5 Computer Vision and Deep Learning Fundamentals. Required to Better Understand How the Latest CV & DL Projects Work
As I was collating my previous list of deep tech projects and then talking about each one, I realized that it might be a good idea to write an easy-to-understand guide that will cover the fundamentals of Computer Vision and Deep Learning so non-technical readers can better understand the latest deep tech projects. The first part will be about image processing basics (old school computer vision techniques that are still relevant today) and then the second part will be deep learning related stuff :)
I personally understand it as the bridge between the virtual world and the physical world as we know it. It is the eyes that we implant on machines for them to “gain consciousness” and navigate our world in intelligent ways that’s never been explored before. It is actually used in:
There’s TONS of other Computer Vision projects that’s going on right now and undoubtedly, Computer Vision applications will proliferate and then seamlessly integrate into our daily lives — changing the way we live as we know it. So without further ado,
Let’s get started! ᕙ(^▿^-ᕙ)
A few compelling reasons for you to starting learning Computer. In today’s world, Computer Vision technologies are everywhere.
Part 1 of the series looked at representation learning and how self-supervised learning can alleviate the problem of data inefficiency in learning representations of images.
Implementations regarding all of above experiments alongside the different result plots are provided in GitHub repository.
Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image.
4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning. Using State-of-the-Art Pre-trained Neural Network Models to Tackle Computer Vision Problems with Transfer Learning