| shenweichen/Bytedance_ICME2019_challenge_baseline |
136 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
mit |
Python |
| Bytedance_ICME2019_challenge_baseline |
| mratsim/McKinsey-SmartCities-Traffic-Prediction |
38 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge |
| ShenDezhou/icme2019 |
34 |
|
0 |
0 |
about 6 years ago |
0 |
|
2 |
mit |
Python |
| 短视频内容理解与推荐竞赛 |
| alyakhtar/AQI-Delhi |
15 |
|
0 |
0 |
almost 10 years ago |
0 |
|
0 |
|
Python |
| Predicting air pollution level in a specific city |
| notwaldorf/tensorflow-experiments |
14 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
|
JavaScript |
| kalgishah02/SnapLoc |
12 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| SnapLoc is a product that does automatic image classification and spatio-temporal analysis in order to recommend the places of interest in a new city. The packages that I have used for creating the product are Python(Pandas, NumPy, Shapely, Keras, Leaflet) and TensorFlow |
| jimmyshi360/Charm-City-Murals |
6 |
|
0 |
0 |
over 5 years ago |
0 |
|
4 |
mit |
JavaScript |
| AR and ML powered Web App for Baltimore mural visualization. (TensorFlow, Flask, Python, HTML, CSS, Bootstrap, JavaScript) |
| rayidghani/CharBucks |
5 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
mit |
Python |
| Takes an image, directory of images, or yelp business id and classifies them into whether they have latte art or not |
| ZackAkil/edge-TPU-safe-bike |
5 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
|
Python |
| An application of realtime object-detection running on an Edge TPU for making cycling in busy cities a little less terrifying. |
