mlpack
autodiff
Our great sponsors
mlpack | autodiff | |
---|---|---|
4 | 7 | |
4,797 | 1,532 | |
2.3% | 2.5% | |
9.9 | 7.5 | |
7 days ago | 20 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | 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.
mlpack
-
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 ?
-
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.
-
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.
-
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
autodiff
- The Elements of Differentiable Programming
-
Astray: A performance-portable geodesic ray tracing library.
I completely agree. Specifying the metric rather than the Christoffel symbols would make it much easier for the users. Something like https://github.com/autodiff/autodiff might just work as the metric tensor is made up of primitives.
-
Point-to-Point Distance Constraint: Gradient of Forward Kinematics
Old username :D So far I have been using Eigen for linear algebra and NLOPT for optimization algorithms. I have found "autodiff" that hopefully looks easy to use: https://github.com/autodiff/autodiff
- Autodiff: Simple C++17 library for Automatic Differentiation
-
Gradients Without Backpropagation
Forward-mode differentiation is easy to implement in C++ with templates, operator overloading, and dual numbers (https://en.wikipedia.org/wiki/Automatic_differentiation#Auto...). Some libraries such as autodiff (https://github.com/autodiff/autodiff) and CppAD (https://github.com/coin-or/CppAD) use this method.
- Ensmallen: A C++ Library for Efficient Numerical Optimization
-
I am creating a fast, header-only, C++ library for control algorithms
I was thinking of adding [autodiff](https://github.com/autodiff/autodiff) in the future, mainly because it works seamlessly with *Eigen*. One big advantage would be that I could use it for AD for NonlinearSystems as well.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
CppAD - A C++ Algorithmic Differentiation Package: Home Page
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
FastAD - FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
SHOGUN - ShÅgun
MathNet - Math.NET Numerics
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
MKL.NET - A simple cross platform .NET API for Intel MKL
Caffe - Caffe: a fast open framework for deep learning.
CppRobotics - Header-only C++ library for robotics, control, and path planning algorithms. Work in progress, contributions are welcome!
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
PythonRobotics - Python sample codes for robotics algorithms.