| matterport/Mask_RCNN |
23,745 |
|
0 |
0 |
over 2 years ago |
5 |
March 05, 2019 |
1,993 |
other |
Python |
| Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow |
| emilwallner/Screenshot-to-code |
14,132 |
|
0 |
0 |
almost 4 years ago |
0 |
|
17 |
other |
HTML |
| A neural network that transforms a design mock-up into a static website. |
| tangyudi/Ai-Learn |
7,757 |
|
0 |
0 |
over 2 years ago |
0 |
|
20 |
|
|
| 人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域 |
| nl8590687/ASRT_SpeechRecognition |
7,253 |
|
0 |
0 |
about 2 years ago |
1 |
October 23, 2020 |
101 |
gpl-3.0 |
Python |
| A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统 |
| czy36mengfei/tensorflow2_tutorials_chinese |
6,541 |
|
0 |
0 |
over 5 years ago |
0 |
|
15 |
|
Jupyter Notebook |
| tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials |
| jeffheaton/t81_558_deep_learning |
5,590 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
other |
Jupyter Notebook |
| T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis |
| wepe/MachineLearning |
4,895 |
|
0 |
0 |
about 2 years ago |
0 |
|
39 |
|
Python |
| Basic Machine Learning and Deep Learning |
| idealo/image-super-resolution |
4,392 |
|
0 |
0 |
over 2 years ago |
0 |
|
106 |
apache-2.0 |
Python |
| 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks. |
| zhixuhao/unet |
4,218 |
|
0 |
0 |
almost 3 years ago |
0 |
|
203 |
mit |
Jupyter Notebook |
| unet for image segmentation |
| TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials |
3,436 |
|
0 |
0 |
almost 3 years ago |
0 |
|
152 |
other |
Python |
| A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more. |
