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Mastering PyTorch

September 1 @ 8:00 am – October 31 @ 5:00 pm

Mastering PyTorch

Master PyTorch, an open-source deep learning framework for AI. This course covers everything from the Tensors computations, custom architectures, and advanced functions in PyTorch. It also covers how to debug PyTorch codes to gain confidence in debugging codes. The course is packed with plenty of hands-on activities, homework, and instructor consulting to make learning PyTorch enjoyable and rewarding. 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.

Course at a Glance

Course Hours: 16 hours

Instructional Period: September 1 – September 28, 2025

Time to Complete Badge: 60 days

Last Dy to Earn Badge: October 31, 2025

Expertise Level: Beginner/Intermediate

This course is part of the Foundational AI track in the TrAC Micro-Credential pathway at Iowa State University.

Foundational AI courses

Mastering PyTorch

End-to-End Computer Vision

End-to-End Natural Language Processing

Generative Models

MLOps

Interpretability in AI

Learn more about TrAC Microcredential Courses!

Prerequisites & intended Audience

Prerequisites:
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.Use this space for describing your block. Any text will do. Description for this block. You can use this space for describing your block.

Learning Outcomes

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

  1. Apply PyTorch to solve real-world problems in domains like computer vision and natural language processing
  2. Apply PyTorch Fundamentals in Deep Learning and Scientific Computing
  3. Debug faulty PyTorch codes using debugging techniques
  4. Develop custom PyTorch layers or functions to address specific tasks

Assessments
  • 2 Quizzes to help debug code errors (unlimited attempts available)
  • 2 Coding exercise questions which include implementing Python codes based on hands-on activities. This includes coding a custom neural network architecture and exploring additional exercises.

Course Outline
  • 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 September 1, 2025. All coursework must be completed by October 31, 2025, in order to earn the micro-credential badge. You will continue to have access to the course materials until January 1, 2026. The approximate time to complete this course is 16 hours.
  • This course has an instructional period from September 1 to September 28, 2025. During this instructional period, course materials will be released weekly, and live synchronous sessions will be held. You may complete the course materials at your own pace. Live Zoom meetings will be conducted for interactive coding sessions and answering any questions you have.
  • You will receive the Mastering PyTorch micro-credential badge upon successful completion of the course assessments.
  • Course Materials:
  • Course materials are provided within the course. No additional purchase is required.

Registration

Students

Register for the course as a 1-credit independent study course with a maximum of 3 such TrAC courses per semester.

Students must register for the independent study by emailing benearl@iastate.edu and cc-ing baditya@iastate.edu. Also, fill this Google form for our records: https://forms.gle/mQRefUJ4qpa29s5w6

Industry Professionals/ISU Staff/Post Docs

$ 500 .00
Register Now

ISU Professionals/Staff and Government Employees: $300

About the Instructor

Zaki Jubery, Research Scientist

Zaki Jubery is a research scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests are in (i) High-throughput phenotyping (ii) Crop modeling (iii) Image processing (iv) Applied machine learning in agriculture.

Zaki works on integrating engineering tools into various agricultural applications. He has been dedicated to pioneering research in this field since September 2013.

Zaki earned his Ph.D. in Mechanical Engineering from Washington State University and completed a postdoctoral fellowship at the University of Illinois Urbana-Champaign. Before transitioning to agriculture, his background includes designing, simulating, and manufacturing point-of-care microfluidics sensors for biomedical and industrial applications.