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Official Implement of "ADGym: Design Choices for Deep Anomaly Detection", NeurIPS 2023
Interact with your documents using the power of GPT, 100% privately, no data leaks
A playbook for systematically maximizing the performance of deep learning models.
A curated list of awesome Machine Learning frameworks, libraries and software.
Investment Research for Everyone, Everywhere.
Realtek RTL8811CU/RTL8821CU USB Wi-Fi adapter driver for Linux
list of papers, code, and other resources
😎 Awesome lists about all kinds of interesting topics
This repository contains the solutions and explanations to the algorithm problems on LeetCode. Only medium or above are included. All are written in C++/Python and implemented by myself. The proble…
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Curated list of awesome GAN applications and demo
DeepLab v3+ model in PyTorch. Support different backbones.
U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI
An attempt to implement 'DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series'
Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based) settings for anaomaly detection in time series data
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explana…
Anomaly detection on time series using Deep Learning techniques
A game theoretic approach to explain the output of any machine learning model.
📖 A collection of pure bash alternatives to external processes.
A complete computer science study plan to become a software engineer.
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
Open source release of the evaluation benchmark suite described in "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"
Automatic extraction of relevant features from time series:
Lime: Explaining the predictions of any machine learning classifier