머신러닝 · GitHub

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. 🏆 Best Paper Awards @ NeurIPS ML Safety Workshop 2022

AI-powered weather intelligence for agricultural commodity futures. Predicts price moves 48-72 hours early across OJ, Coffee, Cocoa, Sugar, Corn and Wheat using Claude Sonnet + satellite data

PyTorch implementation of Real-ESRGAN model

vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers

Recent research papers about Foundation Models for Combinatorial Optimization

Cloud-native Financial Reinforcement Learning

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

[ICLR'25] Official PyTorch implementation of "Framer: Interactive Frame Interpolation".

An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

• --loadcheck option now can load the arguments saved from the checkpoint, so you can resume the training from the last checkpoint by just running python main.py --loadcheck <checkpoint_name>.
• The training now captures the SIGINT signal (Ctrl+C) to gracefully stop the training process and save the current state. The checkpoint is saved in checkpoints/paused/ directory. This can be disabled by setting --save_after_interrupt=0 in the command line.

[CVPR 2020] Meshed-Memory Transformer for Image Captioning

🎯 AI Zero to Hero 👑

PyTorch code for EMNLP 2019 paper "LXMERT: Learning Cross-Modality Encoder Representations from Transformers".

A DSL for deep neural networks, supporting Caffe and Torch

Unofficial PyTorch implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation"

T2F: text to face generation using Deep Learning