Convolutional Neural Networks - Beyond Basic Architectures
You don't need to deeply understand an architecture to use it effectively in a product.
You can drive a car without knowing whether the engine has 4 or 8 cylinders and what the placement of the valves within the engine is. However - if you want to design and appreciate an engine (computer vision model), you'll want to go a bit deeper. Even if you don't want to spend time designing architectures and want to build products instead, which is what most want to do - you'll find important information in this lesson. You'll get to learn why using outdated architectures like VGGNet will hurt your product and performance, and why you should skip them if you're building anything modern, and you'll learn which architectures you can go to for solving practical problems and what the pros and cons are for each.
If you're looking to apply computer vision to your field, using the resources from this lesson - you'll be able to find the newest models, understand how they work and by which criteria you can compare them and make a decision on which to use.
I'll take you on a bit of time travel - going from 1998 to 2022, highlighting the defining architectures developed throughout the years, what made them unique, what their drawbacks are, and implement the notable ones from scratch. There's nothing better than having some dirt on your hands when it comes to these.
You don't have to Google for architectures and their implementations - they're typically very clearly explained in the papers, and frameworks like Keras make these implementations easier than ever. The key takeaway of this lesson is to teach you how to find, read, implement and understand architectures and papers. No resource in the world will be able to keep up with all of the newest developments. I've included the newest papers here - but in a few months, new ones will pop up, and that's inevitable. Knowing where to find credible implementations, compare them to papers and tweak them can give you the competitive edge required for many computer vision products you may want to build.
By the end - you'll have comprehensive and holistic knowledge of the "common wisdom" throughout the years, why design choices were made and what their overall influence was on the field. You'll learn how to use Keras' preprocessing layers, how KerasCV makes new augmentation accessible, how to compare performance (other than just accuracy) and what to consider if you want to design your own network.
Note: This Guided Project is part of our in-depth course on Practical Deep Learning for Computer Vision and assumes that you've read the previous lessons or have that prerequisite knowledge from before.
What is a Guided Project?
Turn Theory Into Practice
All great learning resources, books and courses teach you the holistic basics, or even intermediate concepts, and advise you to practice after that. As soon as you boot up your own project - the environment suddenly isn't as pristine as in the courses and books! Things go wrong, and it's oftentimes hard to pinpoint even why they do go wrong.
StackAbuse Guided Projects are there to bridge the gap between theory and actual work. We'll respect your knowledge and intelligence, and assume you know the theory. Time to put it into practice.
When applicable, Guided Projects come with downloadable, reusable scripts that you can refer back to whenever required in your new day-to-day work.