A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.

✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】

cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，支持sso登录，多租户，大数据平台对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务VGPU，边缘计算，serverless，标注平台，自动化标注，数据集管理，大模型微调，vllm大模型推理，llmops，私有知识库，AI模型应用商店，支持模型一键开发/推理/微调，…

标注自己的数据集，训练、评估、测试、部署自己的人工智能算法

3D-printable hexagonal mirror array capable of reflecting sunlight into arbitrary patterns

Repository containing notebooks of my posts on Medium

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

PromptCLUE, 全中文任务支持零样本学习模型

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.

This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning

关注AI模型上线、模型部署

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

Bearing fault diagnosis model based on MCNN-LSTM

Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.

An intuitive library to extract features from time series. To cite this software publication: https://www.sciencedirect.com/science/article/pii/S2352711020300017

多元多步时间序列的LSTM模型预测——基于Keras

University Project for Anomaly Detection on Time Series data

Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and M…

Implementation of categorical generative adversarial networks for unsupervised bearing fault diagnostics

Analysis of CWRU Bearing Data Set and Development of WeChat Mini Program Interface

Machine Learning Based Unbalance Detection of a Rotating Shaft Using Vibration Data

1D-CNN Vibration Signal Bearing Fault Diagnosis