As with the other videos from our codecentric.ai Bootcamp (Random Forests, Neural Nets & Gradient Boosting), I am again sharing an English version of the script (plus R code) for this most recent addition on How Convolutional Neural Nets work. In this lesson, I am going to explain how computers learn to see; meaning, how do they learn to recognize images or object on images? One of the most commonly used approaches to teach computers “vision” are Convolutional Neural Nets.

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In a recent video, I covered Random Forests and Neural Nets as part of the codecentric.ai Bootcamp. In the most recent video, I covered Gradient Boosting and XGBoost. You can find the video on YouTube and the slides on slides.com. Both are again in German with code examples in Python. But below, you find the English version of the content, plus code examples in R for caret, xgboost and h2o.

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In my last blogpost about Random Forests I introduced the codecentric.ai Bootcamp. The next part I published was about Neural Networks and Deep Learning. Every video of our bootcamp will have example code and tasks to promote hands-on learning. While the practical parts of the bootcamp will be using Python, below you will find the English R version of this Neural Nets Practical Example, where I explain how neural nets learn and how the concepts and techniques translate to training neural nets in R with the H2O Deep Learning function.

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A few colleagues of mine and I from codecentric.ai are currently working on developing a free online course about machine learning and deep learning. As part of this course, I am developing a series of videos about machine learning basics - the first video in this series was about Random Forests. You can find the video on YouTube but as of now, it is only available in German. Same goes for the slides, which are also currently German only.

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These are the slides from my workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018. The entire code accompanying the workshop can be found below the video. The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o: reading in data exploratory data analysis missingness feature engineering training and test split model training with Random Forests, Gradient Boosting, Neural Nets, etc.

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When looking through the CRAN list of packages, I stumbled upon this little gem: pkgnet is an R library designed for the analysis of R libraries! The goal of the package is to build a graph representation of a package and its dependencies. And I thought it would be fun to play around with it. The little analysis I ended up doing was to compare dependencies of popular machine learning packages.

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Since I migrated my blog from Github Pages to blogdown and Netlify, I wanted to start migrating (most of) my old posts too - and use that opportunity to update them and make sure the code still works. Here I am updating my very first machine learning post from 27 Nov 2016: Can we predict flu deaths with Machine Learning and R?. Changes are marked as bold comments. The main changes I made are:

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