Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Aialpha | 1,618 | 4 years ago | 8 | mit | Python | |||||
Use unsupervised and supervised learning to predict stocks | ||||||||||
Deeplearning_tutorials | 1,216 | 5 years ago | 7 | Jupyter Notebook | ||||||
The deeplearning algorithms implemented by tensorflow | ||||||||||
Tensorflow Tutorial | 751 | 6 years ago | ||||||||
TensorFlow and Deep Learning Tutorials | ||||||||||
Ad_examples | 738 | 2 years ago | 2 | mit | Python | |||||
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network. | ||||||||||
Dancenet | 453 | 5 years ago | mit | Python | ||||||
DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras) | ||||||||||
Time Series Autoencoder | 386 | a year ago | 5 | apache-2.0 | Python | |||||
:chart_with_upwards_trend: PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series :chart_with_upwards_trend: | ||||||||||
Sequitur | 370 | 6 months ago | 9 | January 28, 2021 | 8 | mit | Python | |||
Library of autoencoders for sequential data | ||||||||||
Easy Deep Learning With Keras | 336 | 3 years ago | Jupyter Notebook | |||||||
Keras tutorial for beginners (using TF backend) | ||||||||||
Kekoxtutorial | 289 | 8 months ago | 32 | Jupyter Notebook | ||||||
전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다. | ||||||||||
Accel Brain Code | 289 | 2 | 4 months ago | 12 | July 26, 2022 | 1 | gpl-2.0 | Python | ||
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing. |