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算法岗笔试面试大全，励志做算法届的《五年高考，三年模拟》！
PatchAD, deep learning, anomaly detection, outlier detection, time series, PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection
[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
MOMENT: A Family of Open Time-series Foundation Models
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"
A Library for Advanced Deep Time Series Models.
MTAD: Tools and Benchmark for Multivariate Time Series Anomaly Detection
A library for composing end-to-end tunable machine learning pipelines.
A machine learning library for detecting anomalies in signals.
About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Official repository for the paper "RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection"
🔗 Some useful websites for programmers.
12 Weeks, 24 Lessons, AI for All!
An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection
[Read-Only Mirror] Benchmarking Toolkit for Time Series Anomaly Detection Algorithms using TimeEval and GutenTAG
Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"
Time series anomaly detection algorithm implementations for TimeEval (Docker-based)
Evaluation Tool for Anomaly Detection Algorithms on Time Series
《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing
TODS: An Automated Time-series Outlier Detection System
A framework for easy running and evaluating your TSAD algorithm.
KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network