sorry to be offtopic but does any of you know a trick to get back into an instagram account? I was stupid lost my account password. I love any tricks you can offer me.
@Davian Jabari I really appreciate your reply. I got to the site through google and im in the hacking process atm. I see it takes a while so I will get back to you later with my results.
I was just yesterday entering a new Kaggle competition and was looking for some more insights into the field. I don't know how you did it, but the algorithm on choosing your guests works!
Thank you for such an insightful interview! Your questions perfectly mirrored my own curiosities, and it's the first video I've watched till the end. It provided a wealth of knowledge and left me feeling incredibly satisfied.
That wire made me think he was plugged in! Amazing talk though, WandB if you see this comment please make a video series or something on the clever techniques kind of case studies. That data augmentation trick was really cool...
He says cross validation can be used to prevent overfitting..?? Its used to check overfitting right...overfitting is reduced by limiting features or adding more data.
This is kind of like meta-overfitting. In a kaggle competition people will typically try a ton of different techniques, so with a fixed test set it's possible to overfit hyperparameters to the specific test set, and then performance drops on kaggle's held out test set.
Around 13:20 he mentions a Google researcher that I didn't catch. Does anyone know the name of the researcher he mentions? Regarding data sets that are too small to CV.
Great interview Lukas, I didn't think I'd get good advices in winning Kaggle from someone at that top-level like the CEO but turns out he is well versed in the business and was generous with the information provided, thank you for prompting and leading the conversation with questions that really matter.
Loved listening to this talk! I am about to finish my masters soon and am excited to start my professional journey in the field of ML. I often hear people say that kaggling is quite different from working in the industry because the datasets you see on kaggle have already been cleaned. Any advice for a noob who's looking to bridge this gap and also gain experience in data cleaning and wrangling?
