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Computer Vision and Self-Supervised Learning

Mastering PyTorch

Registration Now Opened!

Unlock the power of AI with ‘Mastering PyTorch’ course

Course Description

In this course, we will learn about PyTorch, an open-source framework for AI. We will cover everything from the Tensors computations, custom architectures, and advanced functions in PyTorch. We will also cover how to debug your PyTorch codes to make you confident in debugging codes.

We’ve packed in plenty of hands-on activities, homework, and instructor consulting to make learning PyTorch enjoyable and rewarding. You will also be able to tackle real-world problems, from image recognition to natural language processing. By the end of this course, you’ll have the skills and confidence to tackle any machine-learning challenge with PyTorch. This course is one of a series of courses from the Translational AI Center at Iowa State University.

Prerequisite

  • Basic python programming
  • Basic understanding of deep learning

Intended Audience

The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a basic understanding of AI and how to use PyTorch for a broad range of audiences.

  • Learning Outcomes
  • Assessments
  • Course Outline

By the end of the course, you should be able to:

  • Apply PyTorch fundamentals in deep learning and scientific computing.
  • Demonstrate proficiency in debugging PyTorch codes.
  • Develop custom PyTorch layers or functions to address specific tasks.
  • List advanced functionality in PyTorch.
  • Apply PyTorch to solve real-world problems in domains like computer vision and natural language processing.
  • 2 Quizzes to help debug code errors (there will be unlimited attempts)
  • 2 Coding exercise questions which would include implementing python codes based on hands-on activities. This would include coding a custom neural network architecture and exploring some additional exercises.
  • Module 1: Introduction to PyTorch
  • Module 2: Implementing and Debugging PyTorch Codes
  • Module 3: Designing Custom PyTorch Codes
  • Module 4: Advanced PyTorch Functionalities

Course Procedures

The course starts on July 1, 2024. All coursework must be completed before the course ends on August 2 15, 2024. The approximate time to complete this course is 16 hours. You can complete the modules at your own pace.

Live Zoom meetings will be conducted for interactive coding sessions. A suitable time for these live sessions will be determined through a group poll. The recordings of those sessions will be available soon after each meeting.

You will receive a Certificate of Completion upon completing the assessments at the end.

Course Materials

Course materials are provided within the course. No additional purchase is required.

Contact Information

Contact isopd@iastate.edu for more information.

Course Developer

Translational AI Center Wordmark

The Translational AI Center breaks down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. For more information, visit Translational AI Center at Iowa State University

 

Registration Cost: $550.00 USD

Course Hours: 16 hours

Course Start Date: July 1, 2024

Last Day to Register: July 8, 2024

Course End Date: August 2, 2024

Course Access Time: 30 Days

*At the time of registration, you’ll be asked to create an account for this course.

Instructor

photo of Aditya Balu

Aditya Balu, Data Scientist

Aditya Balu is a data scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests are in (i) Geometry-aware scientific machine learning (ii) Distributed and Decentralized Deep Learning (iii) Geometry-aware computational simulation tools.

Aditya also works on several topics in AI and its applications to diverse domains such as healthcare imaging, transportation, manufacturing, design, etc.

As part of TrAC, Aditya has organized several tutorials and workshops at reputed conferences such as CVPR, AAAI, and Supercomputing. He also has several publications in Neurips, ICML, CVPR and AAAI.

Learn more about this instructor