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Machine Learning Is Changing the Rules
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. This in-depth ebook provides practical advice for organizations looking to launch a machine-learning initiative, and explores use cases for six industries involved in AI and machine learning today.
Author Peter Morgan, CEO of Data Science Partnership, takes you through three primary requirements for machine learning: sophisticated learning algorithms, dedicated hardware, and large datasets. 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.
You’ll explore:
How machine learning is transforming healthcare, finance, transportation, computer technology, energy, and science
Use cases including self-driving cars, software development, genomics, blockchains, algorithmic trading, particle physics, and data center energy management
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 Platform, Azure, and IBM Cloud
Disruptive technologies that are just beginning to emerge
Author Peter Morgan, CEO of Data Science Partnership, takes you through three primary requirements for machine learning: sophisticated learning algorithms, dedicated hardware, and large datasets. 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.
You’ll explore:
How machine learning is transforming healthcare, finance, transportation, computer technology, energy, and science
Use cases including self-driving cars, software development, genomics, blockchains, algorithmic trading, particle physics, and data center energy management
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 Platform, Azure, and IBM Cloud
Disruptive technologies that are just beginning to emerge
49 pages, ebook
First published April 1, 2018
19 people want to read
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Displaying 1 of 1 review
September 29, 2018
Very short introduction to machine learning - more of a research paper with lots of references. Good introduction to possibilities, tools and products.
Displaying 1 of 1 review