From 747e0263e97593562524b9ab797a55064e1ca5c1 Mon Sep 17 00:00:00 2001 From: ahmetgul23 <155829316+ahmetgul23@users.noreply.github.com> Date: Fri, 2 Feb 2024 23:23:02 +0300 Subject: [PATCH] Update README.md --- README.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/README.md b/README.md index 767c76d..3c6a781 100644 --- a/README.md +++ b/README.md @@ -5,10 +5,8 @@ This course is focused on the question: **How do we do matrix computations with 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. - ## 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). - ## 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)) @@ -17,8 +15,6 @@ The following listing links to the notebooks in this repository, rendered throug - [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) - - ### [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) @@ -27,7 +23,6 @@ We start with a high level overview of some foundational concepts in numerical l - [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) - ### [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)