Number of deep learning toolbox updates
Latest activity Reply by Mike Croucher
on 29 Jan 2025
What is the side-effect of counting the number of Deep Learning Toolbox™ updates in the last 5 years? The industry has slowly stabilised and matured, so updates have slowed down in the last 1 year, and there has been no exponential growth.Is it correct to assume that? Let's see what you think!
releaseNumNames = "R"+string(2019:2024)+["a";"b"];
releaseNumNames = releaseNumNames(:);
numReleaseNotes = [10,14,27,39,38,43,53,52,55,57,46,46];
exampleNums = [nan,nan,nan,nan,nan,nan,40,24,22,31,24,38];
bar(releaseNumNames,[numReleaseNotes;exampleNums]')
legend(["#release notes","#new/update examples"],Location="northwest")
title("Number of Deep Learning Toolbox™ update items in the last 5 years")
ylabel("#release notes")
1 Comment
Time Descending
I've not asked the Deep Learning team about this but here's my take:
- Not all features are created equally. Some will be big, some will be small.
- The rate limiting factor is always the number of developers working on DLT. There is a LOT that needs to be done but only a finite number of devs to do it. Its hard to sustainably grow teams exponentially.
- Not all deep learning functionality will be listed in the release notes of the deep learning toolbox. For example, I don't think we mention in the release notes (at least I couldn't find it) about the YOLO support that keeps getting updated on our GitHub repo matlab-deep-learning/MATLAB-Deep-Learning-Model-Hub: Discover pretrained models for deep learning in MATLAB
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