Deepgpy
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Gpflow | 1,783 |  | 17 | 19 | 3 months ago | 39 | August 09, 2023 | 149 | apache-2.0 | Python |
| Gaussian processes in TensorFlow | ||||||||||
| Agrobot | 32 | 10 months ago | bsd-3-clause | Jupyter Notebook | ||||||
| Neural-Kalman GNSS/INS Navigation for Precision Agriculture | ||||||||||
| Deepgpy | 29 | 8 years ago | 5 | bsd-3-clause | Jupyter Notebook | |||||
| Deep GPs with GPy | ||||||||||
| Intelligent Vehicle Perception Based On Inertial Sensing And Artificial Intelligence | 18 | 3 years ago | n,ull | other | ||||||
| Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence | ||||||||||
| Deep Semi Supervised Gps Transport Mode | 11 | 4 years ago | 1 | Python | ||||||
| Metadata Digger | 9 | 4 years ago | 1 | apache-2.0 | Scala | |||||
| Big Data tool for metadata extraction (Exif), enrichment (using DeepLearning) and analysis | ||||||||||
| Savesession Arkit Coreml | 7 | 2 years ago | apache-2.0 | Swift | ||||||
| A project to show the possibility to save and load session in ARkit using CoreML, the end goal is to make a guided tours app | ||||||||||
| Deeppredtec | 6 | 6 years ago | mit | Python | ||||||
| Deep Learning on Predicting GPS TEC Maps | ||||||||||
| Trackkr | 6 | 11 years ago | 7 | JavaScript | ||||||
| django based service for locating gps units | ||||||||||
| Doubly Stochastic Deep Gaussian Process | 5 | 4 years ago | 1 | apache-2.0 | Python | |||||
| Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression. | ||||||||||
Alternatives To DeepgpySelect To Compare
Alternative Project Comparisons
Popular Gps Projects
Popular Deep Learning Projects
Popular Hardware Categories
Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Jupyter Notebook
Deep Learning
Operating System
Clang
Gps
Openmp