Linear Algebra for Deep Learning – Towards Data Science

#artificialintelligence

Linear algebra, probability and calculus are the'languages' in which machine learning is formulated. Learning these topics will contribute a deeper understanding of the underlying algorithmic mechanics and allow development of new algorithms. When confined to smaller levels, everything is math behind deep learning. So it is essential to understand basic linear algebra before getting started with deep learning and programming it. The core data structures behind Deep-Learning are Scalars, Vectors, Matrices and Tensors.


Basic Linear Algebra for Deep Learning – Towards Data Science

#artificialintelligence

Linear Algebra is a continuous form of mathematics and is applied throughout science and engineering because it allows you to model natural phenomena and to compute them efficiently. Because it is a form of continuous and not discrete mathematics, a lot of computer scientists don't have a lot of experience with it. Linear Algebra is also central to almost all areas of mathematics like geometry and functional analysis. Its concepts are a crucial prerequisite for understanding the theory behind Machine Learning, especially if you are working with Deep Learning Algorithms. You don't need to understand Linear Algebra before getting started with Machine Learning, but at some point, you may want to gain a better understanding of how the different Machine Learning algorithms really work under the hood.


Linear Algebra for Deep Learning – Towards Data Science

@machinelearnbot

Linear Algebra is a continuous form of mathematics and it is applied throughout science and engineering because it allows you to model natural phenomena and to compute them efficiently. Because it is a form of continuous and not discrete mathematics, a lot of computer scientists don't have a lot of experience with it. Linear Algebra is also central to almost all areas of mathematics like geometry and functional analysis. Its concepts are a crucial prerequisite for understanding the theory behind Machine Learning, especially if you are working with Deep Learning Algorithms. You don't need to understand Linear Algebra before you get started with Machine Learning but at some point, you want to gain a better intuition for how the different machine learning algorithms really work under the hood.


Matrix Math & Numpy Refresher For Deep Learning – Towards Data Science

#artificialintelligence

Deep learning involves a lot of matrix math, and it's important for you to understand the basics before diving into building your own neural networks. These lessons provide a short refresher on what you need to know for this course, along with some guidance for using the NumPy library to work efficiently with matrices in Python. Python is convenient, but it can also be slow. However, it does allow you to access libraries that execute faster code written in languages like C. NumPy is one such library: it provides fast alternatives to math operations in Python and is designed to work efficiently with groups of numbers -- like matrices. NumPy is a large library and we are only going to scratch the surface of it here.


What Is a Tensor? – Physics Forums Insights

@machinelearnbot

It is a long way from a scheme of numbers to this categorial definition. To be of practical use, the truth lies – as so often – in between. Numbers don't mean anything without basis, and categorial terms are useless in everyday's business where coordinates are dominant.