Reddit reviews Understanding Machine Learning: From Theory to Algorithms
We found 3 Reddit comments about Understanding Machine Learning: From Theory to Algorithms. Here are the top ones, ranked by their Reddit score.
Vezi si fast.ai, au 4 cursuri foarte bune. Apoi si asta e bun. Hugo Larochelle avea un curs de retele neuronale, un pic mai vechi.
La carti as adauga si The Hundred Page Machine Learning Book si asta , probabil cea mai buna carte practica, da asteapta editia a 2a, cu tensorflow 2.0, are tf.keras.layers, sequential model, practic tf 2 include keras si scapi de kkturile alea de sessions. Asa, si ar mai fi si asta, asta si asta. Nu pierde timp cu cartea lui Bengio de deep learning, e o mizerie superficiala. Spor la invatat si sa vedem cat mai multi romani cu articole pe ML si DL!
> I was just pointing out that you have to give it goals. I can't see any possible way to it to exist or do anything without having some way of turning disorder into order, and the only way to do that is give it non-random instructions. Those instructions would be the "goal," in effect.
You have to give it goals somehow, but not necessarily by specifying a utility function. You could, for example, give it a corpus of historical texts and try to have it learn human values from that (this would be a terrible idea, but definitely possible).
> But this convo we had definitely piques my interest. I'm strongly considering learning more about AI in general, on a technical level. It's too interesting not to dive in.
(Feel free to ignore everything I'm saying now.) This sounds like a very challenging prospect. At the very least you need to have a basic grasp of advanced math and a grasp of linear algebra in particular, otherwise you have no chance to understand a textbook on machine learning. If you do, then it's doable. Fwiw If you're looking for a textbook, I recommend this one.
I kind of see your point, but I don't completely agree. As I said already, I know something about active research in this field: enough, as a matter of fact, to be able to read these books
https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132
https://www.amazon.com/Foundations-Machine-Learning-Adaptive-Computation/dp/0262039400/
https://www.amazon.com/High-Dimensional-Probability-Introduction-Applications-Probabilistic/dp/1108415199/
However, as most researchers, I mostly focus on my specific subfield of Machine Learning. Also, every now and then, I'd like to read something about my job, which however doesn't feel like work (even a professional football player may want to kick a ball for fun every now and then 😉). Thus, I was looking for some general overview of Machine Learning, which wouldn't be too dumbed down, according to experts (otherwise I wouldn't have fun reading it), but which at the same time wasn't a huge reference textbook. After all, this would be just a leisure read, it shouldn't become work after work.
That's why I asked here, rather than on r/LearnMachineLearning. However, if other users also feel I should ask there, I will reconsider.