| microsoft/AI-For-Beginners |
26,668 |
|
0 |
0 |
over 2 years ago |
0 |
|
64 |
mit |
Jupyter Notebook |
| 12 Weeks, 24 Lessons, AI for All! |
| zalandoresearch/fashion-mnist |
9,856 |
|
0 |
0 |
about 4 years ago |
0 |
|
24 |
mit |
Python |
| A MNIST-like fashion product database. Benchmark :point_down: |
| advimman/lama |
9,669 |
|
0 |
0 |
over 1 year ago |
0 |
|
70 |
apache-2.0 |
Jupyter Notebook |
| 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022 |
| datawhalechina/leedl-tutorial |
8,682 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
other |
Jupyter Notebook |
| 《李宏毅深度学习教程》，PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases |
| 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 |
| MorvanZhou/PyTorch-Tutorial |
7,372 |
|
0 |
0 |
about 3 years ago |
0 |
|
28 |
mit |
Jupyter Notebook |
| Build your neural network easy and fast, 莫烦Python中文教学 |
| 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 |
| 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. |
| MorvanZhou/Tensorflow-Tutorial |
3,873 |
|
0 |
0 |
over 5 years ago |
0 |
|
7 |
mit |
Python |
| Tensorflow tutorial from basic to hard |
| borisbanushev/stockpredictionai |
3,235 |
|
0 |
0 |
over 4 years ago |
0 |
|
320 |
|
|
| In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later. |
