Publications by Dr. Shirin Glander
Image clustering with Keras and k-Means
A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. It is written in Python, though �...
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Using R to help plan the future of transport. Join MünsteR for our next meetup!
In our next MünsteR R-user group meetup on Tuesday, November 20th, 2018, titled Using R to help plan the future of transport, Mark Padgham will provide an overview of several inter-related R packages for analysing urban dynamics. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f The primary motivation for developing these packages has been the...
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Sketchnotes from TWiML&AI: Evaluating Model Explainability Methods with Sara Hooker
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Evaluating Model Explainability Methods with Sara Hooker: Sketchnotes from TWiMLAI talk: Evaluating Model Explainability Methods with Sara Hooker You can listen to the podcast here. In this, the first episode of the Deep Learning Indaba series, ...
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Slides from my m-cubed talk about Explaining complex machine learning models with LIME
The last two days, I was in London for the M-cubed conference. Here are the slides from my talk about Explaining complex machine learning models with LIME: 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...
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Slides from my talk at the R-Ladies Meetup about Interpretable Deep Learning with R, Keras and LIME
During my stay in London for the m3 conference, I also gave a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and LIME. Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and ...
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Machine Learning Basics – Random Forest
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,...
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‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.
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 versi...
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TWIMLAI European Online Meetup about Trust in Predictions of ML Models
At the upcoming This week in machine learning and AI European online Meetup, I’ll be presenting and leading a discussion about the Anchors paper, the next generation of machine learning interpretability tools. Come and join the fun! 🙂 Date: Tuesday 4th December 2018 Time: 19:00 PM CET/CEST Join: https://twimlai.com/meetups/trust-in-predict...
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Slides from my talks about Demystifying Big Data and Deep Learning (and how to get started)
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: Related To leav...
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Machine Learning Basics – Gradient Boosting & XGBoost
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 c...
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