Introduction
- Introduction
- Motivation for RegNet
Network Design Spaces
- What is a Design Space?
- What is the Structure of a Neural Network?
- The AnyNet Design Space
RegNet
- The RegNet Design Space
- RegNet in Action
What you'll learn
- Define how to design a search space for network architectures
- Describe the AnyNet architecture and how it informs the RegNet design space
- Describe the analysis and findings of the RegNet paper
- Perform image classification using RegNet
Description
In this course, you are going to learn about RegNet architecture.
You'll learn what it is, why it's important, and the novelty that it introduced. With all of the free courses that I put on Udemy, I'm focused more on giving you the intuition and the kind of reasoning into the importance of this paper. If you're looking for mathematical details, I'll link to the paper and additional resources that you can go through. The thing about these free courses is that they're limited to no more than an hour, which means I can only fit so much into this course.
You will learn a lot and you'll walk away with a template for a project that you can use, cuz you will see RedNet inaction using the open-source training library called SuperGradients. It'll provide a great foundation for you to just plug and play your own data sets and do a cool image classification project.
Here's what the agenda is like for this course: Start off by talking about the motivation for RegNet: what it is, why is it that we're embarking on this journey. And then from there you'll learn about design spaces and then we'll get into two specific design spaces, the AnyNet design space and the RegNet design space. I'll link to resources that will give you a deeper understanding of these as well.
The course will wrap up with an overview of the analysis and findings that the researchers discovered in their experiments.
And then finally, we'll see RegNet in action using the SuperGradients training library to perform image classification tasks.
The University of Sheffield
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About the instructors
- 4.45 Calificación
- 2206 Estudiantes
- 3 Cursos
Harpreet Sahota
I create content about deep learning.
Hey, I'm Harpreet.
I'm a data science and deep learning practitioner working in the industry.
Throughout undergrad and grad school I've studied economics, actuarial sciences, statistics, and mathematics.
I've worked as an actuary, biostatistician, and data scientist (senior and lead) in a variety of industries.
I love working in:
• Python ?
• PyTorch ?
• SuperGradients ???
I'm currently working in developer relations and have worked at companies like Comet, Pachyderm and now at Deci AI and absolutely loving it!
I love sharing my knowledge and experience with others, whether it's here, on LinkedIn, Twitter, or in my community Deep Learning Daily?
I also host a podcast called The Artists of Data Science. I've interviewed over 300 people on topics ranging from breaking into data science, how to become a leader in data science, self-improvement, philosophy, production machine learning, and so much more.
Student feedback
4.5