by Matt Coatney | Released February 2017 | ISBN: 9781491977842

Business executives and entrepreneurs keen on cutting through the hype surrounding deep learning will benefit from this course detailing how top players across a variety of industries deploy deep learning for real world purposes and real world revenues.Concrete examples provide practical guidance on strategy, business models, implementation considerations, privacy and ethics, and evolving trends related to Big Data, data analytics, machine learning, artificial intelligence, and more. With the information included in this course, the strategic decision maker will be able to connect deep learning with their particular industry and business. Understand the core concepts of deep learning and the types of problems it solves Master the distinctions between machine learning, deep learning, and artificial intelligence See how successful businesses practically apply deep learning across a variety of industries Explore deep learning business models, approaches, and revenue streams Participate in exercises that identify how deep learning applies to your businessMatt Coatney is a data scientist, TEDx speaker, entrepreneur, business advisor, and author who has championed the fields of machine learning and AI for more than 20 years. Matt works as the VP of Services for Exaptive, where he focuses on bringing advanced technologies to market in the fields of AI and analytics. He holds an MS in Computer Science from Ohio State University. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Welcome To The Course 00:01:58
   • About The Author 00:02:12
2. Deep Learning In Business
   • Introduction To Machine Learning, Deep Learning And Artificial Intelligence 00:12:30
   • Business Models 00:07:48
   • Introduction To Key Applications 00:07:01
3. Characterizing The World
   • Extracting Features 00:12:04
   • Summarizing Content 00:08:48
   • Tagging Content 00:06:56
   • Advanced Topics On Content 00:08:23
4. Identifying And Grouping Things Together
   • Group Things Together 00:06:36
   • Segmenting (Clustering) 00:11:24
   • Categorizing (Classification) 00:08:50
5. Predicting Outcomes And Behavior
   • Making Recommendations 00:09:49
   • Predicting Behavior/Outcomes 00:08:45
6. Human-Machine Interaction
   • Understanding Human Communication 00:13:53
   • Understanding Abstract Thinking And Emotions 00:11:02
   • Creating Original Work 00:05:25
7. Robotics And Real-World Interaction
   • Sensing The Physical Environment 00:07:43
   • Moving In The Real World 00:06:54
   • Handling Uncertainty 00:07:33
8. Implications
   • Building Buy-In For Ai Projects 00:10:29
   • Addressing Ethical And Privacy Concerns 00:08:47
   • Driving User Acceptance 00:10:16
9. Conclusion
   • Future Applications 00:10:16
   • Wrap Up And Thank You 00:03:54
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