Learn how to apply feature scaling during the data transformation phase to improve Machine Learning results.
This is the standard author on the site. Most articles are published by individual authors, with their own profiles, but when multiple people have a strong contribution, we publish collectively here.
Here's what I've written (so far):
A quick and practical guide to binary search trees.
Learn the core concepts of the functional programming paradigm and how it compares to OOP.
Learn how different dimensions are used in convolutional neural networks.
Learn how to solve the Lowest Common Ancestor problem of two nodes in a binary tree.
Study two fundamental components of Convolutional Neural Networks – the Rectified Linear Unit and the Dropout Layer.
Study the nonlinear activation functions most commonly used in backpropagation algorithms and other learning procedures.
Learn how to implement a Stack data structure using two Queues.
Learn about the ABA problem; how it happens, what problems it can cause, and how to fix it.
Explore the differences between linear and nonlinear problems, and how the former are inadequate in dealing with complex nonlinear problems.