Here’s an interesting tutorial for Keras and TensorFlow that predicts employee retention.

In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll use this dataset available at Kaggle, which has features that measure employee satisfaction in a company. To create this model, you’ll use the Keras sequential layer to build the different layers for the model.

James McCaffrey recently gave a talk on binary classification in Keras. Here are his thoughts on the topic.

I recently gave a short workshop/talk at the tech company I work for on binary classification using the Keras neural network code library. The goal of a binary classification problem is to predict something that can take on one of just two possible values. For example, you might want […]

Here’s an interesting article on creating and using custom loss functions in Keras. Why would you need to do this?

Here’s one example from the article:

Let’s say you are designing a Variational Autoencoder. You want your model to be able to reconstruct its inputs from the encoded latent space. However, you also want your encoding in the latent space to be (approximately) normally distributed.

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Josh Gordon sits down with J.J. Allaire, the founder of RStudio. They discuss TensorFlow and Keras support in R, and the educational resources available for R developers new to deep learning. Learn more about the R interface to Keras, TensorFlow Estimators, and the Core TensorFlow API that allows the R community access to many machine learning tools.