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 email@example.com 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?
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.
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.
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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