bottydim

adversarial_explanations

You shouldn't trust me: Learning models which conceal unfairness from multiple explanation methods

keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.

actoggltimeimport

Send time from Toggl to ActiveCollab with ease.

ads8-neuralnets

ADS Course Neural Network Module

Audible

A(Sync) Interface for internal Audible API written in pure Python.

azure-acs-mongodb-demo

Demo project showing how to deploy MongoDB on the Azure Container Services (ACS) with Kubernetes

detect-credit-card-fraud

This repository contains working code of a Credi Card Fraud Detection Project

keras-vis

Neural network visualization toolkit for keras

RNNVis

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

SentimentAnalysis

Sentiment Analysis Project

constructed-datasets

Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"

cs231n.github.io

Public facing notes page

DataProcessingModule

Small project for hackaton

dginn

This repository contains the code related to Chapter 4 of Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

django-booking

A reusable Django app that manages bookings for various purposes.

django-rest-angular-vagrant

Template for django rest application with Anjular on a virutal machine provissioned by vagrant

ImageNet_Utils

:arrow_double_down: Utils to help download images by id, crop bounding box, label images, etc.

keras-event-monitor

This is a nodejs app to display keras events

keras-xai

malmo-challenge

Task and example code for the Malmo Collaborative AI Challenge

NetDissect-Lite

Light version of Network Dissection for Quantifying Interpretability of Networks

node-js-getting-started

Getting Started with Node on Heroku

razer-macos

Color effects manager for Razer devices for macOS. Supports High Sierra (10.13) to Monterey (12.0). Made by the community, based on openrazer.

scikit-learn

scikit-learn: machine learning in Python

shap

A unified approach to explain the output of any machine learning model.

tensorflow

Computation using data flow graphs for scalable machine learning