Matthew Mayo

KDnuggets Managing Editor

Matthew Mayo (@mattmayo13) holds a master's degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

Data Science Basics: What Types of Patterns Can Be Mined From Data?

By Matthew Mayo, KDnuggets Managing Editor on December 14, 2016 in Beginners, Classification, Data Science, Frequent Pattern Mining, Outliers, Regression
Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.
arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well?

By Matthew Mayo, KDnuggets Managing Editor on December 13, 2016 in Academics, arXiv, Deep Learning, Machine Learning
The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.
4 Cognitive Bias Key Points Data Scientists Need to Know

By Matthew Mayo, KDnuggets Managing Editor on December 9, 2016 in Bias, Cognitive Bias, Confirmation Bias
Cognitive biases are inherently problematic in a variety of fields, including data science. Is this something that can be mitigated? A solid understanding of cognitive biases is the best weapon, which this overview hopes to help provide.
Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition

By Matthew Mayo, KDnuggets Managing Editor on November 30, 2016 in AI, Amazon, Artificial Intelligence, Books, Machine Learning
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview

By Matthew Mayo, KDnuggets Managing Editor on November 24, 2016 in Algorithms, Linear Regression
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
Predictive Science vs Data Science

By Matthew Mayo, KDnuggets Managing Editor on November 22, 2016 in Algorithms, Applied Statistics, Data Science, Prediction
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
Data Avengers… Assemble!

By Matthew Mayo, KDnuggets Managing Editor on November 19, 2016 in Comic, Data Science, Data Science Team
The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.
Top 10 Amazon Books in Data Mining, 2016 Edition

By Matthew Mayo, KDnuggets Managing Editor on November 11, 2016 in Amazon, Books, Data Mining, Data Science
Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.
Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications

By Matthew Mayo, KDnuggets Managing Editor on November 10, 2016 in Algorithms, CUDA, GPU, NVIDIA, Parallelism
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
Data Science Basics: An Introduction to Ensemble Learners

By Matthew Mayo, KDnuggets Managing Editor on November 8, 2016 in Beginners, Boosting, Data Science, Ensemble Methods
New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.

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