Skymind & Deeplearning4j: Deep Learning for the Enterprise
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The document presents an overview of distributed deep learning technologies and their significance for enterprises, highlighting how they mirror human neural processes to recognize patterns in data. It identifies key stakeholders in deep learning, including CIOs and data scientists, and discusses the competitive edge large tech firms have gained through early adoption of these technologies. Additionally, it outlines various use cases for deep learning across different domains such as image, sound, and text processing, emphasizing its transformative potential for businesses.
Skymind & Deeplearning4j: Deep Learning for the Enterprise
- 1. Distributed Deep Learning For Enterprise Tech Planet - Nov. 3, 2014 Presented by Adam Gibson
- 2. Adam Gibson | Skymind What Are Deep Learning Nets? ● 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 Define
- 3. Adam Gibson | Skymind Who Is Deep Learning For? ● 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. Users
- 4. Brands
- 5. "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. 30 2013
- 6. 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.
- 7. • “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.
- 8. Domains ● IMAGE ○ FACES ○ MACHINE VISION ● SOUND ○ SPEECH TO TEXT ○ MACHINE TRANSLATION ● TEXT ○ SEARCH ○ INFORMATION RETRIEVAL ● TIME SERIES ○ WEATHER, BIODATA, STOCKS
- 9. Adam Gibson | Skymind Faces Learned By Our DL Networks
- 10. Numbers Grouped by Skymind
- 11. Use Case ● Search => Google, Bing, Yahoo ● Information retrieval ● Document clustering ● Text, sound and image search ● Question-Answer ~ Watson
- 12. Use Case ● CRM ● Customer Resource Management ● Churn prediction (SaaS) ● Big spender prediction (gambling) ● Log/behavior analysis ● Sentiment Analysis
- 13. ● 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 Use Case
- 14. ● Enterprise Resource Management/Planning ○ Factories ○ Complex Logistics ○ Personnel Deployments ○ Oil & Gas Use Case
- 15. Back- ground
- 16. 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
- 17. Deep learning ... ● Needs scale to be powerful ● Can identify signal at scale ● Achieves scale with raw data ● And thousands of CPUs or GPUs ● Running in parallel Signal & Scale
- 18. Difference Amount of Data Accuracy Machine Learning Deep Learning Deep Learning Is Setting Records
- 19. 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.
- 20. 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.
- 21. Why Is Deep Learning Hard? We see this… Machines see this… (Hat tip to Andrew Ng) Problem
- 22. Take aways Be careful out there.