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. :-)
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:
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-predictions-of-ml-models/
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.
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 highly flexible because it works with a modular layer system to define, compile and fit neural networks.
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 that a model is good enough for deployment if it passes certain thresholds of these performance criteria.