Update README.md

README.md

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 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.
 
 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).
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 ## Table of Contents
 The following listing links to the notebooks in this repository, rendered through the [nbviewer](http://nbviewer.jupyter.org) service.  Topics Covered:
 ### [0. Course Logistics](https://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/0.%20Course%20Logistics.ipynb) ([Video 1](https://www.youtube.com/watch?v=8iGzBMboA0I&index=1&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY))
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   - [Importance of Technical Writing](https://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/0.%20Course%20Logistics.ipynb#Writing-Assignment)
   - [List of Excellent Technical Blogs](https://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/0.%20Course%20Logistics.ipynb#Excellent-Technical-Blogs)
   - [Linear Algebra Review Resources](https://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/0.%20Course%20Logistics.ipynb#Linear-Algebra)
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 ### [1. Why are we here?](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/1.%20Why%20are%20we%20here.ipynb) ([Video 1](https://www.youtube.com/watch?v=8iGzBMboA0I&index=1&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY))
 We start with a high level overview of some foundational concepts in numerical linear algebra.
   - [Matrix and Tensor Products](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/1.%20Why%20are%20we%20here.ipynb#Matrix-and-Tensor-Products)
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   - [Memory use](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/1.%20Why%20are%20we%20here.ipynb#Memory-Use)
   - [Speed](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/1.%20Why%20are%20we%20here.ipynb#Speed)
   - [Parallelization & Vectorization](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/1.%20Why%20are%20we%20here.ipynb#Scalability-/-parallelization)
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 ### [2. Topic Modeling with NMF and SVD](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb) ([Video 2](https://www.youtube.com/watch?v=kgd40iDT8yY&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY&index=2) and [Video 3](https://www.youtube.com/watch?v=C8KEtrWjjyo&index=3&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY))
 We will use the newsgroups dataset to try to identify the topics of different posts.  We use a term-document matrix that represents the frequency of the vocabulary in the documents.  We factor it using NMF, and then with SVD.
   - [Topic Frequency-Inverse Document Frequency (TF-IDF)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#TF-IDF)