Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Ai For Beginners | 30,191 | 3 months ago | 64 | mit | Jupyter Notebook | |||||
12 Weeks, 24 Lessons, AI for All! | ||||||||||
Fashion Mnist | 9,856 | 2 years ago | 24 | mit | Python | |||||
A MNIST-like fashion product database. Benchmark :point_down: | ||||||||||
Leedl Tutorial | 8,682 | 5 months ago | 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 | ||||||||||
Deeplearning | 7,463 | 2 years ago | 8 | apache-2.0 | Jupyter Notebook | |||||
深度学习入门教程, 优秀文章, Deep Learning Tutorial | ||||||||||
Pytorch Tutorial | 7,372 | a year ago | 28 | mit | Jupyter Notebook | |||||
Build your neural network easy and fast, 莫烦Python中文教学 | ||||||||||
Lama | 7,098 | 5 months ago | 70 | apache-2.0 | Jupyter Notebook | |||||
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022 | ||||||||||
T81_558_deep_learning | 5,590 | 7 months ago | 3 | other | Jupyter Notebook | |||||
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis | ||||||||||
Image Super Resolution | 4,392 | 6 months ago | 106 | apache-2.0 | Python | |||||
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks. | ||||||||||
Tensorflow Tutorial | 3,873 | 4 years ago | 7 | mit | Python | |||||
Tensorflow tutorial from basic to hard | ||||||||||
Stockpredictionai | 3,235 | 2 years ago | 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. |