CCV
mlpack
Our great sponsors
| CCV | mlpack | |
|---|---|---|
| 3 | 4 | |
| 7,040 | 4,797 | |
| - | 2.3% | |
| 9.5 | 9.9 | |
| 25 days ago | 2 days ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | 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.
CCV
- Modern Image Processing Algorithms Implementation in C
-
[BBC solent sport] Gary Cahill on the verge of joining AFC Bournemouth on a free transfer
CCV?
-
How to extract dominant color of an image?
liuliu/ccv: C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library (github.com)
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
What are some alternatives?
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
tensorflow - An Open Source Machine Learning Framework for Everyone
Caffe - Caffe: a fast open framework for deep learning.
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
MeTA - A Modern C++ Data Sciences Toolkit
SHOGUN - ShÅgun
NN++ - A small and easy to use neural net implementation for C++. Just download and #include!
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
Fido - A lightweight C++ machine learning library for embedded electronics and robotics.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more