14–17 Oct 2019

Deep learning with PyTorch

Rich Ott (The Data Incubator)
Monday, 14 Oct & Tuesday, 15 Oct,
9:00-17:00
Location: Westminster 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.

PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Rich Ott to get the knowledge you need to build deep learning models using real-world datasets and PyTorch.

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

  • Understand PyTorch's tensors and automatic differentiation package
  • Examine different deep learning model architectures
  • Learn to build and train deep neural networks in PyTorch

Who is this presentation for?

  • You're a developer or analyst with some machine learning and Python experience.

Level

Intermediate

Prerequisites:

  • A basic understanding of Python, matrices and linear algebra, modeling and machine learning, and neural networks

Outline

Day 1

  • PyTorch tensors
  • Automatic differentiation package
  • Neural networks
  • Multilayer perceptrons

Day 2

  • Network architectures
  • Convolutional neural network
  • Autoencoders

About your instructor

Photo of Rich Ott

Richard Ott is a data scientist in residence at the Data Incubator, where he combines his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

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