index.yaml

# This defines what the user sees in the library dialog -- smc-webapp/library.cjsx
# see index.py for more details.
---
# an entry can have a list of tags
tags:
  calc:
    name: Calculus
  linalg:
    name: Linear Algebra
  stats:
    name: Statistics
  intro:
    name: Introduction
  python:
    name: Python
    info: "Make sure to pick a suitable Python environment (Python 2, Python 3, Anaconda, ...)"
  math:
    name: Mathematics
  julia:
    name: Julia
    info: "Be patient. Starting Julia the first time might require to recompile some modules."
  finance:
    name: Finance
  datascience:
    name: Data science
# an entry can have exactly one license
licenses:
  a20: Apache 2.0
  asis: AS IS
  bsd: BSD
  cc0: CC0 1.0 Universal
  ccby: CC BY 4.0
  ccbysa: CC BY-SA
  ccbync3: CC BY-NC 3.0
  mit: MIT
  gpl3: GPLv3
  gfdl: GNU Free Documentation License
  mpl2: Mozilla Public License, v. 2.0.
# each entry must have exactly one category
# weight: additional weighting for sorting by category – default 0
categories:
  intro:
    name: Introduction
    weight: -1
  stats:
    name: Statistics
  cs:
    name: Computer science
  datascience:
    name: Data science
  math:
    name: Mathematics
  physics:
    name: Physics
  chemistry:
    name: Chemistry
  latex:
    name: LaTeX templates
  science:
    name: Science
  misc:
    name: Miscellaneous
    weight: 1
  finance:
    name: Finance
references:
  - cocalc-example-files/index.yaml
---
id: "bayes-for-hackers"
title: "Bayesian Methods for Hackers"
src: bayesian-methods-for-hackers/
thumbnail: thumbs/bayesian.png
description: |
  Hands-on tutorial how Bayesian Methods work, based on Python's PyMC library.
author: Cameron Davidson-Pilon
license: asis
category: stats
preview: "https://cocalc.com/share/8d8556442c370812af9f5268e7fa66a5262931f5/examples/bayesian-methods-for-hackers?viewer=share"
tags:
  - stats
  - python
---
id: cmichi-latex-templates
src: cmichi-latex-templates/
title: "Michael Müller's LaTeX Templates"
thumbnail: thumbs/muller-latex.png
category: latex
license: mit
description: |
  Collection of different LaTeX/XeTeX templates (cv, invoices, timesheets, letters, etc.).
website: "https://github.com/cmichi/latex-template-collection"
---
id: deedy-latex-templates
src: deedy-latex-templates/
title: "Deedy LaTeX Templates"
thumbnail: thumbs/deedy-latex.png
license: cc0
category: latex
description: |
  A concise set of Latex templates that serves a small set of needs - CV, Essays, Articles and Problem Sets.
website: https://github.com/deedy/Latex-Templates
#---
#src: martinthoma-latex-examples/
#license: None
#title: "More than 570 LaTeX examples"
#description: "More than 570 examples for the usage of LaTeX -- https://github.com/MartinThoma/LaTeX-examples/"
---
id: schymanski-leaf-scale
src: Schymanski_leaf-scale_2016/
license: gpl3
category: misc
title: "Leaf-scale experiments reveal an important omission in the Penman–Monteith equation"
description: |
  Support information for article: http://www.hydrol-earth-syst-sci-discuss.net/hess-2016-363/
---
id: datasci-notebooks
src: data-science-ipython-notebooks/
title: "Data science Python notebooks"
thumbnail: thumbs/datascience-tutorials.png
category: datascience
license: a20
tags:
  - python
website: https://github.com/donnemartin/data-science-ipython-notebooks
description: |
  Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle,
  big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
---
id: beezer-linear-algebra
src: beezer-linear-algebra/
category: math
title: "Sage and Linear Algebra"
description: "This is a collection of classroom-tested worksheets to accompany Beezer's [First Course in Linear Algebra](http://linear.pugetsound.edu/)."
website: http://linear.pugetsound.edu/
author: "Robert A. Beezer"
license: ccbysa
thumbnail: thumbs/sage-lin-alg.png
---
id: paulsen-abstract-algebra
src: paulsen-abstract-algebra/
category: math
title: "Abstract Algebra: An Interactive Approach"
description: "Jupyter notebooks to accompany William Paulsen's [Abstract Algebra: An Interactive Approach, Second Edition](https://www.crcpress.com/Abstract-Algebra-An-Interactive-Approach-Second-Edition/Paulsen/p/book/9781498719766)."
website: https://www.crcpress.com/Abstract-Algebra-An-Interactive-Approach-Second-Edition/Paulsen/p/book/9781498719766
author: "William Paulsen"
license: asis
thumbnail: thumbs/aa-paulsen.png
---
id: ml-with-python
src: introduction_to_ml_with_python/
title: "Introduction to Machine Learning with Python"
author: "Andreas Mueller and Sarah Guido"
description: 'Notebooks and code for the book "Introduction to Machine Learning with Python"'
website: https://github.com/amueller/introduction_to_ml_with_python
thumbnail: thumbs/intro-to-ml.jpg
category: datascience
tags:
  - python
---
id: sci-comp-python
src: scientific-python-lectures/
title: Lectures on scientific computing with Python
author: Robert Johansson
website: https://github.com/jrjohansson/scientific-python-lectures
thumbnail: thumbs/sci-computing-python.png
category: datascience
tags:
  - python
license: ccby
---
id: python-data-science-handbook
category: datascience
src: PythonDataScienceHandbook/
title: Python Data Science Handbook
author: Jake Vanderplas
thumbnail: thumbs/PDSH-cover.png
tags:
  - python
license: mit
---
id: stanford-tensorflow-tutorials
category: datascience
src: stanford-tensorflow-tutorials/
title: Stanford Tensorflow Tutorials
description: >
  This repository contains code examples for the course **CS 20SI: TensorFlow for Deep Learning Research**.
website: http://cs20si.stanford.edu
tags:
  - python
---
id: aaron-tresham-calc
category: math
src: tresham-calculus-worksheets/
title: Aaron Tresham Calculus Materials
author: Aaron Tresham
description: |
  A collection of classroom-tested worksheets to accompany Tresham's [Math 205](http://www2.hawaii.edu/~tresham/Math205%20Lab/math205lab.html) and [Math 206](http://www2.hawaii.edu/~tresham/Math206%20Lab/math206lab.html).

