While developing a product from scratch based on deep learning you always end up asking you this question: "How will I ship and maintain my deep learning models in production?". Given you are a data scientist or a deep learning researcher, maintaining deployed products is by far the…
If you are a data scientist or a deep learning researcher, maintaining deployed products is by far the less exciting part of the process. In this guide, I'll show you how I managed to ship my image super-resolution project with minimal devops and maintenance.…
Introduction In this post we will learn how Unet works, what it is used for and how to implement it. To do so we will use the original Unet paper, Pytorch and a Kaggle competition where Unet was massively used. If you don't know anything about Pytorch, you are afraid…
Here you'll learn how to build Tensorflow either for your x86_64 machine or for the raspberry pi 3 as a standalone shared library which can be interfaced from the C++ API. (This tutorial couldn't be possible without the help of the people from the References section) Watch out for…
Introduction and goal In part 1 we learned how to setup your computer locally for deep learning. Now imagine you're like me and you usually work on your laptop but you still want to use your computer with a powerful GPU whenever you want from anywhere in the world, in…
Introduction and goal Before I jumped into the field of deep learning my first thoughts were about the hardware I would need to run deep learning models. Of course I could have used cloud services such as Amazon AWS GPU instances, but when I saw their pricing I realized that…