Last Updated on August 9, 2019 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 “No bullshit guide to linear algebra” that provides a gentle introduction to the field of linear algebra and assumes no prior mathematical knowledge. After […]

Last Updated on August 9, 2019 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 is often left unexplained or explained using abstract concepts such as vector spaces or specific matrix operations. In this […]

Last Updated on August 9, 2019 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 and simplify your code. In this tutorial, you will discover the concept of array broadcasting and how to implement […]

Last Updated on August 9, 2019 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 learning such as natural language processing. It is computationally expensive to represent and work with sparse matrices as though […]

Last Updated on August 9, 2019 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 by computer science or software engineering degree programs. In their seminal textbook on deep learning, Ian Goodfellow and others […]

Last Updated on August 9, 2019 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” on the topic of numerical linear algebra that includes Python notebooks and video lectures recorded at the University of […]

Last Updated on August 9, 2019 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 findings, the nuts and bolts tools and notations taken from the field are required for machine learning practitioners. With […]

Last Updated on May 7, 2020 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 make great progress if you know a few basic areas of mathematical notation and some tricks for working through […]