1. Good ideas come from ML sources that are a bit quirky.
- NeurIPS from 1987 - 1997
- Stanford’s CS224n & CS231n projects
- Twitter likes from ML outliers
- ML Reddit’s WAYR
- Kaggle Kernels
- Top 15-40% papers on Arxiv Sanity
Untangle student papers, e.g. back-propagation was introduced by a Finnish Master student. Revisit old ideas that might work now (ANN), or tinker with the StyleGAN-BigGAN hybrid that was just released in a Kaggle kernel.
Newcomers often come up with similar ‘gut ideas’ and have a habit of reasoning themselves into believing they are novel. At best, they ship a me-too idea, but most become too euphoric to be productive.
We tend to be critical of other’s ideas but seldom judge or own ideas. Thus it's better to first scout other’s research directions. And then improve a direction that's already promising.
For a month, index a few hundred project directions and create a short-list by rating projects. Then select the project direction that makes you the most excited.
Reproduce the most relevant project from scratch. One paper can take several months to understand and recreate. That’s fine. Take the time you need.
reddit.com/r/MachineLearn…
Here’s how ~1000 students document Core War: github.com/search?p=100&q…
I spent a few extra hours to document it and share it on Reddit, here’s the difference:
github.com/emilwallner/Co…
Build a graphical user interface or generate quality visual results. Use bullet points, simple graphs, video walkthroughs, gifs, and large explainer images.
Another example:
github.com/emilwallner/Co…
Create a one-click install with: @FloydHub_ , @runwayml, @HelloPaperspace, Google Colab, and @kaggle kernels in relevant competitions. A plus for pip packages and model layers.
Sometimes you have to relaunch your project a few times. Social media has a component of luck. There is also an aspect of karma. Being active and supportive online can often help you reach a critical mass.
Michael Galarnyk: towardsdatascience.com/how-to-build-a…
Eric Jang: blog.evjang.com/2016/07/how-to…
Stanford: cs229.stanford.edu/projects.html
Andrej Karpathy: karpathy.github.io/2016/09/07/phd/
Rachael Tatman: kaggle.com/rebeccaturner/…
Edouard Harris: towardsdatascience.com/the-cold-start…