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共 1659 个源码项目awesome-adversarial-machine-learning
A curated list of awesome adversarial machine learning resources
DemoGPT
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
benchm-ml
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
brat
brat rapid annotation tool (brat) - for all your textual annotation needs
ChatYuan
ChatYuan: Large Language Model for Dialogue in Chinese and English
TradingGym
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
deepgaze
Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.
PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
aitextgen
A robust Python tool for text-based AI training and generation using GPT-2.
gplearn
Genetic Programming in Python, with a scikit-learn inspired API
ceval
Official github repo for C-Eval, a Chinese evaluation suite for foundation models [NeurIPS 2023]
claudia-bot-builder
Create chat bots for Facebook Messenger, Slack, Amazon Alexa, Skype, Telegram, Viber, Line, GroupMe, Kik and Twilio and deploy to AWS Lambda in minutes
DeepMind-Atari-Deep-Q-Learner
The original code from the DeepMind article + my tweaks
DataFrames.jl
In-memory tabular data in Julia
Bender
Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
Awesome-Deep-Learning-Resources
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
Metaworld
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
oryx
Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
kiba
Data processing & ETL framework for Ruby
twitterbio
Generate your Twitter bio with AI
sharegpt
Easily share permanent links to ChatGPT conversations with your friends
MemNN
Memory Networks implementations
lightning-bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers.
NeuroNER
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
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