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.
In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.
This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.
Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
277 Data Science Key Terms, Explained
42 Steps to Mastering Data Science