Sep 23–26, 2019

Machine learning from scratch in TensorFlow (Day 2)

Dylan Bargteil (The Data Incubator)
Location: 1E 07
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Who is this presentation for?

  • You're a software engineer or programmer who wants to develop a basic understanding of machine learning.
  • You have experience modeling or have a background in data science, and you want to learn TensorFlow.




The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil explores TensorFlow’s capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow’s Keras API with several hands-on applications.


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

Prerequisite knowledge

  • A working knowledge of Python
  • Familiarity with matrices, modeling, and statistics

What you'll learn

  • Understand machine learning, neural networks, deep learning, and artificial intelligence basic concepts
  • Learn what TensorFlow is and what applications it's good for
  • Discover how to create deep learning models for classification and regression using TensorFlow
  • Evaluate the benefits and disadvantages of using TensorFlow over other machine learning software
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.

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