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Deep Learning Fundamentals

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An Interactive Introduction to Artificial Neural Networks and TensorFlow

Jon Krohn

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.

This Deep Learning primer brings the revolutionary machine-learning approach to life with interactive demos from the leading Deep Learning library, TensorFlow, as well as its high-level API, Keras.

To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models.

What you'll learn-and how you can apply it

  • Understand the language and fundamentals of artificial neural networks
  • Straightforwardly build TensorFlow Deep Learning models using the Keras API
  • Interpret the output of Deep Learning models to troubleshoot and improve results

This training course is for you because...

  • You work with data and want to be exposed to the range of applications of Deep Learning approaches.
  • You want to understand how Deep Learning works.
  • You want to create machine-learning models well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets.

Prerequisites

Materials, downloads, or Supplemental Content needed in advance:

  • During class, we’ll work on Jupyter notebooks interactively in the cloud via the Safari JupyterHub platform. A link will be provided at the beginning of class.
  • Alternatively, if you’d like to run this Live Training’s Jupyter notebooks on your own local machine or server, cross-platform step-by-step instructions are available here.

About your instructor

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Segment 1: The Unreasonable Effectiveness of Deep Learning (45 min)

  • Training Overview
  • Introduction to Neural Networks and Deep Learning
  • The Deep Learning Families and Libraries
  • Break + Q&A (5 minutes)

Segment 2: Essential Deep Learning Theory (75 min)

  • The Cart Before the Horse: A Shallow Neural Network in Keras
  • Learning with Artificial Neurons
  • TensorFlow Playground—Visualizing a Deep Net in Action
  • Break + Q&A (5 minutes)

Segment 3: Deep Learning with Keras, TensorFlow’s High-Level API (60 min)

  • Revisiting our Shallow Neural Network
  • Deep Nets in Keras
  • What to Study Next, Depending on Your Interests