There are a lot of articles about how to use Python for solving Machine Learning problems, with this article I start series of materials on how to use modern C++ for solving same problems and which libraries can be used. I assume that readers are already familiar with Machine Learning concepts and will concentrate on technical issues only. I start with simple polynomial regression to make a model to predict an amount of traffic passed through the system at some time point. Our prediction will be based on data gathered over some time period. The X data values correspond to time points and Y data values correspond to time points. For this tutorial I chose XTensor library, you can find documentation for it here. This library was chosen because of its API, which is made similar to numpy as much as possible. There are a lot of other linear algebra libraries for C++ like Eigen or VieanCL but this one allows you to convert numpy samples to C++ with a minimum effort.
This is our second post in this sub series “Machine Learning Types”. Our master series for this sub series is “Machine Learning Explained”. First post about Supervised Machine Learning is available here Unsupervised Learning; is one of three types of machine learning i.e. Supervised Machine Learning, Unsupervised Machine Learning and Reinforcement Learning. This post is limited to Unsupervised Machine Learning to explorer its details.
My blog last week articulated a first shot at automating the creation of meta data for the American Community Survey 2012-2016 household data set, using its published data dictionary. I deployed Python to wrangle the DD, ultimately generating the R syntax to convert many of the data.table’s integers to R factors with levels and labels. While this ‘worked’ and the Python code was simple, the process of moving between the Python wrangling and R data creation Jupyter notebooks was clunky and error-prone. This notebook hopefully presents the next level of sophistication with that work. Entirely R code both munges the data dictionary file to produce R syntax, and then applies the generated code to the household data table to convert many of its attributes from integers to factors.
Explore the definition of centrality, learn what different types of centrality measures exist in network analysis and pick the best one for a given network!
Generate TensorFlow Tensor full of random numbers in a given range by using TensorFlow’s random_uniform operation
1. The Nature Of Statistical Learning Theory
2. Pattern Classification by Richard O Duda (2007-12-24)
3. Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
5. Pattern Recognition and Machine Learning (Information Science and Statistics)
6. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
7. Deep Learning