Deep Learning with Keras and Tensorflow in R

About this course

In this course you will learn how to build powerful convolutional neural networks in R, from scratch. This special kind of deep networks is used to make accurate predictions in various fields of research, either academic or practical.

If you want to use R for advanced tasks like image recognition, face detection or handwriting recognition, this course is the best place to start. It’s a hands-on approach on deep learning in R using convolutional neural networks. All the procedures are explained live, step by step, in every detail.

Most important, you will be able to apply immediately what you will learn, by simply replicating and adapting the code we will be using in the course.

To build and train convolutional neural networks, the R program uses the capabilities of the Python software. But don’t worry if you don’t know Python, you won’t have to use it! All the analyses will be performed in the R environment. I will tell you exactly what to do so you can call the Python functions from R and create convolutional neural networks.

Now let’s take a look at what we’ll cover in this course.

The opening section is meant to provide you with a basic knowledge of convolutional neural networks. We’ll talk about the architecture and functioning of these networks in an accessible way, without getting into cumbersome mathematical aspects. Next, I will give you exact instructions concerning the technical requirements for running the Python commands in R.The main sections of the course are dedicated to building, training and evaluating convolutional neural networks.

We’ll start with two simple prediction problems where the input variable is numeric. These problems will help us get familiar with the process of creating convolutional neural networks.

Afterwards we’ll go to some real advanced prediction situations

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.