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Weather forecasting with AI, Kaggle tips and tricks, dealing with missing data, deep learning with Jesper Dramsch, The Data Scientist Show #040
The Data Scientist Show
Deep Learning Models
Sherind a: I was surprised how little data i needed to nit. Even if you're working with physical data, you can't underestimate transfer learning from like an image net or something like that because they have such a diversity in training data. And this also is very congruent with something fascinating that they learned about,. You want to pre train your neural network an on, on just diverse dataand supervise it. That way you get really high scores on image net, for example.
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