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Keep the gradient flowing

Policy Gradients Part 1: The REINFORCE Estimator On the Link Between Optimization and Polynomials, Part 6. Optimization Nuggets: Stochastic Polyak Step-size, Part 2 Optimization Nuggets: Stochastic Polyak Step-size On the Convergence of the Unadjusted Langevin Algorithm The Russian Roulette: An Unbiased Estimator of the Limit Notes on the Frank-Wolfe Algorithm, Part III: backtracking line-search On the Link Between Optimization and Polynomials, Part 5 Optimization Nuggets: Implicit Bias of Gradient-based Methods Optimization Nuggets: Exponential Convergence of SGD On the Link Between Optimization and Polynomials, Part 4 On the Link Between Optimization and Polynomials, Part 3 On the Link Between Optimization and Polynomials, Part 2 On the Link Between Polynomials and Optimization, Part 1 How to Evaluate the Logistic Loss and not NaN trying Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis Three Operator Splitting Notes on the Frank-Wolfe Algorithm, Part I Optimization inequalities cheatsheet A fully asynchronous variant of the SAGA algorithm Hyperparameter optimization with approximate gradient Lightning v0.1 scikit-learn-contrib, an umbrella for scikit-learn related projects. SAGA algorithm in the lightning library On the consistency of ordinal regression methods Holdout cross-validation generator PyData Paris - April 2015 Data-driven hemodynamic response function estimation Plot memory usage as a function of time Surrogate Loss Functions in Machine Learning
IPython/Jupyter notebook gallery
Fabian Pedregosa · 2015-04-21 · via Keep the gradient flowing

Due to lack of time and interest, I'm no longer maintaining this project. Feel free to grab the sources from https://github.com/fabianp/nbgallery and fork the project.

TL;DR I created a gallery for IPython/Jupyter notebooks. Check it out :-)

Notebook gallery

A couple of months ago I put online a website that displays a collection of IPython/Jupyter notebooks. The is a website that collects user-submitted and publicly available notebooks and displays them with a nice screenshot. The great thing about this website compared to other similar efforts is that this one updates and categorizes the notebooks (by date and views) automatically. You can even search for anything in this database that contains already more than 400 notebooks!

Vision

I would like to make a website where it is possible to

  1. Find the notebook you are looking for. There is precious information (examples, documentation, tutorials, etc.) contained within IPython/Jupyter notebooks. It should be easy to find this information.

  2. Discover new notebooks. I would like to see new notebooks as they are submitted in order to discover new notebooks, possibly about new technologies that I did not know about.

How it works

Under the hood there's a django app, for which the source code lives here (don't hesitate to use the issues feature in github to suggest features or to report bugs). To propose new notebooks there's a tab where anyone can leave the URL of a notebook. If all goes right, the django app will take a screenshot will be taken and incorporated into the database. The only non-trivial part of this process is to take the screenshot, for which I use a bit of javascript around phantomjs.

Future plans

What you see is just the beginning!. As time permits I would like to implement user authentication so that registered users can bookmark their favorite notebooks, up and down-vote notebooks etc. Cathegorization of notebooks (e.g. in bins such as Math, Phisics, Machine Learning, R, Python, Julia, etc.) is also high on my list. Leave me a comment if you would like to see some specific feature!