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The second one will create a 'notebook.pdf' file in the current directory. The notebook generator will add your source code with syntax highlight, additionally you can add .tex files which will be rendered as latex code.

https://github.com/pin3da/notebook-generator Dependencies:

commander : ^2.15.1

latex : 0.0.1

through2 : ^2.0.0

Tags | notebook acm-icpc notebook-generator programming-contests programming-challenges pdf notebook-icpc programming contest |

Implementation | Javascript |

License | Mozilla |

Platform | OS-Independent |

This is a repository for the Stanford ACM-ICPC teams. It currently hosts (a) the team notebook, and (b) complete lecture slides for CS 97SI. The team notebook is compiled from codes written by previous Stanford team members and coaches.

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

bayesian-methods pymc mathematical-analysis jupyter-notebook data-science statisticsThis repository contains the notebook of team viRUs from Reykjavik University. MIT: see the LICENSE file.

competitive-programming library notebook c-plus-plus algorithms data-structuresPython is a modern, robust, high level programming language. It is very easy to pick up even if you are completely new to programming. Mac OS X and Linux comes pre installed with python. Windows users can download python from https://www.python.org/downloads/ .

ipython-notebook tutorialEvery week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something. While these started as opportunities to collectively "raise the tide" on common stumbling blocks in data munging and analysis tasks, they have since grown to machine learning, statistics, and general programming topics. Anything that will help us do our jobs better is fair game.

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages.

notebook analytics data-visualization data-analytics data-discovery data-scienceA template notebook is provided as asl_recognizer.ipynb. The notebook is a combination tutorial and submission document. Some of the codebase and some of your implementation will be external to the notebook. For submission, complete the Submission sections of each part. This will include running your implementations in code notebook cells, answering analysis questions, and passing provided unit tests provided in the codebase and called out in the notebook. This will open the Jupyter Notebook software and notebook in your browser which is where you will directly edit and run your code. Follow the instructions in the notebook for completing the project.

Celiz is a open source Judge System. Judge System is a software for doing judge work in Olympiad Informatics match, and also being used in ACM-ICPC International Collegiate Programming Contest.

Beaker is a code notebook that allows you to analyze, visualize, and document data using multiple programming languages. Beaker's plugin-based polyglot architecture enables you to seamlessly switch between languages in your documents and add support for your favorite languages that we've missed.

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

bayesian-methods deep-learning machine-learning data-science tensorflow neural-networks statistics probabilistic-programmingCMS, or Contest Management System, is a distributed system for running and (to some extent) organizing a programming contest. CMS has been designed to be general and to handle many different types of contests, tasks, scorings, etc. Nonetheless, CMS has been explicitly build to be used in the 2012 International Olympiad in Informatics, held in September 2012 in Italy.

programming-contests programming-competitions grading-systemIt is stable enough for my day to day work, but I can't guarantee the safety for your notebook data. So please make sure you have backup. Emacs IPython Notebook (EIN) provides a IPython Notebook client and integrated REPL (like SLIME) in Emacs. While EIN makes notebook editing very powerful by allowing you to use any Emacs features, it also expose IPython features such as code evaluation, object inspection and code completion to the Emacs side. These features can be accessed anywhere in Emacs and improve Python code editing and reading in Emacs.

The dashboards layout extension is an add-on for Jupyter Notebook. It lets you arrange your notebook outputs (text, plots, widgets, ...) in grid- or report-like layouts. It saves information about your layouts in your notebook document. Other people with the extension can open your notebook and view your layouts. For a sample of what's possible with the dashboard layout extension, have a look at the demo dashboard-notebooks in this repository.

dashboard jupyter jupyter-notebook ipython dashboardsFor students not having access to canvas as yet, HW 0 is cs109a_hw0.ipynb in this folder. The due date is Sep 8th, 11:59PM. Registered students should upload both a notebook and a pdf produced from the notebook (use the browser print function) to canvas. Students who are not yet registered, such as MIT students, should email cs109a2017@gmail.com with these two files attached. The Lab and Lecture material can be accessed from the respective folders.

This repository contains the Jupyter Notebooks behind my O'Reilly report, A Whirlwind Tour of Python (free 100-page pdf). 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.

Do you use Vim? And you need to use Jupyter Notebook? This is a Jupyter Notebook (formerly known as IPython Notebook) extension to enable Vim like environment powered by CodeMirror's Vim. I'm sure that this plugin helps to improve your QOL. While I changed my job, I don't use jupyter notebook and I can't make enough time to maintain this plugin.

jupyter-notebook vim-mode codemirror-vim jupyter-vim-bindingPapermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. To parameterize your notebook designate a cell with the tag parameters.

jupyter notebooks notebook-generator nteract publishing pipelineDeep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearningThis project contains pytudes—Python programs for perfecting programming skills. I got the idea for the "etudes" part of the name from this 1978 book by Charles Wetherell that was very influential to me when I was first learning to program.

python-3 programming practice demonstrate-skillsThis repository contains the slides and exercises for the Deep Learning Summer School 2015 programming tutorials. The tutorials are written in Python, using Theano and Fuel. They are designed to be run locally on a laptop, without using a GPU.

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