JAX compilation of RDDL description files, and a differentiable planner in JAX.

Code snippets for a crash course in ml & biomed eng.

A simple deep learning library for training end-to-end fully-connected Artificial Neural Networks (ANNs), primarily based on numpy and autograd.

Effect of Optimizer Selection and Hyperparameter Tuning on Training Efficiency and LLM Performance

This project focuses on land use and land cover classification using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The classification task aims to predict the category of land based on satellite or aerial images.

🌌 📚 This project embarks on an exploration in the Mathematical Foundations of Computer Science, heralding the inaugural semester of 2023 with a profound dive into the realm of Linear Regression.

This AI "Plant Doctor" uses image analysis to diagnose plant diseases. It learns from the PlantVillage dataset and employs powerful techniques

Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻

Artificial neural network package written in python

This implementation serves as a starting point for understanding and experimenting with linear regression algorithm and optimization methods in Python.

Retrieval based biomedical chatbot to answer questions related to diseases

deforce: Derivative-Free Algorithms for Optimizing Cascade Forward Neural Networks

Built a custom adam scheduler using gradient clipping, LR scheduling, momentum updates, with two different loss functions

Visualisation SGD optimizator, exemples

Habiba Omran Thesis

Detect and classify toxic behavior in social media comments using a bidirectional LSTM-based neural network. Achieved precision of 0.932 and recall of 0.733. Applications include customer service, reputation management, and market research. Real-time predictions available via a Gradio app. Future scope includes multi-lingual sentiment analysis.

MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library

[Optimization algorithms] Study of the Proximal Gradient Method, Stochastic Gradient Descent method and Adam optimizer