Code Monkey home page Code Monkey logo

teachingdatascience's Introduction

Teaching Data Science

Repository for LaTeX course notes for Python, Machine Learning, Deep Learning, Natural Language Processing, etc. Core content is in the form for Beamer slides, which in turn can get compiled into presentation pdf as well as two-column course notes pdf.

All tex sources and images have been open sourced as I have built them by referring to material from others, learnt from others, although I have added some of mine, I need to give it back!!

LinkedIn post: https://www.linkedin.com/feed/update/urn:li:activity:6523000857385103360

Copyright (C) 2019 Yogesh H Kulkarni

Steps for LaTex source files to pdfs

Code Arrangement

  • LaTeX directory
    • Has tex sources along with necessary images
    • Naming: subject_maintopic_subtopic.tex eg maths_linearalgebra_matrices.tex
    • Main_Workshop/Seminar_Presentation/CourseMaterial.tex are the driver files
    • They intern contain common content files, which have included actual source files
    • Make bat files compile the appropriate sources
  • Code directory
    • Has running python or ipython notebook files with necessary images and data
    • Naming should be such that corresponding latex file can be associated
    • Library based tex file, say, sklearn_decisiontree.tex will have just template code and short fully working examples from Mastering Machine Learning whereas the sklearn_decisiontree.ipynb will have running workflows. No need to sync both. You may want to keep ipynb’s pdf in LaTeX/images directory for reference
  • References directory (not uploaded, as it is mostly from others github repos, nothing much of mine)
    • Has papers, code, ppts as base material to be used for content preparation

Requirements

  • LaTeX (tested with MikTex 2.9 on Windows 7, 64bit)
  • Need to install LaTeX packages, as and when you get such warning/suggestions.
  • Using TexWorks as IDE

How to Run LaTeX:

  • Driver files for the courses are named with "Main_Workshop/Seminar__CheatSheet/Presentation.tex"
  • Both the Cheatsheet (meaning course notes in two column format) and Presentation.tex refer to same core content file, which in turn contains are the topic files.
  • Run make bat for the course you need. Inside, its just a texify command, so you can modify it as per your OS.
  • You can compile individual "Main_Workshop/Seminar__CheatSheet/Presentation.tex" also using your LaTeX system.
  • Instead of these given driver files, you can have your own main files and include just the *content.tex files.

Disclaimer:

  • Author ([email protected]) gives no guarantee of the correctness of the content. Notes have been built using lots of publicly available material.
  • Although care has been taken to cite the original sources as much as possible, but there could be some missing ones. Do point them and I will update wherever possible.
  • Lots of improvements are still to be made. So, don’t depend on it at all, fully.
  • Do send in your suggestions/comments/corrections/Pull-requests.

teachingdatascience's People

Contributors

yogeshhk avatar dependabot[bot] avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.