AI & Analytics

Neural networks with an application in R/H2O

Aspiring Data Scientists with a wish to deepdive on the most important technologies.

Aimed at:

Delivery method:

Interactive classroom training combining theory with practical exercises


Required preparation:

Knowledge of programming basics (not necessarily in R)

Bring a laptop with Rstudio and H2O library installed

3-4 hours



Skill level:

Prefered group size:

8 participants per trainer (scalable)

Course description

During this training, participants will get an introduction to feed-forward Neural Networks, and gain an understanding of how they work, what their strengths and weaknesses are, and how they can train and utilize neural networks. Starting from exercises on paper, the session builds up to training small networks on practice data sets. As final touch deep learning is applied on a face recognition task using raw images.

Learning objective

Upon completion of this training, participants will be able to decide when a neural network is applicable, how to decide on its number of layers and layer size, and how to train a neural network model.