Of all rankings of ML books Aurélien’s HOML tops most lists. With having traversed it now cover to cover for a second time, I’d say this book totally lives up to the hype. I will make a case for that shortly. But first, I must say I gotta read it a third time, for the charm of it. To even the score with this latest review being for the third edition.

I first heard of the book, a few years back, probably 3. Then the book was a heavy tome of torture, having not solidified my skills as a programmer and my mathematical background still lacking to say the least. Regardless, even then the book was well written to float through it as an initialization step on my learning of ML.

Most technical books order their contents in terms how graspable the topics are, that is gradually – from simpler ones onto complex ones. To my understanding, Aurélien’s HOML begins with `Machine Learning Algorithms` which are quite taxing to comprehend immediately. Due to their many technical nature from mathematical background and statistics of course. As with everything AI/ML – its the taxonomy that is hard to wrap your head around. Most other concepts are fairly easy, once you are done with all the hard words – most just names of the inventors of those particular methods used. Be warned, there are many of those around.

Then the second part of the book, if studied chronologically, will be way easier and I dare say fun to read. Most applicable knowledge within deep learning which is in a craze hype of AI right now: – is seen upfront in this latter part of the book. Which is still technical in nature, even more so, cause stakes are higher with each deep neural network; but, the fun is also surreal. As one builds deep neural nets that will surely be used in the field to create some of the most dazzling models. The book also is cutting edge of reaching some of the model’s creation used in likes of the top AI research labs.

Other than those two prime reasons to read this book; the other, rather lingering indirectly throughout this book, just as the field of AI is and as the book is written – you will come to the realization of how research driven everything really is and there is no finality even remotely. This fact of course is heighten by the sheer research papers cited in this book. Which reading all of them constitutes another odyssey.

With all that said, as one works out all the implementation details of each ML/DL neural network, I sure hope the engineers on track here will build safe models. In whichever corner of AI they eventually converge to! As Aurélien says, “you now have superpowers: use them well!”

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