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VIP cheatsheets for Stanford's CS 229 Machine Learning course
A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
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.
A comprehensive collection of resources for anomaly detection, including books, papers, videos, and toolboxes.
Solutions and notes for the Machine Learning Specialization by Andrew NG on Coursera.
An open-source anomaly detection library with state-of-the-art algorithms and features like experiment management and edge inference.
A Python library of extension and helper modules for data analysis and machine learning.
A self-supervised learning framework for computer vision tasks with a focus on contrastive learning.
Detects anomalies and drift in data with algorithms for outlier, adversarial, and concept-drift detection.
PyTorch implementation of SimCLR, a simple framework for contrastive learning of visual representations.
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
A curated list of community detection research papers with implementations for data science and network analysis.
A curated collection of free AI resources including courses, jobs, blogs, and research for developers.
A curated list of resources for transfer learning and domain adaptation research and applications.
A minimalistic implementation of the Self Organizing Maps (SOM) algorithm for clustering and dimensionality reduction.
Unsupervised image classification library using contrastive learning and SCAN algorithm.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
A Python library for unsupervised time series anomaly detection, useful for data science and ML applications.
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