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 This course is focused on the question: **How do we do matrix computations with acceptable speed and acceptable accuracy?**
 
-This course was taught in the [University of San Francisco's Masters of Science in Analytics](https://www.usfca.edu/arts-sciences/graduate-programs/analytics) program, summer 2017 (for graduate students studying to become data scientists).  The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons.
+This course was taught in the [University of San Francisco's Masters of Science in Analytics](https://www.usfca.edu/arts-sciences/graduate-programs/analytics) program, summer 2017 (for graduate students studying to become data scientists).  The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons.dqd ans
 
 Accompanying the notebooks is a [playlist of lecture videos, available on YouTube](https://www.youtube.com/playlist?list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY).  If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations, and answer questions.
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 ## Getting Help
 You can ask questions or share your thoughts and resources using the [**Computational Linear Algebra** category on our fast.ai discussion forums](http://forums.fast.ai/c/lin-alg).