cnn-with-adversarial-examples

We use the MNIST dataset to train a simple CNN then we create Adversarial examples

countries-socio-economic-profile-clustering

Clustering of Countries based on their socio-economic profiles

deeppocket

Ligand Binding Site detection using Deep Learning

github-slideshow

A robot powered training repository :robot:

mar-ina-thal

nested-cv

Use the nested cross validation method in order to fine tune and test the perfomance of 5 machine learning algorithms, namely Sup- port Vector Machines, Linear Regression, Gaussian Naive Bayes and Linear Discriminant Analysis, on the Hepatitis C dataset

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

rgcs-classification

Classification of Retinal Ganglion Cell based on gene expression

rotamers-clustering

Study of GSK3 active site and clustering approaches

salinas-hsi-clustering

The aim of this project is to implement and evaluate the performance of CFO and Hierarchical Clustering algorithms in the task of finding homogeneous regions in the Salinas HSI

scrna-seq-data-analysis

Utilization of dimensionality reduction and GMM clustering for the analysis of single cell RNA data

socio-economic-profile-country-clustering

Analyze socio economic data of vaious countries and group them into clusters using matlab

sr2019

strelka

Strelka2 germline and somatic small variant caller

tfbs-nn

Constructing a pipeline for the creation of a tfbs dataset and the training of a NN

transfer-learning-vision

Study on the performance of pre-trained models (VGG16, EfficientNetb0, ResNet50, ViT16) with weight fine tuning, as well as classical ML algorithms (Naive Bayes, Logistic Regression, Random Forest) on a dataset of 6.806 fungi microscopy Images utilizing Pytorch.

variant-callers-comparison

A qualitative comparison of Gatk and Strelka2 variant callers for exome sequencing data of germline variants