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Sanskar

Keen Learner and Exp... • 3m

Day 2 of learning mathematics for AI/ML as a no math person. Topic: vectors and matrices. We use NumPy python library for these. I got introduced to the concept of vectors and matrices. Vectors are like lists and are divided Vectors are divided into two categories i.e. row vector and column vector. Row vectors are like series of numbers that is they have one row however can have "n" number of columns. Column vector on the other have can have "n" number of rows however each row may have only one column. We can refer row vector as (1,n) and column vector as (n,1). When we combine both categories of vectors we get matrices which is like a list of lists it can contain both "n" number of rows and "n" number of columns. We can therefore refer matrices as (m x n). Then I have learn something called as "Transpose". Transpose means conversion of rows into column and column into rows. It is denoted by letter "T" and it is one of the most important concept for Machine Learning. We can perform arithmetic operations in these matrices for example addition, subtraction, multiplication etc. I have however not went deep into it today as my focus was more on understanding the basics of vectors and matrices. However I have plans to explore more about matrices because I think it is one of the most fundamental and important topic with respect to AI/ML. A lot of people have also recommended me some of the really great resources which I explored as well. Suggestions and recommendations of you amazing people always helps me learn better. Also here's my own handwritten notes and I am again sorry for my handwriting. 😅

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