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This is pretty harsh. I use Goodfellow as a reference text and then supplement the mathematics behind it with more comprehensive texts like Hastie or Wasserman.
by whymauri 8y ago
This is pretty harsh. I use Goodfellow as a reference text and then supplement the mathematics behind it with more comprehensive texts like Hastie or Wasserman. Maybe if you sit down and read it cover-to-cover, it will seem disjointed. But I usually read chapters independently - I recently read the convolutional neural network chapter in preparation for an interview and I thought it was fine.
- spectramax 8y agoI much prefer reading Karpathy's notes and watching Stanford CS230 than to delve into Goodfellow. It sits on my shelf collecting dust.
- platz 8y agoSo it's a reference, not a pedagogical tool then? A reference implies you already know the topic and just want an index to jog your memory for things you can't hold all in your head at once. That is different than a pedagogical tool. If so, you shouldn't recommend it to those want to learn the topic.
- whymauri 8y agoPhrased like this, I agree with you. I didn't think about it like that. I agree with the poster below. Outside of classes, lecture notes, the books I listed, and Sutton/Barto (Intro. to Reinforcement Learning) have taught me the material. I use Goodfellow to brush up before interviews or jog my memory about topic I don't work with very often (like computer vision).
- yasp 8y agoWhat are Wasserman and Hastie?
- esfandia 8y ago- Wasserman has a book called "All of statistics" that gives a lot of the background required to understand modern machine learning - Hastie is a co-author of two machine learning books, one is "Elements of Statistical Learning" which is very comprehensive, and "Introduction to Statistical Learning", which is more approachable by people without too much background in stats.