Put AI to Work
April 15-18, 2019
New York, NY

Deep learning with TensorFlow (SOLD OUT)

Dylan Bargteil (The Data Incubator)
Location: Gibson
Average rating: *....
(1.50, 2 ratings)

Who is this presentation for?

  • You're a software engineer or programmer with a background in Python, and you want to develop an understanding of 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.



Prerequisite knowledge

  • Familiarity with Python, matrices, modeling, and statistics
  • No experience with TensorFlow required

What you'll learn

  • Learn what machine learning, neural networks, deep learning, and artificial intelligence are
  • Understand what TensorFlow is and what applications it's good for
  • Create deep learning models for classification and regression using TensorFlow
  • Evaluate the benefits and disadvantages of using TensorFlow over other machine learning software



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
Photo of Dylan Bargteil

Dylan Bargteil

The Data Incubator

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with the Howard Hughes Medical Institute (HHMI). Dylan studied physics and math at the University of Maryland and holds a PhD in physics from New York University.