In this post you will learn how to build a simple neural network using Tensorflow!
We continue our journey through neural networks and we explore the training stage, when gradient descent allows us to select optimal parameters for the ANN.
This posts introduces the basic concepts about artificial neural networks (ANN).
Introduction In previous posts, I discussed how to deal with situations where you measure a continuous outcome and you want to explain its variability as a function of one or more continuous or discrete variables, using linear regression or mixed-effects models. However, there are situations where these linear models are not the best solution to […]
Pseudoreplication is a major issue in biomedical sciences. This post will show you how to avoid it and properly analyse your data in R!
Linear regression is one of the simplest, yet powerful machine learning techniques. I will teach you how to handle multiple predictors & interactions in R!
Linear regression is one of the simplest, yet extremely powerful statistical techniques, that you definitely want to study in detail. Here we will see how to do it in R!