Technical topics like mathematics, physics, and even computer science are taught using a bottom-up approach. This approach involves laying out the topics in an area of study in a logical way with a natural progression in complexity and capability. The problem is, humans are not robots executing a learning program. We require motivation, excitement, and […]

## Basics of Mathematical Notation for Machine Learning

You cannot avoid mathematical notation when reading the descriptions of machine learning methods. Often, all it takes is one term or one fragment of notation in an equation to completely derail your understanding of the entire procedure. This can be extremely frustrating, especially for machine learning beginners coming from the world of development. You can […]

## Linear Algebra for Machine Learning (7-Day Mini-Course)

Linear Algebra for Machine Learning Crash Course. Get on top of the linear algebra used in machine learning in 7 Days. Linear algebra is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning. Although linear algebra is a large field with many esoteric theories and […]

## Computational Linear Algebra for Coders Review

Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. It is an area that requires some previous experience of linear algebra and is focused on both the performance and precision of the operations. The company fast.ai released a free course titled “Computational Linear Algebra” […]

## Introduction to Linear Algebra by Gilbert Strang for Machine Learning

If you are interested in getting started with or brushing up on the topic of linear algebra, then there is one textbook that is most likely going to be recommended to you. That textbook is “Introduction to Linear Algebra” by Gilbert Strang and it provides a reference for his linear algebra course taught at MIT […]

## Linear Algebra for Deep Learning

Linear algebra is a field of applied mathematics that is a prerequisite to reading and understanding the formal description of deep learning methods, such as in papers and textbooks. Generally, an understanding of linear algebra (or parts thereof) is presented as a prerequisite for machine learning. Although important, this area of mathematics is seldom covered […]

## A Gentle Introduction to Sparse Matrices for Machine Learning

Matrices that contain mostly zero values are called sparse, distinct from matrices where most of the values are non-zero, called dense. Large sparse matrices are common in general and especially in applied machine learning, such as in data that contains counts, data encodings that map categories to counts, and even in whole subfields of machine […]

## A Gentle Introduction to Broadcasting with NumPy Arrays

Arrays with different sizes cannot be added, subtracted, or generally be used in arithmetic. A way to overcome this is to duplicate the smaller array so that it is the dimensionality and size as the larger array. This is called array broadcasting and is available in NumPy when performing array arithmetic, which can greatly reduce […]

## 10 Examples of Linear Algebra in Machine Learning

Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms. It is a key foundation to the field of machine learning, from notations used to describe the operation of algorithms to the implementation of algorithms in code. Although linear algebra is integral to the field of machine learning, the tight relationship […]

## No Bullshit Guide To Linear Algebra Review

There are many books that provide an introduction to the field of linear algebra. Most are textbooks targeted at undergraduate students and are full of theoretical digressions that are barely relevant and mostly distracting to a beginner or practitioner to the field. In this post, you will discover the book “” that provides a gentle […]