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.
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. :-)
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.
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.