| apachecn/ailearning |
37,352 |
|
0 |
2 |
over 2 years ago |
8 |
March 20, 2022 |
1 |
other |
Python |
| AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2 |
| JaidedAI/EasyOCR |
20,438 |
|
0 |
69 |
over 2 years ago |
32 |
September 04, 2023 |
340 |
apache-2.0 |
Python |
| Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. |
| emilwallner/Screenshot-to-code |
14,132 |
|
0 |
0 |
about 4 years ago |
0 |
|
17 |
other |
HTML |
| A neural network that transforms a design mock-up into a static website. |
| BlinkDL/RWKV-LM |
10,705 |
|
0 |
0 |
over 2 years ago |
0 |
|
60 |
apache-2.0 |
Python |
| RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding. |
| Mikoto10032/DeepLearning |
7,463 |
|
0 |
0 |
about 4 years ago |
0 |
|
8 |
apache-2.0 |
Jupyter Notebook |
| 深度学习入门教程, 优秀文章, Deep Learning Tutorial |
| huseinzol05/Stock-Prediction-Models |
6,233 |
|
0 |
0 |
about 3 years ago |
0 |
|
46 |
apache-2.0 |
Jupyter Notebook |
| Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations |
| ml5js/ml5-library |
6,134 |
|
0 |
2 |
almost 3 years ago |
4 |
June 14, 2018 |
267 |
other |
JavaScript |
| Friendly machine learning for the web! 🤖 |
| kjw0612/awesome-rnn |
5,856 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
|
|
| Recurrent Neural Network - A curated list of resources dedicated to RNN |
| fchollet/keras-resources |
3,174 |
|
0 |
0 |
over 3 years ago |
0 |
|
13 |
|
|
| Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library |
| guillaume-chevalier/LSTM-Human-Activity-Recognition |
3,074 |
|
0 |
0 |
over 3 years ago |
0 |
|
19 |
mit |
Jupyter Notebook |
| Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier |
