14–17 Oct 2019

Deep learning with TensorFlow

Michael Cullan (The Data Incubator)
Monday, 14 Oct & Tuesday, 15 Oct,
Location: Park Suite
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Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. Michael Cullan walks you through TensorFlow's capabilities in Python, from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

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

By the end of this two-day training course, you'll understand:

  • What machine learning, neural networks, deep learning, and artificial intelligence are
  • What TensorFlow is and what applications it's good for

And you'll be able to:

  • Create deep learning models for classification and regression using TensorFlow
  • Evaluate the benefits and disadvantages of using TensorFlow over other machine learning software

Who is this presentation for?

  • You're a software engineer or programmer with a background in Python who wants to better understand machine learning.
  • You have experience modeling or have a background in data science, and you want to learn TensorFlow and deep learning.
  • You're in a nontechnical role, and you want to more effectively communicate with the engineers and data scientists in your company about TensorFlow and neural networks.




  • Familiarity with Python, matrices, modeling, and statistics


Day 1

  • Introduction to TensorFlow
  • Iterative algorithms
  • Machine learning
  • Basic neural networks

Day 2

  • Deep neural networks
  • Variational autoencoders
  • Convolutional neural networks
  • Adversarial noise
  • DeepDream
  • Recurrent neural networks

About your instructor

Photo of Michael Cullan

Michael Cullan is a data scientist in residence at the Data Incubator, where he combines a passion for teaching and statistical programming. He has three years of teaching experience in academic and professional settings and four years of research experience spanning topics in nonparametric statistics, applied mathematics, and artificial intelligence. He holds a master’s degree in statistics.

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