repo contains sample code snippets on #ML, #DeepLearning, #GenAI topics

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FEDn: A production-grade, open federated learning framework. This repository contains the open source Python framework, CLI and API.

This repository contains projects that use models created from deep learning frameworks in image or object classification and detection tasks.

Clinical Analysis and Modeling for EHR data, 2D and 3D medical imaging data, applying Pydicom, Pytorch, Keras-tensorflow, with medical and healthcare regulations and standards, for FDA submission

This project aims to detect pneumonia from chest X-ray images using a Convolutional Neural Network (CNN). The model is trained on a dataset of chest X-ray images and evaluated for its performance. The project is ongoing, and I aim to fine-tune the model in the future. If you are seeing this, it means I am still working on the project.

ML/DL Life Cycle utilities with support for BingImageCreator and Discord

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

This project uses TensorFlow and Keras to develop a deep learning model for detecting glasses on human faces in images. It employs convolutional neural networks (CNNs) to classify images into "with glasses" and "without glasses" categories, offering a versatile tool for applications like facial recognition and virtual try-on experiences.

A modular inverse QSAR pipeline

This web app project empowers the strenght of BPM flow chart to monitor envoices and rules.

Stock predictions with LSTM

AI for Eart Observations (AI4EO)

🌟 This repository houses a collection of image classification models for various purposes, including vehicle, object, animal, and flower classification. Each classifier is built using deep learning techniques and pre-trained models to accurately identify and categorize images based on their respective classes.

A collection of AI and ML projects demonstrating various techniques, algorithms, and applications.

Developed a Keras-based text classification model, employing LSTM and CNN layers to accurately classify and analyze text data.

Project exploring how a CNN alongside computer vision can be used to detect drawn keys and track finger input,.

This project leverages deep learning techniques to classify images from the CIFAR-10 dataset using both Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), highlighting the effectiveness of CNNs for image recognition tasks.

sentiment-analysis using pertrained models (BERT, BiLSTM)