Deep Learning Overview

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There are no scheduled dates for this course.

  Available by Request

The Deep Learning Overview course is designed to give you a high-level understanding of what sort of problems deep learning can address and how deep learning can be practically integrated into products and businesses. It offers a minimally technical introduction to AI and deep learning, including: why deep learning has taken off in the past 5 years; how it is similar to – and different from – other kinds of business analytics, predictive analytics, and machine learning; and how to engage with the many free deep learning tools and techniques.

Although the class does not involve hands-on activities, it does include demonstrations of creating, training, and running deep-learning networks. By the end of this course, you will have a complete, high-level understanding of how deep learning works and what it can be useful for.

This course is ideal as a standalone introduction for managers, team leads, marketers, or analysts who want to get a feel for what deep learning is all about.

This course does not require any previous knowledge about deep learning, machine learning, math, or coding.

1 Day/Lecture & Lab

This course is suited to managers, team leads, marketers, or analysts who want a deep learning intro with less math, and less code. It is also suitable for engineers preparing to dive into a more technical class subsequent to this one.

  • What is Deep Learning, and what is it typically used for?
  • Brief history of neural networks and why they are suddenly so popular.
  • What kind of problems deep learning addresses well
  • Problems, pitfalls, and challenges using neural networks and deep learning tools
  • How to determine if deep learning may be helpful for a project or business
  • Choosing the right level to engage: how to explore neural networks without a Ph.D. and without studying research papers
  • Tools and frameworks: what toolkits to look at and how to integrate them with your existing data workflow
  • Hardware: what servers or virtual machines you need (and don't need!) to explore deep learning




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