alphafold
tensorflow
alphafold | tensorflow | |
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35 | 223 | |
11,683 | 182,456 | |
1.0% | 0.5% | |
6.4 | 10.0 | |
9 days ago | 6 days ago | |
Python | C++ | |
Apache License 2.0 | Apache License 2.0 |
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alphafold
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What is a recent scientific discovery that you find exciting?
For all you programmer types, these are the repos for each of them. AlphaFold - ProGen - ProtGPT2
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RFdiffusion: Diffusion model generates protein backbones
2. https://github.com/deepmind/alphafold
- GitHub - deepmind/alphafold: Open source code for AlphaFold.
- New Code Update to AlphaFold 2.0 (links in comments)
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What happened in tech in 2022
b. But seriously, AlphaFold2 dramatically improved the ability to predict protein structures. If you want to read a lot more about it go here or check out the code at https://github.com/deepmind/alphafold
- New Update To AlphaFold 2 (links in comments)
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The anti-vaccine movement and pro-Russian propaganda in social media – a summary report. Since the beginning of the war in Ukraine, there has been a trend in which anti-vaccine accounts have been changing their narrative towards supporting Kremlin propaganda.
If you’re asking about the literal actual sequence of mRNA in the various vaccines, that’s available online, and you can check that it makes the correct shape using a tool like AlphaFold
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Stability AI backs effort to bring machine learning to biomed
Their code/weights/everything.
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Nvida tool kit?
looks like the container uses GPUs for compute https://github.com/deepmind/alphafold
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AlphaFold reveals the structure of the protein universe
> but other famous labs have already moved to ML predictions and are competitive with DeepMind now.
it seems obvious this was going to happen, because https://github.com/deepmind/alphafold
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?
RoseTTAFold - This package contains deep learning models and related scripts for RoseTTAFold
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)
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Visual Studio Code - Visual Studio Code
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
RFdiffusion - Code for running RFdiffusion
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.
online-go.com - Source code for the Online-Go.com web interface
scikit-learn - scikit-learn: machine learning in Python
bypass-paywalls-chrome - Bypass Paywalls web browser extension for Chrome and Firefox.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.