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The practical guide to launching machine learning initiatives in your organization. 

Author Peter Morgan, CEO of Deep Learning Partnership, takes you through 3  ML  requirements: sophisticated learning algorithms; dedicated hardware; large datasets. 

This in-depth ebook provides practical advice for organizations looking to launch a machine-learning initiative, and explores use cases for 6 industries involved in AI and machine learning today.

Companies with big data strategies have already satisfied one condition, but any organization can jump into machine learning through a variety of open source and proprietary solutions. This ebook guides you through several options.

We live in a time of massive market disruption. On top of the long-running computer revolution, the business world is now faced with artificial intelligence, machine learning, and deep learning—part of the emerging fourth industrial revolution.

Author Peter Morgan is CEO of Deep Learning Partnership, a company that consults and trains on the latest in deep learning and artificial intelligence algorithms and full stack solutions . He runs the Deep Learning Lab meetup group in London. He is also authoring a book on Quantum Computing for Springer and a paper on active inference, a general theory of intelligence, with Professor Karl Friston at the UCL. In a past life, Peter was a theoretical high energy physicist, and a Solutions Architect for companies such as IBM, BT Labs, and Cisco Systems. He enjoys frisbee and golfing. Further information can be found on his LinkedIn profile.

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Discover the Key to Machine Learning Success

  • Use cases including self-driving cars, software development, genomics, blockchains, algorithmic trading & particle physics 
  • Open source datasets and proprietary data sources for organizations that don’t generate their own unique data   
  • A typical data science life cycle, from data collection to production and scale  
  • Examples of commercial off-the-shelf (COTS) and open source machine-learning solutions—and the pros and cons of each    
  • Open source deep learning frameworks such as TensorFlow, MXnet, and PyTorch   
  • AI as a Service providers including AWS, Google Cloud, Azure, and IBM Cloud 
  • Disruptive technologies that are just beginning to emerge 

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