Workshop material Because this year’s UseR 2020 couldn’t happen as an in-person event, I have been giving my workshop on Deep Learning with Keras and TensorFlow as an online event on Thursday, 8th of October. You can now find the full recording of the 2-hour session on YouTube and the notebooks with code on Gitlab. If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):
I have been working with Keras for a while now, and I’ve also been writing quite a few blogposts about it; the most recent one being an update to image classification using TF 2.0. However, in my blogposts I have always been using Keras sequential models and never shown how to use the Functional API. The reason is that the Functional API is usually applied when building more complex models, like multi-input or multi-output models.
Recently, I have been getting a few comments on my old article on image classification with Keras, saying that they are getting errors with the code. And I have also gotten a few questions about how to use a Keras model to predict on new images (of different size). Instead of replying to them all individually, I decided to write this updated version using recent Keras and TensorFlow versions (all package versions and system information can be found at the bottom of this article, as usual).
In the past, I have written and taught quite a bit about image classification with Keras (e.g. here). Text classification isn’t too different in terms of using the Keras principles to train a sequential or function model. You can even use Convolutional Neural Nets (CNNs) for text classification. What is very different, however, is how to prepare raw text data for modeling. When you look at the IMDB example from the Deep Learning with R Book, you get a great explanation of how to train the model.
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 firstname.lastname@example.org 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.
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 - so I adapted the code to R. You find the results below.
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?
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