Start with Deep Learning & Parameter Tuning with MXnet, H2o Package in R

Introduction

Deep Learning isn't a recent discovery. The seeds were sown back in the 1950s when the first artificial neural network was created. Since then, progress has been rapid, with the structure of the neuron being "re-invented" artificially.

Computers and mobiles have now become powerful enough to identify objects from images.

Not just images, they can chat with you as well! Haven't you tried Google's Allo app ? That's not all—they can drive, make supersonic calculations, and help businesses solve the most complicated problems (more users, revenue, etc).

But, what is driving all these inventions? It's Deep Learning!

With increasing open source contributions, R language now provides a fantastic interface for building predictive models based on neural networks and deep learning. However, learning to build models isn't enough. You ought to understand the interesting story behind them.

In this tutorial, I'll start with the basics of neural networks and deep learning (from scratch). Along with theory, we'll also learn to build deep learning models in R using MXNet and H2O package. Also, we'll learn to tune parameters of a deep learning model for better model performance.

Note: This article is meant for beginners and expects no prior understanding of deep learning (or neural networks).

Table of Contents

1. What is Deep Learning ? How is it different from a Neural Network?
2. How does Deep Learning work ?Why is bias added to the network ?
3. What are activation functions and their types ?
4. Multi Layered Neural NetworksWhat is Backpropagation Algorithm ? How does it work ?
5. Gradient Descent
6. Practical Deep Learning with H2O & MXnet

Go to Tutorial : Deep Learning