| VivekPa/AIAlpha |
1,547 |
|
0 |
0 |
almost 6 years ago |
0 |
|
8 |
mit |
Python |
| Use unsupervised and supervised learning to predict stocks |
| xiaohu2015/DeepLearning_tutorials |
1,216 |
|
0 |
0 |
about 7 years ago |
0 |
|
7 |
|
Jupyter Notebook |
| The deeplearning algorithms implemented by tensorflow |
| wagamamaz/tensorflow-tutorial |
751 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
|
| TensorFlow and Deep Learning Tutorials |
| shubhomoydas/ad_examples |
738 |
|
0 |
0 |
about 4 years ago |
0 |
|
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. |
| jsn5/dancenet |
453 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
mit |
Python |
| DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras) |
| JulesBelveze/time-series-autoencoder |
386 |
|
0 |
0 |
almost 3 years ago |
0 |
|
5 |
apache-2.0 |
Python |
| :chart_with_upwards_trend: PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series :chart_with_upwards_trend: |
| shobrook/sequitur |
370 |
|
0 |
0 |
over 2 years ago |
9 |
January 28, 2021 |
8 |
mit |
Python |
| Library of autoencoders for sequential data |
| buomsoo-kim/Easy-deep-learning-with-Keras |
336 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Keras tutorial for beginners (using TF backend) |
| KerasKorea/KEKOxTutorial |
289 |
|
0 |
0 |
over 2 years ago |
0 |
|
32 |
|
Jupyter Notebook |
| 전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다. |
| accel-brain/accel-brain-code |
289 |
|
0 |
2 |
over 2 years 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. |
