2 things in mathematics always had me in awe . One was complex number ( thanks to Welch Labs and this awesome book ) and other one was linear algebra ( thanks to the one and only great Grant Sanderson ) . From state of the art Machine Learning to the core of Quantum Computing , Linear Algebra is practically everywhere . In my level 2 in undergrad , I had to take Linear Algebra Course which was a course offered from the math department . To get some CS flavour and gain better intuition , I had to dive deep into other resources (details in the Resource section) . This is some kind of hybrid of personal note + blog of that time , contains some of the core stuffs that I had learnt and gained better intuition through this time . I have also implemented some of the basic LA algorithm stuffs in python for reference code along with the theories .

Hopefully this serves as a good starting point / refresher for anyone who is starting / practising ML / QC or anyone who is interested in general .

• Website : Link

• Code : Link

This is yet to be completed . In Future , I have plans to add more applications like in ML and QC . If you are interested to contribute with anything (writeup/code/simulation) , feel free to send pull request / let me know .

• Essence of Linear Algebra : 3Blue1Brown
• MIT 18.06 Linear Algebra, Spring 2005 : Gilbert Strang
• Linear Algebra and Its Applications : David C Lay, Judi J. McDonald, and Steven R Lay
• Introduction to Linear Algebra : Gilbert Strang
• Coding the Matrix: Linear Algebra Through Applications to Computer Science