| jonkrohn/ML-foundations |
2,224 |
|
0 |
0 |
almost 3 years ago |
0 |
|
1 |
mit |
Jupyter Notebook |
| Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science |
| AutoAILab/FusionDepth |
57 |
|
0 |
0 |
over 3 years ago |
0 |
|
4 |
mit |
Python |
| Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR" |
| dayyass/dayyass |
18 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
|
|
| alen-smajic/Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning |
17 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset. |
| chhzh123/ToolsSeminar-CS |
16 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
|
TeX |
| Seminar on selected tools in Computer Science |
| alen-smajic/Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment |
15 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
mit |
C# |
| This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach. |
| inyong37/Study |
10 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Study about Artificial Intelligence :robot:, Machine Learning :rocket:, and Deep Learning :dna:. Plus, Electrical Engineering :zap: and Computer Science :computer:. |
| kannanjayachandran/Full-Stack-Data-Science |
7 |
|
0 |
0 |
about 2 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Full stack Data science : How to become a data scientist |
| QQBrowserVideoSearch/CBVS-UniCLIP |
5 |
|
0 |
0 |
about 2 years ago |
0 |
|
0 |
|
Python |
| A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios |
