arl
m2cgen
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
arl
- Most-starred *insert programming language* GitHub repositories
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If anyone has a good source for learning VHDL basics from scrarch, please recommend.
I think another good approach might be to study some existing VHDL-based open source projects. For example, here is a list of the most-stared VHDL GitHub repositories.
- A list of GitHubs's most stared VHDL repositories
m2cgen
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How to use python ML script in tauri?
Check out: https://github.com/BayesWitnesses/m2cgen
- EleutherAI announces it has become a non-profit
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Redis as a Database — Data Migration With RedisOM, RedisGears and Redlock
Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.
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Why isn’t Go used in AI/ML?
I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen
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Use your decision tree model in your Javascript project today with m2cgen
And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.
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We use Rust for an opensource malware detection engine. It's great at detecting ransomwares and we want to share results and ideas with you.
I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.
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Is data science/engineering in Rust practical, does it provide any benefit over Python, and what are the best crates?
Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).
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Flutter Machine Learning App
These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn
What are some alternatives?
pivotnacci - A tool to make socks connections through HTTP agents
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
speed-comparison - A repo which compares the speed of different programming languages.
Synapses - A group of neural-network libraries for functional and mainstream languages
lotus - Open Source Pricing & Packaging Infrastructure
R Provider - Access R packages from F#
liquid-cpp - A C++ liquid parser/renderer, with an eye on embeddability, performance, extensibility, sandboxability, and multi-language interop.
gorse - Gorse open source recommender system engine
tabnine-vscode - Visual Studio Code client for Tabnine. https://marketplace.visualstudio.com/items?itemName=TabNine.tabnine-vscode
randomforest - Random Forest implementation in golang
lsp-mode - Emacs client/library for the Language Server Protocol
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)