Last week I published a blog post about how easy it is to train image classification models with Keras. What I did not show in that post was how to use the model for making predictions. This, I will do here. But predictions alone are boring, so I’m adding explanations for the predictions using the lime package. I have already written a few blog posts (here, here and here) about LIME and have given talks (here and here) about it, too.

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Practical Deep Learning with Rachel Thomas: Sketchnotes from TWiMLAI talk: Practical Deep Learning with Rachel Thomas You can listen to the podcast here. In this episode, i’m joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course.

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I’ve been using keras and TensorFlow for a while now - and love its simplicity and straight-forward way to modeling. As part of the latest update to my Workshop about deep learning with R and keras I’ve added a new example analysis: Building an image classifier to differentiate different types of fruits And I was (again) suprised how fast and easy it was to build the model; it took not even half an hour and only around 100 lines of code (counting only the main code; for this post I added comments and line breaks to make it easier to read)!

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On May 21st and 22nd, I had the honor of having been chosen to attend the rOpenSci unconference 2018 in Seattle. It was a great event and I got to meet many amazing people! rOpenSci rOpenSci is a non-profit organisation that maintains a number of widely used R packages and is very active in promoting a community spirit around the R-world. Their core values are to have open and reproducible research, shared data and easy-to-use tools and to make all this accessible to a large number of people.

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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?

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Systems and Software for Machine Learning at Scale with Jeff Dean: Sketchnotes from TWiMLAI talk #124: Systems and Software for Machine Learning at Scale with Jeff Dean You can listen to the podcast here. In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View.

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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.

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