Deep Learning for Data Scientists

TALK SUMMARY

Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.

In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.

ABOUT THE SPEAKERS
Andrew B. Gardner is the Founder and President of Momentics, Inc., a data science and machine learning consulting & training company. Jake Smith is a Senior Economic Analyst at UPS. Andrew and Jake have been working together on a deep learning stack & hardware appliance for data scientists.

SPEAKER PROFILES
Andrew B. Gardner, http://www.linkedin.com/in/andywocky
Jacob L. Smith, http://www.linkedin.com/in/jacoblsmith

Agenda:

6:30p Pizza, drinks & networking

7:00p Kickoff

7:15p Keynote

8:30p Networking

9:00p Goodnight!