The field of deep learning is vast. The sheer number of publications on the subject is enough to overwhelm anyone. In this series we’ll be taking a step back. We’ll forget about the latest tips and tricks that are pushing the state of the art. Instead, through the use of simple datasets and toy problems, we’ll explore the fundamentals of deep learning to give you a better understanding of the big picture.
This series currently contains the following posts:
Most open source datasets for computer vision are huge and complex. Building a model from scratch using ImageNet or Coco is impossible without days of traini...
This post is about semantic segmentation. This is the task of assigning a label to each pixel of an images. It can be seen as an image classification task, e...
In this post, I will describe a python project structure that I have found extremely useful over a wide variety of projects. We’re going to build this struct...
Python logging isn’t easy. When I was learning python, I made many attempts to use logging in my applications. Usually I would end up frustrated and thinking...
