Computational Linear Algebra is the method by which computers are made to perform using matrices of data science. Why is it necessary to learn it? Because it is the way forward in data science.
What is Computational Linear Algebra exactly?
Computational Linear Algebra is manipulating matrices since almost all data can be represented as a matrix: time-series, structured data, anything that fits in a spreadsheet or SQL database, images, and language.
What are the benefits of learning Computational Linear Algebra?
- It makes the system faster
- Increases accuracy of computation
- Efficiency in retaining memory
- Improves scalability
Practical aids to learn
- Randomized algorithms
- Numba: a library that compiles Python to optimized C code
- PyTorch: an alternative to Numpy that runs on the GPU (also, an excellent deep learning framework, although we are using it just as a Numpy alternative here to speed up our code)
This course uses the same top down, code first, application centered teaching method
- The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github.
- Playlist of lecture videos
Who is conducting this?
Course material will be given online and candidates can learn at their own pace
Practical applications of Computational Linear Algebra
- How to identify the foreground in a surveillance video, by decomposing a matrix into the sum of two other matrices
- How to reconstruct an image from a CT scan using the angles of the x-rays and the readings
- How to identify topics in a corpus of documents
- The algorithm behind Google's PageRank, used to rank the relative importance of different web pages