arogozhnikov.github.io
Brilliantly wrong (blog by Alex Rogozhnikov)Brilliantly Wrong — Alex Rogozhnikov's blog about math, machine learning, programming and high energy physics.
http://arogozhnikov.github.io/
Brilliantly Wrong — Alex Rogozhnikov's blog about math, machine learning, programming and high energy physics.
http://arogozhnikov.github.io/
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Brilliantly wrong (blog by Alex Rogozhnikov) | arogozhnikov.github.io Reviews
https://arogozhnikov.github.io
Brilliantly Wrong — Alex Rogozhnikov's blog about math, machine learning, programming and high energy physics.
arogozhnikov.github.io
Gradient Boosting Interactive Playground
http://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html
Thoughts on science and programming. Gradient Boosting Interactive Playground. Jul 5, 2016 Alex Rogozhnikov. Decision functions of first. Show gradients on hover. This is an interactive demonstration-explanation of gradient boosting. Algorithm applied to classification problem. Boosting takes a decision ('blue' or 'orange') by iteratively building many simpler classification algorithms (decision trees in our case). From some point test loss should increase). Same for depth = 1? That's why some challengin...
Jupyter (IPython) notebooks features
http://arogozhnikov.github.io/2016/09/10/jupyter-features.html
Thoughts on science and programming. Jupyter (IPython) notebooks features. Sep 10, 2016 Alex Rogozhnikov. Jupyter (IPython) notebooks features ¶. It is very flexible tool to create readable analyses, because one can keep code, images, comments, formula and plots together:. Jupyter is quite extensible, supports many programming languages, easily hosted on almost any server — you only need to have ssh or http access to a server. And it is completely free. Sharing notebooks ¶. Convert notebooks to html file.
Interactive demonstrations for ML courses
http://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html
Thoughts on science and programming. Interactive demonstrations for ML courses. Apr 28, 2016 Alex Rogozhnikov. Machine learning becomes more and more popular, and there are now many demonstrations available over the internet which help to demonstrate some ideas about algorithms in a more vivid way. (many of those are java-applets which are not so easy to use). Here are some demos which I find useful:. Demo of ROC curve. And a detailed post. Demos by Andrej Karpathy. Which demonstrates the process of buil...
Gradient Boosting explained [demonstration]
http://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html
Thoughts on science and programming. Gradient Boosting explained [demonstration]. Jun 24, 2016 Alex Rogozhnikov. Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how the gradient boosting algorithm works using several interactive visualizations. ( renewcommand{ vx}{ mathbf{x} ). Semi-transparent target function ( f( vx) ). The greediness of ...
Occam razor vs. machine learning
http://arogozhnikov.github.io/2016/07/12/secret-of-ml.html
Thoughts on science and programming. Occam razor vs. machine learning. Jul 12, 2016 Alex Rogozhnikov. Whenever possible, substitute constructions out of known entities for inferences to unknown entities. Occam's razor (Russell's version). If Russell was studying Machine Learning our days, he’d probably throw out all of the textbooks. The reason is that ML introduces too many terms with subtle or no difference. It’s hard understand the scale of the problem without a good example. Mean squared error (MSE).
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Brilliantly wrong (blog by Alex Rogozhnikov)
Thoughts on science and programming. Dec 19, 2016. Hamiltonian Monte Carlo explained. Sep 10, 2016. Jupyter (IPython) notebooks features. Jul 12, 2016. Occam razor vs. machine learning. Jul 12, 2016. MLHEP 2016 lectures slides. Jul 5, 2016. Gradient Boosting Interactive Playground. Jun 24, 2016. Gradient Boosting explained [demonstration]. May 22, 2016. New item for python lists. Apr 28, 2016. Interactive demonstrations for ML courses. Feb 15, 2016. Testing implementations of LibFM. Feb 9, 2016. Jun 22, ...
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