Pinned Repositories
Automated-Cell-Semantic-Segmentation-with-UNet
A machine learning solution for automating nucleus detection in biomedical images, leveraging the U-Net architecture to accelerate medical research and disease treatment discovery.
Capstone-Project-IBM
A data-driven project to predict the success of Falcon 9 rocket landings, crucial for cost analysis and competitive strategy in the space industry. Involves data manipulation in Pandas, JSON data processing, and insightful analysis using Python.
CNN-Predictor-for-Malaria_Cells-LIME-CAM
Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.
DeepLearning
Python code accompanying the course "A deep understanding of deep learning (with Python intro)"
DeepLearning-VAE
Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.
fastapi-questionnaire-api
A FastAPI application to manage and retrieve questionnaire data
github_actions
gpu-python-tutorial
GPU Development in Python 101 tutorial
LIME-for-Time-Series
LIME for TimeSeries enhances AI transparency by providing LIME-based interpretability tools for time series models. It offers insights into model predictions, fostering trust and understanding in complex AI systems.
Neural-Compression-with-Autoencoders
Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.
mdhabibi's Repositories
mdhabibi/LIME-for-Time-Series
LIME for TimeSeries enhances AI transparency by providing LIME-based interpretability tools for time series models. It offers insights into model predictions, fostering trust and understanding in complex AI systems.
mdhabibi/CNN-Predictor-for-Malaria_Cells-LIME-CAM
Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.
mdhabibi/DeepLearning-VAE
Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.
mdhabibi/Neural-Compression-with-Autoencoders
Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.
mdhabibi/Automated-Cell-Semantic-Segmentation-with-UNet
A machine learning solution for automating nucleus detection in biomedical images, leveraging the U-Net architecture to accelerate medical research and disease treatment discovery.
mdhabibi/Capstone-Project-IBM
A data-driven project to predict the success of Falcon 9 rocket landings, crucial for cost analysis and competitive strategy in the space industry. Involves data manipulation in Pandas, JSON data processing, and insightful analysis using Python.
mdhabibi/DeepLearning
Python code accompanying the course "A deep understanding of deep learning (with Python intro)"
mdhabibi/fastapi-questionnaire-api
A FastAPI application to manage and retrieve questionnaire data
mdhabibi/github_actions
mdhabibi/gpu-python-tutorial
GPU Development in Python 101 tutorial
mdhabibi/InvertedPendulumSDS-Ben
Projektausarbeitung zum Wahlmodul - Sondergebiete der Simulation - WS21/22
mdhabibi/machine-learning-for-physicists
Code for "Machine Learning for Physicists 2020" lecture series
mdhabibi/mdhabibi
mdhabibi/Variational_AutoEncoders
Various VAEs
mdhabibi/ML_Turb_CR_DeepLearning
PyTorch CNNs and U-nets trained on astrophysical turbulence simulations
mdhabibi/MLAlgorithms_check
Minimal and clean examples of machine learning algorithms implementations
mdhabibi/movie-recommendation-systems
A comprehensive collection of movie recommendation systems, implementing collaborative filtering, content-based filtering, weighted average, and more.
mdhabibi/MultiFidelity_POD_AE
mdhabibi/pbdl-book-PINN
Welcome to the Physics-based Deep Learning Book (v0.2)
mdhabibi/PlasmaPy
An open source Python package for plasma research and education
mdhabibi/postpic-epoch
The open-source particle-in-cell post-processor.
mdhabibi/pyppt
Python interface for adding figures to Microsoft PowerPoint presentations on-the-fly.
mdhabibi/TelescopeML-forked
Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra