magrittr
tensorflow
magrittr | tensorflow | |
---|---|---|
10 | 223 | |
951 | 182,575 | |
0.0% | 0.5% | |
2.3 | 10.0 | |
about 1 year ago | 1 day ago | |
R | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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magrittr
- This is not a pipe - René Magritte
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Six programming languages I’d like to see
R (yes, the statistics language) has exactly this.
You can literally extract the body of a function as a list of "call" objects (which are themselves just dressed-up lists of symbols), inject/delete/modify individual statements, and then re-cast your new list to a new function object.
I don't know why the original devs thought this was necessary or even desirable in a statistics package, but it turns out to be a lot of fun to program with. It has also made possible a wide variety of clever and elegant custom syntaxes, such as a pipe infix operator implemented as a 3rd-party library without any custom language extensions [0]. The pipe infix operator got so popular that it was eventually made part of the language core syntax in version 4.1 [1].
[0]: https://magrittr.tidyverse.org/
[1]: https://www.r-bloggers.com/2021/05/the-new-r-pipe/
- Hadley is pro- base pipe.
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Functional pipes in python like %>% from R's magrittr
In R (thanks to magrittr) you can now perform operations with a more functional piping syntax via %>%. This means that instead of coding this:
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Question about dot notation
Try reading the documentation for magrittr.
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When did WG21 decide this is what networking looks like?
Related note: the statistical programming language R has a library named magrittr to support the pipe operator.
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How can I find the data entry of the row after one found?
About the pipe (%>%) symbol, it's provided by the magrittr package. The package documentation details how to use the pipe operator.
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Best practice for chaining nested functions?
I was wondering what some good ways are to handle nested function calls without chaining them in long, ugly nested statements. I am looking for functionality similar to the pipe forward operator %>% in magrittr/R or |> in F#.
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I much prefer `data.action()` to `action(data). Is it an r/unpopularopinion?
You may like R: https://magrittr.tidyverse.org
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What's so "tidy" about tidyverse?
Agreed on everything else you said (especially the type safety stuff, it massively helps in production), but one correction: magrittr is absolutely in the tidyverse suite. It's not considered one of its "core" packages that it visibly tells you it loads, but magrittr is loaded when calling library(tidyverse) and development of the package is handled by the tidyverse team under their Github account: https://github.com/tidyverse/magrittr
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?
dplyr - dplyr: A grammar of data manipulation
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)
scenebuilder - Scene Builder is a visual, drag 'n' drop, layout tool for designing JavaFX application user interfaces.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
kitten - A statically typed concatenative systems programming language.
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
power-fx-host-samples - Samples for hosting Power Fx engine.
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.
libuv-tutorial - http://nikhilm.github.io/uvbook/
scikit-learn - scikit-learn: machine learning in Python
ggplot2 - An implementation of the Grammar of Graphics in R
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.