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2022

BST 260: Introduction to Data Science Fall 2022 Course Repository

awesome-bugbounty-writeups

A curated list of bugbounty writeups (Bug type wise) , inspired from https://github.com/ngalongc/bug-bounty-reference

awesomexss

Awesome XSS stuff

business-machine-learning

A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)

covid-19

Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images

covid-19-symptom-detection-using-ml-

Designed a Machine Learning model which takes symptoms as input and then calculates the percentage of being infected by Covid-19.

covid-19_mlproject

This project is our final course project for Principles and Techniques of Data Science at UC Berkeley

covid19coughdetection

A Convolutional Neural Network which is trained to detect COVID 19 even in asymptotic patients using only cough recordings.

cs-523-public

cs-video-courses

List of Computer Science courses with video lectures.

cs208

detection-of-covid-19-positive-cases-using-ml

dsbook

Repository for data science book

federated

A framework for implementing federated learning

federated-learning-pytorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

fednlp

FedNLP: A Research Platform for Federated Learning in Natural Language Processing

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

genietype

Implementation of Federated Learning For Mobile Keyboard Prediction: https://arxiv.org/abs/1811.03604

licchavibyzantineresilience

Byzantine resilience investigation under federated learning setup

ml-course-notes

🎓 Sharing course notes on all topics related to machine learning, NLP, and AI

ml-for-paediatric-covid-diagnosis

A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

mlops-basics

modified-darkcovidnet

multiclass-image-classification-

The main aim of the project is to scan the X-rays of human lungs and classify them into 3 given categories like healthy patients, patients with pre-existing conditions, and serious patients who need immediate attention using Convolutional Neural Network. The provided dataset of Grayscale Human Lungs X-ray is in the form of a numpy array and has dimensions of (13260, 64, 64, 1). Similarly, the corresponding labels of X-ray images are of size (13260, 2) with classes (0) if the patient is healthy, (1) if patient has pre-existing conditions or (2) if patient has Effusion/Mass in the lungs. During data exploration, I found that the class labels are highly imbalanced. Thus, for handling such imbalanced class labels, I used Data augmentation techniques such as horizontal & vertical flips, rotation, altering brightness and height & width shift to increase the number of training images to prevent overfitting problem. After preprocessing the data, the dimension of the dataset is (31574, 64, 64, 1). For Model Selection, I built 4 architectures of CNN Model similar to the architecture of LeNet-5, VGGNet, AlexNet with various Conv2D layers followed by MaxPooling2D layers and fitted them with different epochs, batch size and different optimizer learning rate. Moreover, I also built a custom architecture with comparatively less complex structure than previous models. Further to avoid Overfitting, I also tried regularizing Kernel layer and Dense layer using Absolute Weight Regularizer(L1) and to restrict the bias in classification, I used Bias Regularizer in the Dense layer. In addition to this, I also tried applying Dropout with a 20% dropout rate during training and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I split the dataset into training,testing and validation split with (60,20,20) ratio and calculated Macro F1 Score , AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, the CNN model similar to VGGNet with 5 Conv2D and 3 MaxPooling layers and 2 Dense layers performed better than other architecture with Macro F1 score of 0.773 , AUC score of 0.911 and accuracy of 0.777.

multiclass-pulmonary-disease-prediction

neuralsteganography

STEGASURAS: STEGanography via Arithmetic coding and Strong neURAl modelS

opencovidgan

A free implementation of the CovidGAN project

patho-gan

Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"

pentest-scripts

Miscellaneous scripts for pentesting