Datadrivenmodel
Build sim from data for use in reinforcement learning and bonsai platform for machine teaching.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Tensorflow_template_application | 1,869 | 10 months ago | 1 | May 20, 2021 | 14 | apache-2.0 | Python | |||
| TensorFlow template application for deep learning | ||||||||||
| Datadrivenmodel | 18 | a year ago | 13 | mit | Jupyter Notebook | |||||
| Build sim from data for use in reinforcement learning and bonsai platform for machine teaching. | ||||||||||
| Ai Dancer | 10 | 3 years ago | mit | Python | ||||||
| A Long Short Term Memory Neural Network and Style GAN based Dancer that can generate new dance steps. | ||||||||||
| Longsword Stance Restful Service | 7 | 5 years ago | Python | |||||||
| Longsword Stance RESTful Service Working demo: https://www.youtube.com/watch?v=v7hvOyPQ0EM Longsword Stance RESTful Service: Invokes prediction results with real-time multivariate time series data. Using Flask and python, the pre-trained bidirectional LSTM deep learning model is loaded to memory. RESTful post request containing real-time rows of IMU data can be used to classify the longsword movement stance. Information on the classification confidence and execution time in milliseconds is also provided. | ||||||||||
| Machinelearning_for_trading | 6 | 5 years ago | Jupyter Notebook | |||||||
| Undertale_deltarune_soundtrack_generator | 5 | 5 years ago | mit | Jupyter Notebook | ||||||
| My first big personal machine learning (specifically deep learning) project! I'm going to train neural networks to learn patterns from the Undertale OST, then make it generate original soundtrack with similar style! | ||||||||||
| Recession Prediction Lstm | 5 | 6 years ago | Jupyter Notebook | |||||||
| Tumor Cifar | 5 | 3 years ago | 1 | Python | ||||||
| Time Series Prediction | 5 | 5 years ago | mit | HTML | ||||||
| Experimentation about predicting time series with AI | ||||||||||
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