mlpack
tensorflow
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mlpack | tensorflow | |
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4 | 221 | |
4,797 | 182,456 | |
2.3% | 0.8% | |
9.9 | 10.0 | |
5 days ago | about 19 hours ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
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
tensorflow
- 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? :)
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How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
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When the documentation has TODOs
Since you've specifically mentioned ML, here's Tenserflow's GitHub. I'm sure a quick glance through that will change your mind.
What are some alternatives?
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
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)
SHOGUN - Shōgun
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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
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
Caffe - Caffe: a fast open framework for deep learning.
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.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
scikit-learn - scikit-learn: machine learning in Python
examples - TensorFlow examples
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.