DMTK includes the following projects:

• DMTK framework(Multiverso): The parameter server framework for distributed machine learning.
• LightLDA: Scalable, fast and lightweight system for large-scale topic modeling.
• LightGBM: LightGBM is a fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
• Distributed word embedding: Distributed algorithm for word embedding implemented on multiverso.

(1) OpenCV的OpenCL实现了下面的哪些算法？

• • Training Data
  • Normal Bayes Classifier
  • K-Nearest Neighbors
  • Support Vector Machines
    • Prediction with SVM
  • Decision Trees
    • Predicting with Decision Trees
    • Training Decision Trees
    • Variable Importance
  • Boosting
    • Prediction with Boost
  • Random Trees
  • Expectation Maximization
  • Neural Networks
  • Logistic Regression

Ref: How to use NVIDIA GPUs for Machine Learning with the new Data Science PC from Maingear

看样子大家才刚刚意识到这个事情，或者dnn就足够了。

Goto: [CUDA] Install H2O.ai，有部分GPU实现的算法。

• GLM: Lasso, Ridge Regression, Logistic Regression, Elastic Net Regulariation
• KMeans
• Gradient Boosting Machine (GBM) via XGBoost
• Singular Value Decomposition(SVD) + Truncated Singular Value Decomposition
• Principal Components Analysis(PCA)

Real time bench mark: https://www.youtube.com/watch?v=LrC3mBNG7WU，速度快二十倍。

Classification with OpenCV3 C++ (1/2)

Classification with OpenCV3 C++ (2/2)

Code for my blog post: "Classification with OpenCV 3 C++"

/* implement */