Do it in Keras
Do it in Keras has 3 stars on GitHub!
Modern deep learning architectures and tasks, all implemented in Keras
Have you ever scrolled through reddit, journal posts, deep learning forums or 5 year-old blogs trying to find an example for a task that you want to do?
Is your code not working, even after copy-pasting it from the main source?
Do all of the examples only work on MNIST or CIFAR datasets, making it not possible to use your own dataset?
Well, not anymore! Do it in Keras includes a lot of famous deep learning techniques in the format of Jupyter Notebooks, all done in the Keras deep learning library.
All of these architectures are organized in folders, named with the task they represent. Also, they include a folder with tasks for most of the architectures used.
Motivation
Most of the time I try to implement a new architecture, I have to scroll through a lot of blogs, books and courses trying to understand what's going on in the code. Also, a lot of these tutorials don't specify the library used, or how to use the architecture on custom datasets. That's when Do It In Keras comes in, with simple notebooks that implement various use cases (and datasets) with famous deep learning architectures, including tasks to have a further understanding of the architecture.