This spring, I’ll be giving talks at a couple of Meetups and conferences: March, 26th: At the data lounge Bremen, I’ll be talking about Explainable Machine Learning April, 11th: At the Data Science Meetup Bielefeld, I’ll be talking about Building Interpretable Neural Networks with Keras and LIME May, 14th: At the M3 conference in Mannheim, a colleague and I will give our workshop on building production-ready machine learning models with Keras, Luigi, DVC and TensorFlow Serving
In our next MünsteR R-user group meetup on Tuesday, April 9th, 2019, we will have two exciting talks: Getting started with RMarkdown and Trying to make it in the world of Kaggle! You can RSVP here: http://meetu.ps/e/Gg5th/w54bW/f Getting started with RMarkdown First, Niklas Wulms from the University Hospital, Münster will give an introduction to RMarkdown: He started using R in 2018 and learnt the advantages of using only one framework of free software and code.
Today, I am happy to announce the launch of our codecentric.AI Bootcamp! This bootcamp is a free online course for everyone who wants to learn hands-on machine learning and AI techniques, from basic algorithms to deep learning, computer vision and NLP. However, the course language is German only, but for every chapter I did, you will find an English R-version here on my blog (see below for links). Right now, the course is in beta phase, so we are happy about everyone who tests our content and leaves feedback.
Disclaimer: Manning publications gave me the ebook version of Beyond Spreadsheets with R - A beginner’s guide to R and RStudio by Dr. Jonathan Carroll free of charge. Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio.
In the past, I have written and taught quite a bit about image classification with Keras (e.g. here). Text classification isn’t too different in terms of using the Keras principles to train a sequential or function model. You can even use Convolutional Neural Nets (CNNs) for text classification. What is very different, however, is how to prepare raw text data for modeling. When you look at the IMDB example from the Deep Learning with R Book, you get a great explanation of how to train the model.
I am very happy to announce that (after many months) my interactive course on Hyperparameter Tuning in R has now been officially launched on Data Camp! Course Description For many machine learning problems, simply running a model out-of-the-box and getting a prediction is not enough; you want the best model with the most accurate prediction. One way to perfect your model is with hyperparameter tuning, which means optimizing the settings for that specific model.
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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