  Files are publicly hosted [here](https://cocalc.com/share/f8df5b36830778dde7b3c3c4e68c542bbaeeefba/Aaron%20Tresham%20Calculus%20Material/?viewer=share).
thumbnail: thumbs/tresham-calc.png
tags:
  - calc
  - sage
license: ccbysa
---
id: statistical-rethinking-python-PyMC3
category: stats
src: statistical-rethinking-python-PyMC3/
title: "Statistical Rethinking with Python and PyMC3"
license: ccby
description: |
  Statistical Rethinking by Richard McElreath is an incredible good introductory book to Bayesian Statistics.
  It follows a *Jaynesian* and practical approach with very good examples and clear explanations.

  In [this repository](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3)
  we ported the codes (originally in R and Stan) in the book to PyMC3.
  We are trying to keep the examples as close as possible to those in the book,
  while at the same time trying to express them in the most Pythonic and PyMC3onic way we can.
---
id: aata
src: aata/
category: math
title: "Abstract Algebra: Theory and Applications"
author: "Tom Judson"
website: "http://abstract.pugetsound.edu/"
thumbnail: thumbs/aata.jpg
tags:
  - sage
description: |
  This open source textbook designed to teach the principles and theory of abstract algebra to college juniors and seniors in a rigorous manner.
  Its strengths include a wide range of exercises, both computational and theoretical, plus many nontrivial applications.
license: gfdl
---
id: cmws
src: cmws/
category: math
tags:
  - sage
title: "Computational Mathematics with SageMath"
author: "Paul Zimmermann et al."
thumbnail: thumbs/cmws.jpg
license: ccbysa
description: |
  A book about the mathematics needed to use Sage efficiently, illustrated by concrete examples. The first part is accessible to high school and undergraduate students. The remainder is suited for graduate students, teachers, and researchers.
website: http://sagebook.gforge.inria.fr/english.html
---
id: math157
src: math157/
category: math
tags:
  - sage
title: "Math 157: Intro to Mathematical Software"
author: "Kiran S. Kedlaya and William Stein"
thumbnail: thumbs/math157.png
license: ccbysa
description: |
  Course materials from *Math 157: Intro to Mathematical Software*, taught by Kiran Kedlaya at UC San Diego during the winter 2018 quarter.
  These were adapted from a similar course (Math 152) taught by Kedlaya at UCSD during winter 2017,
  and ultimately from several courses (Math 480) taught by William Stein at University of Washington.
---
id: think-complexity-2ed
src: think-complexity-2ed/code/
subdir: think-complexity-code
category: science
license: mit
title: Think Complexity
author: Allen B. Downey
description: |
  This is the accompanying code for this book.
  It is primarily about complexity science, but studying complexity science gives you a chance to explore topics and ideas
  you might not encounter otherwise, practice programming in Python, and learn about data structures and algorithms.
tags:
  - python
thumbnail: thumbs/think_complexity_cover.png
website: http://greenteapress.com/wp/think-complexity-2e/
---
id: think-dsp
src: think-dsp/code/
subdir: think-dsp-code
category: physics
title: Think DSP
description: |
  *Think DSP* is an introduction to Digital Signal Processing in Python.

  *This is the accompanying code for this book.*
author: Allen B. Downey
thumbnail: thumbs/think_dsp_cover.jpg
website: http://greenteapress.com/wp/think-dsp/
tags:
  - python
license: gpl3
---
id: think-stats-2ed
src: think-stats-2ed/code/
subdir: think-stats-code
category: stats
title: Think Stats
author: Allen B. Downey
website: "http://greenteapress.com/wp/think-stats-2e/"
thumbnail: thumbs/thinkstats2cover.jpg
description: |
  *Think Stats* is an introduction to Probability and Statistics for Python programmers.

  *This is the accompanying code for this book.*
tags:
  - python
license: gpl3
---
id: think-bayes
src: think-bayes/code/
subdir: think-bayes-code
category: stats
title: Think Bayes
author: Allen B. Downey
website: "http://greenteapress.com/wp/think-bayes/"
description: |
  *Think Bayes* is an introduction to Bayesian statistics using computational methods.

  *This is the accompanying code for this book.*
tags:
  - python
license: gpl3
thumbnail: thumbs/think_bayes_cover_medium.png
---
id: think-python-2ed
src: think-python-2ed/code/
subdir: think-python-code
title: Think Python
author: Allen B. Downey
thumbnail: thumbs/think_python2_medium.jpg
category: cs
description: |
  *Think Python* is an introduction to Python programming for beginners.
  It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used
  and to develop each new concept in a logical progression.

  *This is the accompanying code for this book.*
tags:
  - python
license: ccby
website: "http://greenteapress.com/wp/think-python-2e/"
---
id: think-OS
src: think-OS/code/
subdir: think-OS-code
title: Think OS
author: Allen B. Downey
category: cs
description: |
  *Think OS* is an introduction to Operating Systems for programmers.

  *This is the accompanying code for this book.*
website: http://greenteapress.com/thinkos/
license: ccbysa
---
id: scikit-image-tutorials
src: scikit-image-tutorials/
title: Scikit-Image Tutorials
category: datascience
thumbnail: thumbs/scikit-image.png
website: "https://github.com/scikit-image/skimage-tutorials"
preview: "https://share.cocalc.com/share/bf8bd3b9a338f447bf053411c13165e35d66b9a5/examples/scikit-image-tutorials/?viewer=share"
license: cc0
description: |
  A collection of tutorials for the scikit-image package.
#---
#id: public-finance-2018-2019
#src: public_finance_2018_2019/
#title: "Public Finance 2018/2019 UCSC"
#author: "Duccio Gamannossi degl'Innocenti"
#website: "https://github.com/dgdi/public_finance_2018_2019"
#license: bsd
#category: finance
#tags:
#  - finance
#description: |
#  This repository stores the [SageMath](http://www.sagemath.org/) notebooks for the course
#  Public Finance 2018/2019 at the Catholic University of the Sacred Heart, Milan.
#  Author: [Duccio Gamannossi degl'Innocenti](http://www.dgdi.me)
---
id: nbgrader-demo
title: NBGrader Examples
src: nbgrader-demo/instructor/source/ps1/
subdir: nbgrader-demo
website: "https://github.com/jhamrick/nbgrader-demo"
category: intro
description: |
  A demonstration how to write [NBGrader](https://nbgrader.readthedocs.io/en/stable/) notebooks.
---
id: whirldwind-tour-of-python
src: WhirlwindTourOfPython/
title: "A Whirlwind Tour of Python"
website: "https://www.oreilly.com/library/view/a-whirlwind-tour/9781492037859/"
license: cc0
category: cs
tags:
  - python
thumbnail: thumbs/whirlwind.jpg
description: |
  *A Whirlwind Tour of Python* is a fast-paced introduction to essential
  components of the Python language for researchers and developers who are
  already familiar with programming in another language.

  This repository contains the Jupyter Notebooks behind the O'Reilly report,
  [*A Whirlwind Tour of Python*](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp)
  (free [100-page pdf](http://www.oreilly.com/programming/free/files/a-whirlwind-tour-of-python.pdf)).
---
id: practical-statistics-for-data-scientists
src: practical-statistics-for-data-scientists/
thumbnail: thumbs/practical-statistics-for-data-scientists.jpeg
title: "Code for Practical Statistics for Data Scientists"
website: "https://github.com/gedeck/practical-statistics-for-data-scientists"
license: gpl3
category: datascience
tags:
  - python
description: |
  This contains the code files for the book

  **Practical Statistics for Data Scientists:**  50+ Essential Concepts Using R and Python

  by Peter Bruce, Andrew Bruce, and Peter Gedeck

  Publisher: O'Reilly Media; 2 edition (June 9, 2020)

  Errata: http://oreilly.com/catalog/errata.csp?isbn=9781492072942
---
id: hub
src: Hub/examples/
thumbnail: thumbs/hub_logo.png
title: "Hub: access and manage datasets for PyTorch and TensorFlow"
website: "https://github.com/activeloopai/Hub"
license: mpl2
category: datascience
tags:
  - datascience
  - python
description: |
  The fastest way to access and manage datasets for PyTorch and TensorFlow

  Hub provides fast access to the state-of-the-art datasets for Deep Learning,
  enabling data scientists to manage them,
  build scalable data pipelines and connect to Pytorch and Tensorflow
---
id: mew-cats
title: Mathematical Explorations With Computer Algebra Technology
src: mew_cats/
subdir: mew_cats/
license: ccbysa
thumbnail: mew_cats/graphics/mew_cats/mews_parabola_mouse.png
category: math
tags:
  - intro
  - math
author: John Harris, Karen Kohl, and John Perry