Linear Algebra Usage Introduction: Linear Algebra is the primary computation tool in both Artificial Intelligence (AI) and Quantum Computing (QC). In this blog will cover the required topics for storage and computing for both the fields.Linear Algebra is basically a Mathematical Computational Tool and widely used in many areas like science , engineering and computational fields.
Linear Algebra Usage Introduction: Linear Algebra is the primary computation tool in both Artificial Intelligence (AI) and *Quantum Computing (QC). *In this blog will cover the required topics for storage and computing for both the fields.
Linear Algebra Concepts
Linear Algebra is basically a Mathematical Computational Tool and widely used in many areas like science , engineering and computational fields. Following diagram describes the concepts used in Linear Algebra for AI and Quantum Computing, first 4 topics (Vectors, Matrices, Tensors and Product Rules) covered in this article and the remaining topics are Decomposition ,*Advanced Concepts *for Quantum Computing, *Matrix Calculus *for Machine Learning and Deep Learning in other articles.
*Note: *Decomposition and Matrix Calculus links updated soon.
Primarily your data has to be stored in some where else to compute, Linear Algebra stores your data in Vectors, Matrices and Tensors and then operate as per your instructions. Linear Algebra is a Storage Space.
Title diagram shows what are mathematical objects as well as AI sub fields and Quantum Computing uses Linear Algebra.
Artificial Intelligence: Linear Algebra can be applied in Machine Learning and Artificial Intelligence, including exposure to clustering, data fitting, classification, validation, and feature engineering.
Linear algebra applied in unsupervised learning methods (like k-means) such as principal component analysis (PCA), non-negative matrix factorization and more sophisticated clustering methods. You can apply regression and classification methods , such as logistic regression and the support vector machine, as well as methods for computing model parameters that scale to extremely large scale problems.
How uses in Artificial Intelligence ? In this article will explain how objects and its properties are using in AI’s sub-fields ML, NLP, DL,etc., algorithms.
With the democratization of AI/ML and open source libraries like Keras, scikit-learn etc, anyone with basic python knowledge can set up a working ML classifier in under 5 mins time. While this is more than enough to get started, if you want to understand how different ML algorithms work or implement the latest SOTA (State of the Art) papers to your particular domain, the lack of mathematical expertise quickly becomes a bottleneck as I have experienced firsthand.
Your options on how to start with working with today’s quantum computers. Quantum computing is one of the most rapidly advancing technologies.
Looking at graphics processing to understand the future of quantum computation. Many people are looking to quantum computing as the next revolutionary technology.
Linear Algebra in ML, CV, and NLP. When people think of the field of data science in general, or of specific areas of it, such as natural language processes, machine learning, or computer vision, they rarely consider linear algebra. The reason linear algebra is often overlooked is that tools used today to implement data science algorithms do an excellent job in hiding the underlying maths that make everything come true.