gotch
tensorflow
gotch | tensorflow | |
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3 | 223 | |
507 | 182,575 | |
- | 0.5% | |
7.5 | 10.0 | |
3 months ago | 3 days ago | |
Go | C++ | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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gotch
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Why can't Go be popular for machine learning?
CGO? Too much overhead in calling C functions (in which you can wrap libtorch or TF C++ code). And too much struggling woth CUDA (actually all GPU stuff). But, there are interesting attempts: https://github.com/gorgonia/gorgonia (I love it most), https://github.com/sugarme/gotch (bindings to libtorch), https://github.com/nlpodyssey/spago.
- Go+: designed for data science
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Tensorflow
- libtorch: https://github.com/sugarme/gotch both training and inference are supported
tensorflow
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
What are some alternatives?
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
Buffalo - Rapid Web Development w/ Go
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
kratos - Your ultimate Go microservices framework for the cloud-native era.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
spaGO - Self-contained Machine Learning and Natural Language Processing library in Go
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Iris - The fastest HTTP/2 Go Web Framework. New, modern and easy to learn. Fast development with Code you control. Unbeatable cost-performance ratio :rocket:
scikit-learn - scikit-learn: machine learning in Python
Fiber - ⚡️ Express inspired web framework written in Go
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.