These are slides from a lecture I gave at the School of Applied Sciences in Münster. In this lecture, I talked about Real-World Data Science and showed examples on Fraud Detection, Customer Churn & Predictive Maintenance. Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance) von Shirin Glander The slides were created with xaringan.

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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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On November 7th, Uwe Friedrichsen and I gave our talk from the JAX conference 2018: Deep Learning - a Primer again at the W-JAX in Munich. A few weeks before, I gave a similar talk at two events about Demystifying Big Data and Deep Learning (and how to get started). Here are the two very similar presentations from these talks:

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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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On November 7th, I’ll be in Munich for the W-JAX conference where I’ll be giving the talk that my colleague Uwe Friedrichsen and I gave at the JAX conference this April again: Deep Learning - a Primer. Let me know if any of you here are going to be there and would like to meet up! Slides from the original talk can be found here: https://www.slideshare.net/ShirinGlander/deep-learning-a-primer-95197733 Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lot of inflated expectation, but also quite some impressive success stories.

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Here I am sharing the slides for a webinar I gave for SAP about Explaining Keras Image Classification Models with LIME. Slides can be found here: https://www.slideshare.net/ShirinGlander/sap-webinar-explaining-keras-image-classification-models-with-lime Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It has been written in Python but can also be used from within R.

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Here I am sharing the slides for a talk that my colleague Uwe Friedrichsen and I gave about Deep Learning - a Primer at the JAX conference on Tuesday, April 24th 2018 in Mainz, Germany. Slides can be found here: https://www.slideshare.net/ShirinGlander/deep-learning-a-primer-95197733 Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lot of inflated expectation, but also quite some impressive success stories.

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