Getting started with PyTorch

Mo Patel (Independent)

1:30pm–5:00pm Tuesday, September 10, 2019

Location: LL21 C/D

Implementing AI

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

Average rating:

(2.67, 3 ratings)

Who is this presentation for?

Data scientists and application developers

Level

Beginner

Description

Since its arrival in January 2017, PyTorch has captured the minds of machine learning researchers and developers. Now two years later, PyTorch has matured into a production-ready machine learning framework with ever-growing examples, use cases, and applications supported by a robust community. Similarly, machine learning and its subdiscipline deep learning have gained immense popularity closely linked to the availability of libraries such as PyTorch and promising results across domains such as computer vision and natural language understanding.

Mo Patel explores PyTorch through several examples. You’ll discover PyTorch through use cases such as image classification, text classification, and regression modeling. Mo leads you through hands-on exploration with transfer learning, working with training and test datasets, and taking PyTorch models to production.

Prerequisite knowledge

Familiarity with Python
A working knowledge of machine learning terms and concepts

Materials or downloads needed in advance

Notebooks for PyTorch labs will be shared via GitHub ready to run on Google Colab environment. Please make sure to have a Google account prior to the tutorial and if you would like you can run through this Google Colab intro notebook, allowing you to test your browser and computer compatibility prior to the conference.

What you'll learn

Learn PyTorch concepts, how to build machine learning models in PyTorch, and working with datasets in PyTorch for machine learning
Understand image classification, text classification, linear modeling, and transfer learning

Mo Patel

Independent

Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.