compression
guesslang
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| compression | guesslang | |
|---|---|---|
| 1 | 2 | |
| 823 | 756 | |
| 2.6% | - | |
| 6.6 | 0.0 | |
| 10 days ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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compression
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[P] I turned Stable Diffusion into a lossy image compression codec and it performs great!
Eg : https://github.com/tensorflow/compression
guesslang
- Is there a tool that could detect what language a snippet of code is written in?
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[DEV] DioHub, an open-source GitHub mobile app, is now available on the Play Store!
Yes that would be cool, alternatively a way for the language to be detected when there is no extension with something like Guesslang would be even better but that would probably require quite a bit more work I guess. I'll open an issue on the repo now, thank you for the great app!
What are some alternatives?
EmoPy - A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
onnx-tensorflow - Tensorflow Backend for ONNX
openrec - OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
serving - A flexible, high-performance serving system for machine learning models
Keras - Deep Learning for humans
deephyper - DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
AlphaPy - Python AutoML for Trading Systems and Sports Betting
tensorflow - An Open Source Machine Learning Framework for Everyone
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!