Cycle.js
tensorflow
Cycle.js | tensorflow | |
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11 | 223 | |
10,235 | 182,693 | |
-0.0% | 0.6% | |
4.1 | 10.0 | |
6 months ago | 3 days ago | |
TypeScript | C++ | |
MIT License | Apache License 2.0 |
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Cycle.js
- Could angular possibly compile rxjs Ahead Of Time?
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Can be the future of JSX be Functional first?
Seems like you might be interested in this
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Front-end Guide
Cycle
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[AskJS] Opinions In Favor of Coding Document Fragments in JS?
This is the standard way of going about things in Mithril and Cycle. Elm as well doesn't use an XML knockoff for view code- and as a fun fact, the original version of React didn't either.
- What is a really cool thing you would want to write in Rust but don't have enough time, energy or bravery for?
- Solid.js feels like what I always wanted React to be
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callbag-rs: An implementation of the callbag spec
For example, an FRP framework (created by the same author who later wrote the callbag spec): https://cycle.js.org/
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Does it make sense to use Scala.js/Laminar in the context of a startup?
TypeScript is relatively mainstream at this point, and I think that's good news. If you want to crank the type-safety and pure FP dials on it to 11, you certainly can do that. I have a project that I've based largely on this post, including the "hardcore" section. However, instead of Redux and otherwise plain React, I've chosen to use Cycle.js and the lessons from this post to use React in a very purely Functional Reactive Programming Way.
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Flame: A PureScript front-end framework inspired by the Elm architecture
This post links to a PureScript project that is probably the easiest PS framework around.
ReScript + rescript-react is a good alternative. Less safe, waaaay more verbose; but backed by Facebook.
This is quite cute (in TypeScript though): https://github.com/cyclejs/cyclejs
And Yew is super cool, it goes the WASM route (in Rust): https://github.com/yewstack/yew
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My Open Source Journey
From now on I was on what I would call a typical open source trajectory. I used the Cycle.js framework to rewrite my frontend and in that process I hit some walls. I eventually figured that the error was on my side and that I was just missing some information to avoid the error. To spare others the hours of debugging I started to contribute small patches to the documentation. At the same time I also found some missing features that I voiced in GitHub issues.
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?
RxJS - A reactive programming library for JavaScript
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)
MobX - Simple, scalable state management.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Bacon - Functional reactive programming library for TypeScript and JavaScript
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
Most.js - Ultra-high performance reactive programming
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.
Cycle.js (react-native) - Cycle.js driver that uses React Native to render
scikit-learn - scikit-learn: machine learning in Python
Elm - Compiler for Elm, a functional language for reliable webapps.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.