| curiousily/Getting-Things-Done-with-Pytorch |
873 |
|
0 |
0 |
almost 5 years ago |
0 |
|
13 |
apache-2.0 |
Jupyter Notebook |
| Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT. |
| qingsongedu/awesome-AI-for-time-series-papers |
627 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
|
| A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals. |
| john-science/scipy_con_2019 |
266 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Tutorial Sessions for SciPy Con 2019 |
| IAMconsortium/pyam |
201 |
|
1 |
15 |
over 2 years ago |
29 |
December 15, 2023 |
83 |
apache-2.0 |
Python |
| Analysis & visualization of energy & climate scenarios |
| DataForScience/Timeseries |
195 |
|
0 |
0 |
about 3 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| Timeseries for everyone |
| niekverw/Deep-Learning-Based-ECG-Annotator |
92 |
|
0 |
0 |
over 6 years ago |
0 |
|
4 |
|
Python |
| Annotation of ECG signals using deep learning, tensorflow’ Keras |
| sktime/sktime-tutorial-pydata-amsterdam-2020 |
91 |
|
0 |
0 |
over 4 years ago |
0 |
|
0 |
bsd-3-clause |
Jupyter Notebook |
| Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020 |
| royalosyin/Python-Practical-Application-on-Climate-Variability-Studies |
75 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python. |
| OpenXAIProject/Tutorials |
51 |
|
0 |
0 |
over 6 years ago |
0 |
|
1 |
apache-2.0 |
Jupyter Notebook |
| tutorials of XAI project |
| lordgrilo/AML-days-TDA-tutorial |
20 |
|
0 |
0 |
about 7 years ago |
0 |
|
0 |
|
Jupyter Notebook |
