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.
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)!
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.
I’ll be talking about Deep Learning with Keras in R and Python at the following upcoming meetup: Ruhr.Py 2018 on Wednesday, April 4th Introducing Deep Learning with Keras and Python Keras is a high-level API written in Python for building and prototyping neural networks. It can be used on top of TensorFlow, Theano or CNTK. In this talk we build, train and visualize a Model using Python and Keras - all interactive with Jupyter Notebooks!