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adversarial-robustness-toolbox

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

agents

An Open-source Framework for Autonomous Language Agents

ai-data-workshop

Workshop for Data and AI Sessions

ai-governance

ai-governance-factsheet-samples

AI Governance factsheet sample assets, notebooks and apps.

ai-ml-apps

This is a repo to test publishing AI/ML apps!

ai-openscale-tutorials

Watson OpenScale tutorials including sample models, notebooks and applications

ai_application_microservice_with_docker

aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

aix360

Interpretability and explainability of data and machine learning models

alibi-detect

Algorithms for outlier, adversarial and drift detection

assistant-improve-recommendations-notebook

Assistant Improve notebooks for Watson Assistant

assistant-toolkit

Toolkit for experimentation with Watson Assistant

auto_ml

[UNMAINTAINED] Automated machine learning for analytics & production

autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

aws-demos

A hands on repo with multiple demonstrations on AWS 🎓

caffe_project

churn-prediction

Deploying a Churn prediction Model as an API on Red Hat OpenShift Container Platform

clothing-co-parsing

CCP dataset from "Clothing Co-Parsing by Joint Image Segmentation and Labeling " (CVPR 2014)

cloud-pak-deployer

Configuration-based installation of OpenShift and Cloud Pak for Data/Integration on various private and public cloud infrastructure providers. Deployment attempts to achieve the end-state defined in the configuration. If something fails along the way, you only need to restart the process to continue the route.

cloud-pak-for-data-tutorial

cloudpakfordata-telco-churn-workshop

This repo contains a workshop for Cloud Pak for Data.

cogvideo

Text-to-video generation.

comparing-corpora

A python library of similarity measures which allow measuring the perceptual similarity between set embeddings corpora.

connect-your-machine-learning-models-to-chatbot-and-whatsapp

Modernize your Machine Learning Workflow with Low Code Tools & Quickly Integrate a Machine Learning Model in your Chatbot & Deploy to Whatsapp

conversational-ai-chatbot

Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users!

conversational-retrieval-agent

cp4d-install

Installation help docs for Cloud Pak for Data on IBM Cloud

cpdemoframework

This Repo will have all the scripts required for the CP Demo Framework.

credit-fraud-detection

Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.