100-Days-Of-ML-Code中文版
VIP cheatsheets for Stanford's CS 229 Machine Learning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Anomaly detection related books, papers, videos, and toolboxes
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
A library of extension and helper modules for Python's data analysis and machine learning libraries.
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
Unsupervised Learning for Image Registration
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
Algorithms for outlier, adversarial and drift detection
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
A curated list of community detection research papers with implementations.
A curated list of pretrained sentence and word embedding models
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
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