The Business of Deep Learning / Coatney, Matt.

Format:

Video

Author/Creator:

Coatney, Matt, author.

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Neural networks (Computer science).

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 3 hr., 29 min.)

Edition:

1st edition

Place of Publication:

Infinite Skills, 2017.

System Details:

video file

Summary:

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 business Matt 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.

Participant:

Presenter, Matt Coatney.

Notes:

Online resource; Title from title screen (viewed February 9, 2017)

Title from title screen (viewed March 3, 2017).

Date of publication from resource description page.

OCLC:

974654497