100-Days-Of-ML-Code中文版
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Updated
Apr 6, 2022 - Jupyter Notebook
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
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Anomaly detection related books, papers, videos, and toolboxes
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
A library of extension and helper modules for Python's data analysis and machine learning libraries.
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Unsupervised Learning for Image Registration
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
A curated list of community detection research papers with implementations.
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
Implementing machine learning algorithms from scratch.
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