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 (246)

• The 4 Levels of Data Usage in Data Science - 09 Jul 2018
  
  This is an overview of the 4 levels, or "buckets," of data usage in business, starting at monitoring and progressing to automation.
• Automated Machine Learning vs Automated Data Science - 02 Jul 2018
  
  Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.
• Building a Basic Keras Neural Network Sequential Model - 29 Jun 2018
  
  The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.
• 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - 25 Jun 2018
  
  Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
• Natural Language Processing Nuggets: Getting Started with NLP - 19 Jun 2018
  
  Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
• Step Forward Feature Selection: A Practical Example in Python - 18 Jun 2018
  
  When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
• Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray - 15 Jun 2018
  
  While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.
• Generating Text with RNNs in 4 Lines of Code - 14 Jun 2018
  
  Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."
• 5 Machine Learning Projects You Should Not Overlook, June 2018 - 12 Jun 2018
  
  Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
• The Keras 4 Step Workflow - 04 Jun 2018
  
  In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.