Shoutout to Stanford CS230 course for making their resources available online for free.
Ever wondered how one should manage a Machine learning or a Deep learning project, without randomly creating arbitrary folders on the go while working on an awesome project.
Worry not, this log post has a nice info-graphic and images, which show the folder hierarchy of how a project structure is maintained.
The base fundamental is the same and this structure is suggested by the Stanford University CS230 course taught by Andrew Ng and should scale pretty well on any DL/ML project that you are working on like Natural Language Processing, Computer Vision, Reinforcement learning, Speech recognition.
Folder Hierarchy
The info-graphic that follows after this explains in short what exactly are stored within the folders and what are the functions of the python files show in this image below.
Which should look like this on our local systems.
Infographic
Hope this gave a clear idea on how to structure your code in a more organized manner and save out your time on the next Deep Learning/Machine learning project that you undertake :)