Stars
remote sensing image classification and image caption by PyTorch
Classification in Remote Sensing Optical Images by CNNs
🌋 基于CNN的海贼王人物图像多分类,包含数据集爬虫,数据集处理,模型保存,图表输出,批量测试等,通用模型模板
Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
A Python module implementing some standard algorithms used in nonlinear time series analysis
Evaluating the performance of STEP with WaveNet and Graph WaveNet architectures on multivariate time series forecasting
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Sequence modeling benchmarks and temporal convolutional networks
Anomaly detection tutorial on univariate time series with an auto-encoder
Python implementation of Stacked Denoising Autoencoders for unsupervised learning of high level feature representation
A CNN + Auto-Encoder Approach for Predicting Financial Time-Series
Machine Learning in Action学习笔记,一个文件夹代表一个算法,每个文件夹包含算法所需的数据集、源码和图片,图片放在pic文件夹中,数据集放在在Data文件夹内。书中的代码是python2的,有不少错误,这里代码是我用python3写的,且都能直接运行
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)
Bi-directional Online Transfer Learning (BOTL) framework and data generators for concept drifting data streams.
Implementation of an online sequential extreme learning machine with kernels for nonstationary time series prediction.
Repository containing notebooks of my posts on Medium
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link
microsoftarchive / redis
Forked from redis/redisRedis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!