Our goal in this article is to explain the intuition behind gradient boosting, provide visualizations for model construction, explain the mathematics as simply as possible, and answer thorny questions …

Our goal in this article is to explain the intuition behind gradient boosting, provide visualizations for model construction, explain the mathematics as simply as possible, and answer thorny questions …

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and …

3 The difference between L1 and L2 regularization Terence Parr (Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program. …

We'll also explore k-nearest neighbors, decision trees, and linear regression models along the way in order to explain and motivate our “power tools,” random forests and neural networks.

Personally, my biggest initial stumbling block was this: The math used to implement regularization does not correspond to pictures commonly used to explain regularization.

Mar 26, 2018 · (Dropping features is a good idea because it makes it easier to explain models to consumers and also increases training and testing efficiency/speed.) For example, the mean radius …

Model LMModel3 and Section "Maintaining the State of an RNN" of Chapter 12 in the fastai book explain this in detail. Each variable h is associated with a single input record and is initialized to the zero …

To visualize tensor dimensionality within exception-free Python statements, TensorSensor provides a mechanism called explain () that is similar to clarify (), except that explain () generates a visualization …

This book is a primer on machine learning for programmers trying to get up to speed quickly.