Registration is now open for my 1.5-day workshop on how to develop end-2-end from a Keras/TensorFlow model to production. It will take place on February 21st & 22nd in Berlin, Germany. The workshop will cost 950.00 Euro + MwST. We will start at 9 am on Thursday and finish around 3 pm on Friday. Please register by sending an email to shirin.glander@gmail.com with the following information: name company/institute/affiliation address for invoice phone number reference to this blog The course material will be in English and we will speak a mix of German and English, depending on the participants’ preferences.

Continue reading

Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on November 8th & 9th in Munich, Germany. You can read about one participant’s experience in my workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?

Continue reading

Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on July 5th & 6th in Münster, Germany. You can read about one participant’s experience in my last workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?

Continue reading

Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on April 12th and 13th in Hamburg, Germany. You can read about one participant’s experience in my last workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?

Continue reading

I have written another blogpost about Looking beyond accuracy to improve trust in machine learning at my company codecentric’s blog: Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria. Why a model makes the predictions it makes, however, is generally neglected.

Continue reading

Recently, I announced my workshop on Deep Learning with Keras and TensorFlow. The next dates for it are January 18th and 19th in Solingen, Germany. You can register now by following this link: https://www.codecentric.de/schulung/deep-learning-mit-keras-und-tensorflow If any non-German-speaking people want to attend, I’m happy to give the course in English! Contact me if you have further questions. As a little bonus, I am also sharing my sketch notes from a Podcast I listened to when first getting into Keras:

Continue reading

Slides from Münster Data Science Meetup These are my slides from the Münster Data Science Meetup on December 12th, 2017. knitr::include_url("https://shiring.github.io/netlify_images/lime_meetup_slides_wvsh6s.pdf") My sketchnotes were collected from these two podcasts: https://twimlai.com/twiml-talk-7-carlos-guestrin-explaining-predictions-machine-learning-models/ https://dataskeptic.com/blog/episodes/2016/trusting-machine-learning-models-with-lime Sketchnotes: TWiML Talk #7 with Carlos Guestrin – Explaining the Predictions of Machine Learning Models & Data Skeptic Podcast - Trusting Machine Learning Models with Lime Example Code the following libraries were loaded: library(tidyverse) # for tidy data analysis library(farff) # for reading arff file library(missForest) # for imputing missing values library(dummies) # for creating dummy variables library(caret) # for modeling library(lime) # for explaining predictions Data The Chronic Kidney Disease dataset was downloaded from UC Irvine’s Machine Learning repository: http://archive.

Continue reading