Fast-Transformer
machine-learning-experiments
| Fast-Transformer | machine-learning-experiments | |
|---|---|---|
| 4 | 8 | |
| 146 | 1,605 | |
| - | - | |
| 0.0 | 2.6 | |
| about 2 years ago | 4 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Fast-Transformer
machine-learning-experiments
What are some alternatives?
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
Torrent-To-Google-Drive-Downloader-v3 - Simple notebook to stream torrent files to Google Drive using Google Colab and python3.
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
osumapper - An automatic beatmap generator using Tensorflow / Deep Learning.
Conformer - An implementation of Conformer: Convolution-augmented Transformer for Speech Recognition, a Transformer Variant in TensorFlow/Keras
lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
TimeSformer-pytorch - Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification
PConv-Keras - Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai
Transformer-in-Transformer - An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches
lucid - A collection of infrastructure and tools for research in neural network interpretability.
embedding-encoder - Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
Hands-On-Meta-Learning-With-Python - Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow