📚 The CoCalc Library - books, templates and other resources
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# This defines what the user sees in the library dialog -- smc-webapp/library.cjsx1# see index.py for more details.2---3# an entry can have a list of tags4tags:5calc:6name: Calculus7linalg:8name: Linear Algebra9stats:10name: Statistics11intro:12name: Introduction13python:14name: Python15info: "Make sure to pick a suitable Python environment (Python 2, Python 3, Anaconda, ...)"16math:17name: Mathematics18julia:19name: Julia20info: "Be patient. Starting Julia the first time might require to recompile some modules."21finance:22name: Finance23# an entry can have exactly one license24licenses:25a20: Apache 2.026asis: AS IS27bsd: BSD28cc0: CC0 1.0 Universal29ccby: CC BY 4.030ccbysa: CC BY-SA31ccbync3: CC BY-NC 3.032mit: MIT33gpl3: GPLv334gfdl: GNU Free Documentation License35# each entry must have exactly one category36# weight: additional weighting for sorting by category – default 037categories:38intro:39name: Introduction40weight: -141stats:42name: Statistics43cs:44name: Computer science45datascience:46name: Data science47math:48name: Mathematics49physics:50name: Physics51chemistry:52name: Chemistry53latex:54name: LaTeX templates55science:56name: Science57misc:58name: Miscellaneous59weight: 160finance:61name: Finance62references:63- cocalc-example-files/index.yaml64---65id: vqe-playground66title: "Variational Quantum Eigensolver Playground"67src: vqe-playground/68license: a2069thumbnail: thumbs/vqe-playground.png70category: physics71description: >72Variational Quantum Eigensolver: gaining intuition about Variational Quantum Eigensolver.73(based on [Quiskit](https://qiskit.org/), the "Quntum Information Science Kit")74website: "https://github.com/JavaFXpert/vqe-playground"75---76id: cocalc-first-steps77title: First Steps in CoCalc78src: first-steps/src/79subdir: first-steps80license: a2081thumbnail: thumbs/cocalc-first-steps.png82category: intro83tags:84- intro85description: |86Quick introduction to CoCalc interface and basic functionalities.87---88id: "bayes-for-hackers"89title: "Bayesian Methods for Hackers"90src: bayesian-methods-for-hackers/91thumbnail: thumbs/bayesian.png92description: |93Hands-on tutorial how Bayesian Methods work, based on Python's PyMC library.94author: Cameron Davidson-Pilon95license: asis96category: stats97tags:98- stats99- python100---101id: cmichi-latex-templates102src: cmichi-latex-templates/103title: "Michael Müller's LaTeX Templates"104thumbnail: thumbs/muller-latex.png105category: latex106license: mit107description: |108Collection of different LaTeX/XeTeX templates (cv, invoices, timesheets, letters, etc.).109website: "https://github.com/cmichi/latex-template-collection"110---111id: deedy-latex-templates112src: deedy-latex-templates/113title: "Deedy LaTeX Templates"114thumbnail: thumbs/deedy-latex.png115license: cc0116category: latex117description: |118A concise set of Latex templates that serves a small set of needs - CV, Essays, Articles and Problem Sets.119website: https://github.com/deedy/Latex-Templates120#---121#src: martinthoma-latex-examples/122#license: None123#title: "More than 570 LaTeX examples"124#description: "More than 570 examples for the usage of LaTeX -- https://github.com/MartinThoma/LaTeX-examples/"125---126id: schymanski-leaf-scale127src: Schymanski_leaf-scale_2016/128license: gpl3129category: misc130title: "Leaf-scale experiments reveal an important omission in the Penman–Monteith equation"131description: |132Support information for article: http://www.hydrol-earth-syst-sci-discuss.net/hess-2016-363/133---134id: datasci-notebooks135src: data-science-ipython-notebooks/136title: "Data science Python notebooks"137thumbnail: thumbs/datascience-tutorials.png138category: datascience139license: a20140tags:141- python142website: https://github.com/donnemartin/data-science-ipython-notebooks143description: |144Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle,145big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.146---147id: beezer-linear-algebra148src: beezer-linear-algebra/149category: math150title: "Sage and Linear Algebra"151description: "This is a collection of classroom-tested worksheets to accompany Beezer's [First Course in Linear Algebra](http://linear.pugetsound.edu/)."152website: http://linear.pugetsound.edu/153author: "Robert A. Beezer"154license: ccbysa155thumbnail: thumbs/sage-lin-alg.png156---157id: paulsen-abstract-algebra158src: paulsen-abstract-algebra/159category: math160title: "Abstract Alebra: An Interactive Approach"161description: "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)."162website: https://www.crcpress.com/Abstract-Algebra-An-Interactive-Approach-Second-Edition/Paulsen/p/book/9781498719766163author: "William Paulsen"164license: asis165thumbnail: thumbs/aa-paulsen.png166---167id: ml-with-python168src: introduction_to_ml_with_python/169title: "Introduction to Machine Learning with Python"170author: "Andreas Mueller and Sarah Guido"171description: 'Notebooks and code for the book "Introduction to Machine Learning with Python"'172website: https://github.com/amueller/introduction_to_ml_with_python173thumbnail: thumbs/intro-to-ml.jpg174category: datascience175tags:176- python177---178id: sci-comp-python179src: scientific-python-lectures/180title: Lectures on scientific computing with Python181author: Robert Johansson182website: https://github.com/jrjohansson/scientific-python-lectures183thumbnail: thumbs/sci-computing-python.png184category: datascience185tags:186- python187license: ccby188---189id: python-data-science-handbook190category: datascience191src: PythonDataScienceHandbook/192title: Python Data Science Handbook193author: Jake Vanderplas194thumbnail: thumbs/PDSH-cover.png195tags:196- python197license: mit198---199id: stanford-tensorflow-tutorials200category: datascience201src: stanford-tensorflow-tutorials/202title: Stanford Tensorflow Tutorials203description: >204This repository contains code examples for the course **CS 20SI: TensorFlow for Deep Learning Research**.205website: http://cs20si.stanford.edu206tags:207- python208---209id: aaron-tresham-calc210category: math211src: tresham-calculus-worksheets/212title: Aaron Tresham Calculus Materials213author: Aaron Tresham214description: |215A 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).216217Files are publicly hosted [here](https://cocalc.com/share/f8df5b36830778dde7b3c3c4e68c542bbaeeefba/Aaron%20Tresham%20Calculus%20Material/?viewer=share).218thumbnail: thumbs/tresham-calc.png219tags:220- calc221- sage222license: ccbysa223---224id: statistical-rethinking-python-PyMC3225category: stats226src: statistical-rethinking-python-PyMC3/227title: "Statistical Rethinking with Python and PyMC3"228license: ccby229description: |230Statistical Rethinking by Richard McElreath is an incredible good introductory book to Bayesian Statistics.231It follows a *Jaynesian* and practical approach with very good examples and clear explanations.232233In [this repository](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3)234we ported the codes (originally in R and Stan) in the book to PyMC3.235We are trying to keep the examples as close as possible to those in the book,236while at the same time trying to express them in the most Pythonic and PyMC3onic way we can.237---238id: aata239src: aata/240category: math241title: "Abstract Algebra: Theory and Applications"242author: "Tom Judson"243website: "http://abstract.pugetsound.edu/"244thumbnail: thumbs/aata.jpg245tags:246- sage247description: |248This open source textbook designed to teach the principles and theory of abstract algebra to college juniors and seniors in a rigorous manner.249Its strengths include a wide range of exercises, both computational and theoretical, plus many nontrivial applications.250license: gfdl251---252id: cmws253src: cmws/254category: math255tags:256- sage257title: "Computational Mathematics with SageMath"258author: "Paul Zimmermann et al."259thumbnail: thumbs/cmws.jpg260license: ccbysa261description: |262A 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.263website: http://sagebook.gforge.inria.fr/english.html264---265id: math157266src: math157/267category: math268tags:269- sage270title: "Math 157: Intro to Mathematical Software"271author: "Kiran S. Kedlaya and William Stein"272thumbnail: thumbs/math157.png273license: ccbysa274description: |275Course materials from *Math 157: Intro to Mathematical Software*, taught by Kiran Kedlaya at UC San Diego during the winter 2018 quarter.276These were adapted from a similar course (Math 152) taught by Kedlaya at UCSD during winter 2017,277and ultimately from several courses (Math 480) taught by William Stein at University of Washington.278---279id: think-complexity-2ed280src: think-complexity-2ed/code/281subdir: think-complexity-code282category: science283license: mit284title: Think Complexity285author: Allen B. Downey286description: |287This is the accompanying code for this book.288It is primarily about complexity science, but studying complexity science gives you a chance to explore topics and ideas289you might not encounter otherwise, practice programming in Python, and learn about data structures and algorithms.290tags:291- python292thumbnail: thumbs/think_complexity_cover.png293website: http://greenteapress.com/wp/think-complexity-2e/294---295id: think-dsp296src: think-dsp/code/297subdir: think-dsp-code298category: physics299title: Think DSP300description: |301*Think DSP* is an introduction to Digital Signal Processing in Python.302303*This is the accompanying code for this book.*304author: Allen B. Downey305thumbnail: thumbs/think_dsp_cover.jpg306website: http://greenteapress.com/wp/think-dsp/307tags:308- python309license: gpl3310---311id: think-stats-2ed312src: think-stats-2ed/code/313subdir: think-stats-code314category: stats315title: Think Stats316author: Allen B. Downey317website: http://greenteapress.com/wp/think-stats-2e/318thumbnail: thumbs/thinkstats2cover.jpg319description: |320*Think Stats* is an introduction to Probability and Statistics for Python programmers.321322*This is the accompanying code for this book.*323tags:324- python325license: gpl3326---327id: think-bayes328src: think-bayes/code/329subdir: think-bayes-code330category: stats331title: Think Bayes332author: Allen B. Downey333website: http://greenteapress.com/wp/think-bayes/334description: |335*Think Bayes* is an introduction to Bayesian statistics using computational methods.336337*This is the accompanying code for this book.*338tags:339- python340license: gpl3341thumbnail: thumbs/think_bayes_cover_medium.png342---343id: think-python-2ed344src: think-python-2ed/code/345subdir: think-python-code346title: Think Python347author: Allen B. Downey348thumbnail: thumbs/think_python2_medium.jpg349category: cs350description: |351*Think Python* is an introduction to Python programming for beginners.352It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used353and to develop each new concept in a logical progression.354355*This is the accompanying code for this book.*356tags:357- python358license: ccby359website: http://greenteapress.com/wp/think-python-2e/360---361id: think-OS362src: think-OS/code/363subdir: think-OS-code364title: Think OS365author: Allen B. Downey366category: cs367description: |368*Think OS* is an introduction to Operating Systems for programmers.369370*This is the accompanying code for this book.*371website: http://greenteapress.com/thinkos/372license: ccbysa373---374id: scikit-image-tutorials375src: scikit-image-tutorials/376title: Scikit-Image Tutorials377category: datascience378thumbnail: thumbs/scikit-image.png379website: https://github.com/scikit-image/skimage-tutorials380license: cc0381description: |382A collection of tutorials for the scikit-image package.383---384id: public-finance-2018-2019385src: public_finance_2018_2019/386title: "Public Finance 2018/2019 UCSC"387author: "Duccio Gamannossi degl'Innocenti"388website: https://github.com/dgdi/public_finance_2018_2019389license: bsd390category: finance391tags:392- finance393description: |394This repository stores the [SageMath](http://www.sagemath.org/) notebooks for the course395Public Finance 2018/2019 at the Catholic University of the Sacred Heart, Milan.396397Author: [Duccio Gamannossi degl'Innocenti](http://www.dgdi.me)398---399id: nbgrader-demo400title: NBGrader Examples401src: nbgrader-demo/instructor/source/ps1/402subdir: nbgrader-demo403website: https://github.com/jhamrick/nbgrader-demo404category: intro405description: |406A demonstration how to write [NBGrader](https://nbgrader.readthedocs.io/en/stable/) notebooks.407408409