Anomaly detection related books, papers, videos, and toolboxes

A high performance and generic framework for distributed DNN training

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

An open-source framework for machine learning and other computations on decentralized data.

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

Implementation of the node2vec algorithm.

An autoML framework & toolkit for machine learning on graphs.

This is an official implementation for "Self-Supervised Learning with Swin Transformers".

DevOps-Models is a series of industrial-first LLMs for theDevOps domain. Asking it for any question in the DevOps domain to get solution!

Tensorflow-based CNN+LSTM trained with CTC-loss for OCR

Calculates various features from time series data. Python implementation of the R package tsfeatures.

A Python package for time series augmentation

Anomaly detection library based on singular spectrum transformation(sst)

SR-CNN

KDD 2021: Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding

A set of tutorials to implement the Federated Averaging algorithm on TensorFlow.

Hawkes Process Estimation

few shot learning (MAML) for time series prediction