Popular GitHub Repositories for Case Studies

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. ?25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. ? Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

CNN-based audio segmentation toolkit. Allows to detect speech, music, noise and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.

A collection of awesome text-to-image generation studies.

Awesome Projects in Python - Machine Learning Applications, Games, Desktop Applications all in Python :snake:

RASA chatbot use case boilerplate

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"

LangFair is a Python library for conducting use-case level LLM bias and fairness assessments

An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case.

Implementation of different summarization algorithms applied to legal case judgements.

WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

Machine reading comprehension on clinical case reports

Implementation of BTree part for paper 'The Case for Learned Index Structures'

Explore this repository for GenAI samples on SAP Business Technology Platform (SAP BTP). We provide examples for single and multitenant versions, showcasing integration of LLMs via SAP AI Core, LangChain in SAP CAP, and advanced techniques like Retrieval Augmented Generation (RAG).

Automatic driving long tail / corner cases scenarios dataset (Anomaly detection)

A Python Series of tutorials aimed at learning Artificial Intelligence concepts. This series of tutorials start from the basics of Python and builds on top of it. We will cover three full-fledged case studies to practice AI Implementation of Python with real data and solve real-world problems.