuning using HyperOpt, enabling optimization of the algorithm's parameters for improved performance.

Usage:

• Each notebook contains detailed explanations and code examples, making it easy to understand and utilize the implementations provided.

Dependencies:

• Python 3.x
• Jupyter Notebook
• TensorFlow
• Keras
• HyperOpt

Feel free to explore the notebooks and experiment with the implementations!