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 on December 20, 2022 at 9:38 PM

#987,378

Hands on machine learning with tensorflow and scikit learn

No_Client_8130 t1_j12if26 wrote on December 21, 2022 at 4:41 AM

#991,683

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

SwanningNonchalantly OP t1_j12st1d wrote on December 21, 2022 at 6:30 AM

#992,367

Replying to Nerveregenerator (#987,378)

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

Alone_Bee_6221 t1_j13x5dr wrote on December 21, 2022 at 2:19 PM

#994,829

Replying to No_Client_8130 (#991,683)

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

jetnew_sg t1_j14agqq wrote on December 21, 2022 at 3:51 PM

#995,796

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.

Nerveregenerator t1_j1572fl wrote on December 21, 2022 at 7:21 PM

#998,350

Replying to SwanningNonchalantly (#992,367)

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.