Course/Book for intermediate to advanced knowledge

Submitted by SwanningNonchalantly t3_zqy2c0 in deeplearning

If I read another intro to how neural nets work I might make a steep descent off a bridge.

I’m looking for a good course or book that gives practical, interesting insights into how networks learn, tips to make them learn better, pitfalls to avoid, etc.

Something like, this type of normalisation is really good in this scenario because of X. Or even just a fresh take on what’s happening while assuming the reader knows the basics.

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Nerveregenerator t1_j10ylwt wrote

Hands on machine learning with tensorflow and scikit learn

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SwanningNonchalantly OP t1_j12st1d wrote

Interesting - so it’s not only about the practical details of using those libraries but also contains general nuggets of info? I use PyTorch.

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Nerveregenerator t1_j1572fl wrote

Deep learning is much different from typical programming topics in that it is composed of a large amount of mathematical and complex theoretical concepts that are not avoidable using a library. Getting the code to run is relatively easy, and the choice of library has mostly to do with deployment goals and utilizing existing implementations. When things aren't working, theres not really a compiler error as to what's wrong with the model/data pipeline, and deep theoretical knowledge comes into play.

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No_Client_8130 t1_j12if26 wrote

I always use the deeplearningbook from Goodfellow and Mathematics for machine learning as reference. Although old, the first book is definitely worth the effort.

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Alone_Bee_6221 t1_j13x5dr wrote

Deep Learning by Goodfellow really gave me some good insights and tips for training a model, also covering the mathematical background around the ideas.

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jetnew_sg t1_j14agqq wrote

I very much enjoyed The Kaggle Book, which shares many useful insights from Kaggle grandmasters from their competition experience. Many of them are also active on Twitter where they share more practical tips.

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