In my last post'GooglyPlusPlus now with Win Probability Analysis for all T20 matches' I had discussed the performance of my ML models, created with and without player embeddings, in computing the Win Probability of T20 matches. While the Random Forest gave excellent accuracy, it was bulky and also took an unusually long time to predict the Win Probability of a single T20 match. The above 2 ML models were built using R's Tidymodels. I had initially tried to use Tensorflow, Keras in Python but then abandoned it, since I did not know how to port the Deep Learning model to R and use in my app GooglyPlusPlus. But later, since I was stuck with a bulky Random Forest model, I decided to again explore options for saving the Keras Deep Learning model and loading it in R. I found out that saving the model as .h5, Hence, I rebuilt a Deep Learning model using Keras, Python with player embeddings and I got excellent performance.

Building a cool machine learning project is one thing, but at the end of the day, you want other people to be able to see your hard work. Sure, you could put the whole project on GitHub, but how are your grandparents supposed to figure that out? No, what we want is to deploy our deep learning model as a web application accessible to anyone in the world. In this article, we'll see how to write a web application that takes a trained Keras recurrent neural network and allows users to generate new patent abstracts. This project builds on work from the Recurrent Neural Networks by Example article, but knowing how to create the RNN isn't necessary.

Building a cool machine learning project is one thing, but at the end of the day, you want other people to be able to see your hard work. Sure, you could put the whole project on GitHub, but how are your grandparents supposed to figure that out? No, what we want is to deploy our deep learning model as a web application accessible to anyone in the world. In this article, we'll see how to write a web application that takes a trained Keras recurrent neural network and allows users to generate new patent abstracts. This project builds on work from the Recurrent Neural Networks by Example article, but knowing how to create the RNN isn't necessary.

Keras is a simple and powerful Python library for deep learning. Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk. In this post you will discover how you can save your Keras models to file and load them up again to make predictions. Save and Load Your Keras Deep Learning Models Photo by art_inthecity, some rights reserved. Model weights are saved to HDF5 format.