# Introduction to Matrices (Pt. 1) ## Why are Matrices important? Wait, what, are we learning math all-over again!? 👻 ![](https://media1.giphy.com/media/aHv7TBImivdAI/giphy.gif) Maybe a good way to start off this GitBook is through the words of this man, a lecturer of MIT, Gilbert Strang. {% embed url="" %} I highly recommend watch his videos first, but will too humbly summarize his discussions. ## Matrices *Matrices* are (good for me, people with OCD lol) neat. It offers both a tidy way to 1. Represent **dimensions**, and 2. A nifty way to **transform** data as well. They are essentially {% hint style="success" %} * *Numbers* (and sometimes algebra), that are * Spaced out neatly in *rows and columns*, whom are * Arranged in a *rectangular* (sometimes brackets or straight-lines lol❓❓❓) array {% endhint %} This eases the representation of multiple *dimensions*, and also offers a *transparent, verifiable* way to demonstrate *transformation process* of data (addressing algorithmic bias). Dimensions are equivalent often input variables, and if you are unsure what it is, don't worry as of yet! 😉 It is merely a term used in **machine learning** to predict *output variables,* *general patterns and trends,* or for *reinforcement learning*. > For example, there may be numerous input variables that may go into a supposed model created for the whole purpose of predicting an output variable "HDB housing price", where these could range from substantial to trivial factors such as *location, type of flat (HDB flats or EC), amount of rooms, the amenities available, years of lease remaining, noise* factor (if it close to an air-base or airport), and etc. Currently, matrices are widely used in: * Inputs (possibly output) of machine learning (specifically deep learning / neural networks) * Term-document matrix in Natural Language Processing (NLP) ## References ``` https://towardsdatascience.com/linear-algebra-for-deep-learning-f21d7e7d7f23 https://www.khanacademy.org/math/pre-algebra/pre-algebra-arith-prop/pre-algebra-ditributive-property/a/distributive-property-explained https://www.analyticsvidhya.com/blog/2017/05/comprehensive-guide-to-linear-algebra/ https://www.youtube.com/watch?v=kYB8IZa5AuE https://www.kaggle.com/mjbahmani/linear-algebra-for-data-scientists https://docs.scipy.org/doc/numpy/reference/routines.linalg.html https://www.quantstart.com/articles/scalars-vectors-matrices-and-tensors-linear-algebra-for-deep-learning-part-1/ https://www.youtube.com/watch?v=J7DzL2_Na80&t=192s ``` ### **More on the next page ⏭** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://tony-ng-1.gitbook.io/matrices-and-linear-algebra-fundamentals/master.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.