fury-benchmarks
ML-For-Beginners
| fury-benchmarks | ML-For-Beginners | |
|---|---|---|
| 4 | 28 | |
| 2 | 67,267 | |
| - | 3.0% | |
| 5.9 | 7.6 | |
| 14 days ago | 9 days ago | |
| Java | HTML | |
| - | MIT License |
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.
fury-benchmarks
- FLaNK Stack Weekly for 20 Nov 2023
- FLaNK Stack Weekly for 30 Oct 2023
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Fury: 170x faster than JDK, fast serialization powered by JIT and Zero-copy
1) Fury is 41.6x faster than jackson for Struct serialization 2) Fury is 65.6x faster than jackson for Struct deserialization 3) Fury is 9.4x faster than jackson for MediaContent serialization 4) Fury is 9.6x faster than jackson for MediaContent deserialization
see https://github.com/chaokunyang/fury-benchmarks for detailed benchmark code.
ML-For-Beginners
-
Good coding groups for black women?
- https://github.com/microsoft/ML-For-Beginners
Also check out this list Pitt puts out every year:
- FLaNK Stack Weekly for 20 Nov 2023
- ML for Beginners GitHub
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is it worth learning NLP without master degree?
I don't recommend just jumping in into natural language processing directly without understanding artificial intelligence theory. I personally recommend for you to start with the basic stuff (regression, classification, and clustering, for example), and then jump into more advanced topics. You already know software developer stuff, so that's a big step already, and it should be easier to understand some concepts. Maybe follow Microsoft's machine learning for beginners curriculum? It looks like a good roadmap overall to not instantly burn out on nlp
- AI i Machine Learning
- I want to learn more about AI and Machine Learning
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Pocetak ML karijere
https://github.com/microsoft/ML-For-Beginners jel mislis na ovo?
- How could I have known
- GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- How do I reset my career after already getting my masters?
What are some alternatives?
jvm-serializers - Benchmark comparing serialization libraries on the JVM
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
MemoryPack - Zero encoding extreme performance binary serializer for C# and Unity.
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
MessagePack for C# (.NET, .NET Core, Unity, Xamarin) - Extremely Fast MessagePack Serializer for C#(.NET, .NET Core, Unity, Xamarin). / msgpack.org[C#]
pycaret - An open-source, low-code machine learning library in Python
grpc-dotnet - gRPC for .NET
Data-Science-For-Beginners - 10 Weeks, 20 Lessons, Data Science for All!
incubator-fury - A blazingly fast multi-language serialization framework powered by JIT and zero-copy.
pyVHR - Python framework for Virtual Heart Rate
orbital - Orbital automates integration between data sources (APIs, Databases, Queues and Functions). BFF's, API Composition and ETL pipelines that adapt as your specs change.
S2ML-Art-Generator - Multiple notebooks which allow the use of various machine learning methods to generate or modify multimedia content [Moved to: https://github.com/justin-bennington/S2ML-Generators]