awesome-datascience
tensorflow
awesome-datascience | tensorflow | |
---|---|---|
9 | 223 | |
23,777 | 182,575 | |
3.7% | 0.5% | |
6.9 | 10.0 | |
8 days ago | 4 days ago | |
C++ | ||
MIT License | Apache License 2.0 |
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awesome-datascience
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About Data analyst, data scientist and data engineer, resources and experiences
Awesome Data Science by Academic
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Good coding groups for black women?
- https://github.com/academic/awesome-datascience
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
9. Awesome Data Science If you’re on the hunt for data science resources, Awesome Data Science is a goldmine. This curated list includes MOOCs, books, courses, blogs, podcasts, software, and more, all related to data science.
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
Awesome Data Science – The awesome lists repositories often provides a good collection of resources around a specific topic, and the awesome-datascience repository is no exception. It contains a very comprehensive list of books, moocs, tutorials, and other content for all learnes of all levels of experience.
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High income skills?
There are several on github, such as: https://github.com/academic/awesome-datascience
- ⚙️ Awesome Data Science: An #OpenSource #DataScience repository to learn and apply towards solving real world problems. h/t @Sauain
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Top GitHub repositories to learn Data Science
Awesome Data Science
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[IWantOut] 21f Peru student -> Canada/UK
If you want to expand your skills and knowledge in data science, there's a ton of free online resources out there. For example, this page is a good place to get started. There's lots of communities like /r/learndatascience or similar subs if you get stuck on something.
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?
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