mdhabibi Goto Github PK

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-mnist-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.

github_actions

invertedpendulumsds-ben

Projektausarbeitung zum Wahlmodul - Sondergebiete der Simulation - WS21/22

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.

machine-learning-for-physicists

Code for "Machine Learning for Physicists 2020" lecture series

mdhabibi

mlalgorithms_check

Minimal and clean examples of machine learning algorithms implementations

multifidelity_pod_ae

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.

pbdl-book-pinn

Welcome to the Physics-based Deep Learning Book (v0.2)

postpic-epoch

The open-source particle-in-cell post-processor.

telescopeml-forked

Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra

variational_autoencoders

Various VAEs