Deep learning with PyTorch

Rich Ott (The Pragmatic Institute)

Monday, 14 Oct & Tuesday, 15 Oct,
9:00-17:00

Location: Westminster Suite

Implementing AI

Secondary topics: Deep Learning, Deep Learning tools, Machine Learning

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

Richard Ott obtained his PhD in particle physics from the Massachusetts Institute of Technology, followed by postdoctoral research at the University of California, Davis. He then decided to work in industry, taking a role as a data scientist and software engineer at Verizon for two years. When the opportunity to combine his interest in data with his love of teaching arose at The Data Incubator, he joined and has been teaching there ever since.