Description

Book Synopsis
From humble evolutions in research papers and labs, artificial intelligence (AI)—which encompasses Machine Learning (ML) and Deep Learning (DL)—has matured in its many forms, infused in applications that can learn on their own and become progressively smarter with each interaction and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning algorithms, and cloud technologies, operational AI has become available to the masses, setting the wheels in motion for a worldwide AI revolution that has never been seen before. This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; how AI is being used across industries; possible future outlook for AI, and an AI Ladder to help accelerate the AI journey.

Artificial Intelligence: Evolution and Revolution

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    A Paperback by Steven Astorino, Mark Simmonds

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      Publisher: MC Press, LLC
      Publication Date: Publication Date: 01/10/2019
      ISBN13: 9781583478929, 978-1583478929
      ISBN10:
      Also in:
      Computing Artificial intelligence (AI) Expert systems / knowledge-based systems

      Description

      Book Synopsis
      From humble evolutions in research papers and labs, artificial intelligence (AI)—which encompasses Machine Learning (ML) and Deep Learning (DL)—has matured in its many forms, infused in applications that can learn on their own and become progressively smarter with each interaction and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning algorithms, and cloud technologies, operational AI has become available to the masses, setting the wheels in motion for a worldwide AI revolution that has never been seen before. This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; how AI is being used across industries; possible future outlook for AI, and an AI Ladder to help accelerate the AI journey.

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