In this tutorial, you will learn how to train a Convolutional Neural Network (CNN) for regression prediction with Keras. You'll then train a CNN to predict house prices from a set of images. Today's tutorial builds on last week's basic Keras regression example, so if you haven't read it yet make sure you go through it in order to follow along here today. By the end of this guide, you'll not only have a strong understanding of training CNNs for regression prediction with Keras, but you'll also have a Python code template you can follow for your own projects. To learn how to train a CNN for regression prediction with Keras, just keep reading!

Once you choose and fit a final machine learning model in scikit-learn, you can use it to make predictions on new data instances. There is some confusion amongst beginners about how exactly to do this. How do I make predictions with my model in scikit-learn? In this tutorial, you will discover exactly how you can make classification and regression predictions with a finalized machine learning model in the scikit-learn Python library. Gentle Introduction to Vector Norms in Machine Learning Photo by Cosimo, some rights reserved.

Very interesting article.I'd like to add that from a risk management perspective the median is not the only relevant quantile. Here, also best and - especially worst - cases like the 5% and 95% are important. I did not see anything like that in ML yet, but I suppose the information could easily be obtained from the residuals. This quantile information is (still) the most important one for banks and insurers when they calculate figures like Value at Risk and Economic Capital and should be also useful for everyone who wants to minimize risks.