Aagam Shah (abs51295)

abs51295

Geek Repo

Company:@KnottLab

Location:Los Angeles, CA

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Aagam Shah's repositories

f8a-hpf-insights

HPF Matrix Factorization for Maven companion recommendation

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f8a-pypi-insights

This repository contains the code for the model that serves the companion recommendations for Python

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openshift-probable-vulnerabilities

This repository contains all the code, models, scripts and reports for flagging probable vulnerabilities for the gokube-openshift eco-system

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bayessets

Implementation of the Bayesian Sets algorithm and variants

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CV_Resources

Resources for building CVs

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depcheck

Brain dead tool for analyzing dependencies in OpenShift 4.0 component ecosystem

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f8a-emr-deployment

This repository contains code for emr deployment API

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f8a-stacks-report

Source code for the cronjob that runs stack analyses aggregation report periodically

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fabric8-analytics-release-monitor

Service for monitoring of latest updates to upstream packages

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fabric8-analytics-scaler

Service that scales workers based on SQS queue size

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fabric8-analytics-server

fabric8-analytics API server

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fabric8-wit

wit stands for Work Item Tracker

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ghtorrent.org

The GHtorrent project website

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Hamiltonian-Networks

Repo to store code for experiments with Hamiltonian Neural networks

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hellovertx

Generated by the Red Hat Developer Launch (https://developers.redhat.com/launch)

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hpfrec

Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.

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maven-simple

Example Maven project demonstrating the use of

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mnist-vanilla-gans

This repository contains GAN tutorials for MNIST data set

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nodejs-health-check-redhat

Booster demonstrating health checks and recovery

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orthologsBioMART

One-liner for mapping orthologous genes in both R and python using ENSEMBL BioMART

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pip

The Python package installer

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poc-go-cves

This is a proof-of-concept to flag potential security vulnerabilities based on commits, issues and PRs.

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probable-cves

Probable CVEs

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scanpy

Single-Cell Analysis in Python. Scales to >1M cells.

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scGAN

Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

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tensorwatch

Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

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