The singular value decomposition (SVD) is a crucial concept for machine learning that builds the foundation of many algorithms you will use throughout your machine learning journey. Itβs not only used in ML or data science but in other fields too. At its core, SVD breaks down complex datasets into simpler, more interpretable components, opening the door to a world of insights. It also allows you to find solutions to various problems such as image compression, text mining, and multidimensional data analysis.
Thatβs why itβs so important to understand. For machine learning, you donβt need to learn how to implement SVD in Python because so many libraries allow you to perform SVD. Another reason why you donβt need to know its implementation is because it can be a very complex project which will deviate you from the main goal of actually learning how to use it. However, to use SVD you need to understand the basics of SVD so that you can make the best of it and understand more advanced concepts such as PCA which I will explain in another post. So, letβs get started and learn SVD.
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