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), & availability of on-demand, inexpensive, & 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 as computer vision, robotics, healthcare, security, retail, & more. Apache MXNet is a fully-featured, flexibly-programmable & ultra-scalable deep learning framework supporting state-of-the-art deep models including convolutional neural networks (CNNs), & long short-term memory networks (LSTMs). MXNet enables Data Scientists, Programmers, & Engineers to train & deploy deep models at scale, using their favorite language (Python, R, Go, Matlab, Scala, C++, more), with the same fast performance.
At this Meetup, 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 Python & the R programming languages. We will train the deep learning models on one, then multiple GPUs. Finally, we will discuss & compare deep models to some traditional Machine Learning models such as Support Vector Machines or Random Forest, & XGboost.
Registration will be needed when getting to the Loft, but if you would like to register in advance, please use this link: https://pages.awscloud.com/namer_EV_loft-ny-AWSMeetup_Mxnet_2017.html