every-programmer-should-know
tensorflow
| every-programmer-should-know | tensorflow | |
|---|---|---|
| 20 | 223 | |
| 76,847 | 182,575 | |
| 0.9% | 0.5% | |
| 0.0 | 10.0 | |
| 8 months ago | 3 days ago | |
| C++ | ||
| Creative Commons Attribution 4.0 | 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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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.
every-programmer-should-know
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10 GitHub repositories that every developer must follow
✅ mtdvio/every-programmer-should-know : https://github.com/mtdvio/every-programmer-should-know
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Quiero estudiar programación de manera autodidacta, por dónde debería empezar? Algun consejo?
Repositorios: https://github.com/practical-tutorials/project-based-learning https://github.com/mtdvio/every-programmer-should-know Desarrollo web: fullstackopen.com theodinproject.com YouTube ````
- Google, Microsoft ChatGPT Clones Will Destroy Internet Search
- What are a few things every developer should know?
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5 GitHub Repositories every Developer should know
5. Every Programmer Should Know
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Open Source Repositories
Every Programmer Should Know. A collection of (mostly) technical things every software developer should know. Backed by a community of developers, of course.
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Primo lavoro IT, dopo laurea
Ci sono anche un po' di repo su GitHub che possono aiutare: https://github.com/arialdomartini/Back-End-Developer-Interview-Questions https://github.com/mtdvio/every-programmer-should-know https://github.com/schmatz/cs-interview-guide
- Which difficulties have you noticed the most with Juniors dev ?
- How do you master the fundamentals
- These GitHub repositories contain so much knowledge you can use to become a better developer.
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?
build-your-own-x - Master programming by recreating your favorite technologies from scratch.
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）
garlic-os-themes - This repo is intended to collect themes I've made for Garlic OS; a custom firmware for the Anbernic RG35XX which was made by Black-Seraph.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Best-websites-a-programmer-should-visit - :link: Some useful websites for programmers.
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
Front-end-Developer-Interview-Questions - A list of helpful front-end related questions you can use to interview potential candidates, test yourself or completely ignore.
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.
professional-programming - A collection of learning resources for curious software engineers
scikit-learn - scikit-learn: machine learning in Python
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.