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Comment by BlackJack | Hacker News Reader

“Some books are to be tasted, others to be swallowed, and some few to be chewed and digested: that is, some books are to be read only in parts, others to be read, but not curiously, and some few to be read wholly, and with diligence and attention.” - Francis Bacon.

This one should be chewed and digested. It's not a race - work slowly and diligently, and start exploring the boundaries of the topics covered, and you'll get much more out of it.

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It's possible that he/she is already familiar with the material but is reading it as review, in which case 10 days sounds more than reasonable.

True. As tsahyt puts it,

> it's always good to review things one has already learned. For me this usually leads to insights I haven't had before and a deeper understanding. Learnings seems to be an iterative process.

This is my primary motivation.

> This one should be chewed and digested. It's not a race - work slowly and diligently, and start exploring the boundaries of the topics covered, and you'll get much more out of it.

Thanks for the advice. Just in case, I am a Masters in CS and currently into my PhD.

There's probably not much new for you then. Pretty much everything in this book is covered in my Master Curriculum at least. The chapter on Quantum Algorithms looks interesting though. Quantum Computing is a bit of a mystery to me anyways.

However, it's always good to review things one has already learned. For me this usually leads to insights I haven't had before and a deeper understanding. Learnings seems to be an iterative process.

I just want to review stuff. It never hurts to go back to the basics and refresh things. I like to have a solid base on Data Structures, Algorithms and Probability. I even sign up for under graduate online courses if and when possible. Udacity makes my job quite simple.

Commented on this above, but the best favor I ever did myself in terms of online learning was to watch Gilbert Strang's (MIT) linear algebra lectures from iTunes U.

Thanks. They are on my radar as well - downloading them right away.

I'd agree that basically all of the material in this text is covered in an undergraduate and master's CS curriculum, but that doesn't mean you won't learn anything by going through it.

For example, I wouldn't say I really understood linear algebra until I went back and watched Gilbert Strang's lectures on iTunes U after my MS[0]. Which is, of course, somewhat awkward, as I encountered many problems which required using it during my studies, but the only course I had on it was my freshmen Calc II class.

Basically I'd made it through years of schooling being able to make use of a tool without actually understanding the tool. If you'd asked me to solve any problem that required insight into LA rather than just application of common mechanical primitives, I probably did quite poorly.

[0]: These lectures are fantastic by the way; they served as a good jumping off point for a bunch of other post-education math study for me.

Gilbert Strang's lectures are indeed very good. I've got a textbook on Linear Algebra written by him as well which is very good too.

As I said, reviewing material will probably lead to insights you haven't had before. If you've got spare time it's always a good idea to read a book on a topic like algorithms or a math book in order to get a deeper understanding, even if the book is just repeating things you already learned.

Yes; reading the book cover-to-cover in ten days sounds like a fantastic way to waste ten days learning nothing.

> Yes; reading the book cover-to-cover in ten days sounds like a fantastic way to waste ten days learning nothing.

Depends on the book and who is reading it. The OP is pursuing PhD and has computing background http://news.ycombinator.com/item?id=4783710

This text is covering selected undergrad topics barring the chapter on quantum algorithms(I am not sure what it is).

The chapter on quantum algorithms covers them at a level suitable for an undergraduate course (it's certainly covered in the undergrad courses at Berkeley, and hews pretty close to what we covered at Brown when I was an undergrad as well).

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