What Are Deep Learning Nets?

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Dwayne Blankenship
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1 What Are Deep Learning Nets? Define Mathematical models that mirror human neurons Recognizing patterns like images, sounds, verbal sequences Neural nets need those patterns to be translated into numbers Adam Gibson Skymind

2 Who Is Deep Learning For? Users Chief Information Officers looking for the highest performance in machine learning Businesses looking to maximize the value of small data science teams, creating apps and cutting costs. Data scientists and programmers who seek to process huge amounts of unstructured data without laborious feature extraction. Adam Gibson Skymind

3 Brands

4 "The biggest disruptor that we are sure about is the arrival of big data and machine intelligence. This disruption will not only change every business globally, it will also have an important impact on the consumer." Google Chairman Eric Schmidt, Bloomberg TV, Dec

5 First Movers Google hired Geoff Hinton, the pre-eminent deep learning expert, last year to help build their search and ad recommendation engines. Facebook hired Yann LeCun, the no. 2 deep-learning expert, to help build a Facebook feeds that recommend more engaging content. Baidu hired Andrew Ng, a Stanford deep-learning professor, away from Google to lead its research team. Microsoft, Amazon and Netflix all have deep-learning teams working to give customers more of what they want through recommendation and search. Tech giants have monopolized deep learning.

6 A breakthrough in machine learning would be worth 10 Microsofts. - Bill Gates, Microsoft Machine learning is the next Internet. - Tony Tether, DARPA Director Web rankings today are mostly a matter of machine learning. - Prabhakar Raghavan, Yahoo Director of Research Machine learning is going to result in a real revolution. - Greg Papadopoulos, Sun CTO Machine learning is todayʼs discontinuity. - Jerry Yang, Yahoo Deep learning is that breakthrough, and Skymind is bringing it to industry.

7 Domains IMAGE FACES MACHINE VISION SOUND SPEECH TO TEXT MACHINE TRANSLATION TEXT SEARCH INFORMATION RETRIEVAL TIME SERIES

8 Faces Learned By Our DL Networks Adam Gibson Skymind

9 Numbers Grouped by Skymind

10 Use Case Search => Google, Bing, Yahoo Information retrieval Document clustering Text, sound and image search Question-Answer ~ Watson

11 Use Case CRM Customer Resource Management Churn prediction (SaaS) Big spender prediction (gambling) Log/behavior analysis Sentiment Analysis

12 Use Case Recommendation Engine E-commerce => Amazon Advertising => Google, Baidu See: "The Chinese Google Is Making Big Bucks Using AI to Target Ads" --Wired, Oct. 20, 2014

13 Use Case Enterprise Resource Management/Planning Factories Complex Logistics Personnel Deployments Oil & Gas

14 Background

15 Cloud Big data lives in the cloud Nets train on big data for good results Training = computationally intensive Deep learning also lives in the cloud

16 Deep learning... Signal & Scale Needs scale to be powerful Can identify signal at scale Achieves scale with raw data And thousands of CPUs or GPUs Running in parallel

17 Deep Learning Is Setting Records Deep Learning Difference Accuracy Machine Learning Amount of Data

18 Lessons The world is more knowable than ever before. Machines can measure events and human behavior more accurately. Business has not caught up with its own data. Machines can grow smarter with deep learning. Analyzing huge seas of data that no human has ever seen. Knowledge is only power... if you interpret data.

19 Method Deep learning classifies the world. It starts with obvious things (which are not obvious for machines), and extends to subtle things, which are not obvious for humans. Nothing is obvious to machines because they only understand numbers. Houses, men, dogs, cats, trees are all numbers.

20 Why Is Deep Learning Hard? We see this Machines see this Problem (Hat tip to Andrew Ng)

21 Take aways Be careful out there.

22 Contact Adam Gibson Skymind Founder

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders