About Matthew Mayo

Matthew Mayo is Machine Learning Researcher and the Editor of KDnuggets, the seminal online Data Science and Machine Learning resource. He is particularly interested in unsupervised learning, deep neural networks, natural language processing, algorithm design and optimization, and distributed approaches to data processing and analysis. Matthew holds a Master's degree in CS and a graduate diploma in Data Mining. Email him at mattmayo at kdnuggets[dot]com.

Matthew Mayo Posts (309)

• NeurIPS 2019 Outstanding Paper Awards - 11 Dec 2019
  
  NeurIPS 2019 is underway in Vancouver, and the committee has just recently announced this year's Outstanding Paper Awards. Find out what the selections were, along with some additional info on NeurIPS papers, here.
• 5 Great New Features in Latest Scikit-learn Release - 10 Dec 2019
  
  From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - 09 Dec 2019
  
  As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
• 10 Free Top Notch Machine Learning Courses - 06 Dec 2019
  
  Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
• KDnuggets Poll: How well do current AutoML solutions work? - 04 Dec 2019
  
  Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions.
• Getting Started with Automated Text Summarization - 28 Nov 2019
  
  This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
• Automated Machine Learning Project Implementation Complexities - 22 Nov 2019
  
  To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.
• GitHub Repo Raider and the Automation of Machine Learning - 18 Nov 2019
  
  Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.
• How to Create a Vocabulary for NLP Tasks in Python - 07 Nov 2019
  
  This post will walkthrough a Python implementation of a vocabulary class for storing processed text data and related metadata in a manner useful for subsequently performing NLP tasks.
• 5 Statistical Traps Data Scientists Should Avoid - 30 Oct 2019
  
  Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.