voyager
mlpack
voyager | mlpack | |
---|---|---|
4 | 4 | |
1,160 | 4,822 | |
2.2% | 1.1% | |
7.9 | 9.9 | |
about 1 month ago | 7 days ago | |
C++ | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
voyager
- FLaNK Stack for 04 December 2023
- Voyager: An approximate nearest-neighbor search library for Python and Java
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Approximate Nearest Neighbors Oh Yeah
Annoy came out of Spotify, and they just announced their successor library Voyager [1] last week [2].
[1]: https://github.com/spotify/voyager
- Voyager: A Library for Approximate Nearest-Neighbor Search by Spotify
mlpack
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How much C++ is used when it comes to performing quant research?
Does C++ have the equivalent of Pandas or Apache Spark? Are there extensive libraries that exist/are being developed that allow you to perform operations with data? Or do people just use a combination of Python & its various libraries (NumPy etc)? If we leave aside the data bit, are there libraries that allow you to develop ML models in C++ (mlpack for instance ) faster & more efficiently compared to their Python counterparts (scikit-learn)? On a more general note, how does C++ fit into the routine of a Quant Researcher? And at what scale does an organization decide they need to start switching to other languages and spend more time developing the code ?
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What is the most used library for AI in C++ ?
mlpack is a great library for machine learning in C++. It's very fast and not too much of a learning curve.
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Ensmallen: A C++ Library for Efficient Numerical Optimization
This toolkit was originally part of the mlpack machine learning library (https://github.com/mlpack/mlpack) before it was split out into a separate, standalone effort.
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Top 10 Python Libraries for Machine Learning
Github Repository: https://github.com/mlpack/mlpack Developed By: Community, supported by Georgia Institute of technology Primary purpose: Multiple ML Models and Algorithms
What are some alternatives?
marker - Convert PDF to markdown quickly with high accuracy
tensorflow - An Open Source Machine Learning Framework for Everyone
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
MITIE - MITIE: library and tools for information extraction
SHOGUN - Shōgun
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.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
onnx-models - A copy of ONNX models, datasets, and code all in one GitHub repository. Follow the README to learn more.
Caffe - Caffe: a fast open framework for deep learning.
nougat - Implementation of Nougat Neural Optical Understanding for Academic Documents
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more