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:

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.

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.

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.

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.

On April 12th, 2018 I gave a talk about Explaining complex machine learning models with LIME at the Hamburg Data Science Meetup - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/hh-data-science-meetup-explaining-complex-machine-learning-models-with-lime-94218890
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.

On April 4th, 2018 I gave a talk about Deep Learning with Keras at the Ruhr.Py Meetup in Essen, Germany. The talk was not specific to Python, though - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/ruhrpy-introducing-deep-learning-with-keras-and-python
Ruhr.PY - Introducing Deep Learning with Keras and Python von Shirin Glander There is also a video recording of my talk, which you can see here: https://youtu.