Best Deep Learning Books
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blog.floydhub.com
The highest-rated Amazon reviews seem to have come to the same conclusion: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...
Put differently, a list such as the linked one may attract a lot of visitors. But without critical, in-depth reviews it is not very useful and might set potential learners on the wrong path.
I felt the same way. Knowledgeable authors, loads of information, but quite poorly written.
That said, I don’t know of another book that’s as up to date or comprehensive, so I guess we’re stuck with it till something better comes along.
This book list would have been much better if promoters would have taken time to apply DL to reviews of promoted books and share results.
Whole DS/AI/DL/ML area is infested with such lack of application on their own stuff.
In general, I have found Manning to be one of the best technical publishers in terms of quality of content and updates.
It is not very difficult, in fact it is a borderline easy concept. So, do not be afraid in case you are. The mathematics behind deep learning is a little complicated but not much.
But if you just want to have a working knowledge then its quite approachable even for beginners and you do not need to learn the mathematics at all. In fact even the professionals rarely worry about the mathematics (they should though).
I agree with mkagenius, I've played around a lot with deep learning and like physics it suffers a lot from unnecessary jargon that hides relatively simple concepts, for example this random quote:
>ReLU stands for rectified linear unit, and is a type of activation function. Mathematically, it is defined as y = max(0, x).
Now everybody could just use plain English - if you have a negative number, set it to zero, in all other cases, keep the number - I don't know why someone has to use 'rectified linear unit' to describe this simple operation.
To quote some snarky comedian whose name escapes me, because words have meanings. The way I understand it, "rectified" here has an analogous in physics. Beyond saving characters, it also tells that the purpose of this function is similar to the one people use when dealing with current. If I were either a physicist or someone used to deal with wave functions, the title alone would tell me everything I need to know. And I bet the type of people who care about activation functions are the type of people who knows what rectifiers are.
I would argue that learning NNs includes learning the jargon needed to speak about them, the same way learning the meaning of "pointer" is required for programming.
On the other hand, maybe I'm just rationalizing the time I spent learning jargon.
I agree this is often a problem, but "rectified linear unit" isn't a deep learning term that was made up, as you obviously know from your physics background - its origins are in AC/DC power conversion. Anyone who knows basic signal processing knows what a rectifier is, and something that employs that function is a unit of it.
There's plenty of nonsensical complexity in machine learning, but rectifiers and sigmoids and other 10-cent words describing maths aren't overly complicated, in my opinion.
Lastly, saying "if you have a negative number, set it to zero, in all other cases, keep the number" is a lot more complicated and tedious than just saying "apply a rectifier." Would "ramp function" be acceptably plain?