Deep Learning for Data Scientists: Using Apache MXNet and R on AWS – June 2017 – #AWS

Learning Objectives:
– Deploy a Data science environment in minutes with the AWS Deep Learning AMI
– Getting started with Apache MXNet on R
– Train and deploy Deep Learning models at scale with R

Deep Learning (DL) is a subset of Machine Learning (ML) that extends the concept of Artificial Neural Networks (ANN) to uncover hidden patterns in unstructured datasets. Due to the current ubiquity of data (Big Data), and availability of on-demand, inexpensive, and parallel hardware such as Graphics Processing Units (GPUs) on Amazon EC2, Deep Learning has revitalized the excitement in Artificial Intelligence. Breakthrough results can be seen in industry applications such, computer vision, robotics, healthcare, security, retail, and more. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting state-of-the-art deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). MXNet enables Data Scientists familiar with the R programing language to train and deploy deep models at scale, using their favorite language, with the same fast performance observed by Python, Scala or C++ ML practitioners.

You will also hear from Jared P. Lander, adjunct professor of statistics at Columbia University and the organizer of the New York Open Statistical Programming Meetup—the world’s largest R meetup—and the New York R Conference.

Participants will learn how to spin up a pre-built, GPU enabled Data Science environment using the AWS Deep Learning Amazon Machine Image (AMI), in few minutes. We will write a deep learning program with MXNet in a few lines of codes using the R programming language. We will discuss training deep learning models on one or multiple GPUs via R. Finally, we will compare deep models to some traditional Machine Learning models such as Support Vector Machines or Random Forest.

About The Author
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