"Python for Data Science For Dummies" by Luca Massaron & John Paul Mueller is a very practical book on machine learning. In a real world scenario your first obstacles will be learning how to use the programming language as well as preparing data. Both topics are well covered in this book. The "learning from data" chapter contains an introduction into basic machine learning algorithms as well as ensemble learning. The book contains minor inaccuracies but I think it's a good, practical start for a novice. It doesn't include anything on neural nets however.
Ask HN: Best books on AI?
31–40 of 95 posts
Re: Ask HN: Best books on AI?
#32AI: A Modern Approach (Russell, Norvig) and Deep Learning (Goodfellow, Bengio) have been mentioned already.
I'd also recommend:
Godel, Escher Bach: an Eternal Golden Braid
by Douglas Hofstadter. Might not be exactly what you're looking for (it's all over the place, touching music theory, math, art, philosophy...), but it's fun and enjoyable to read. Also very dense.
Re: Ask HN: Best books on AI?
#33To iterate on what others said, but what was not emphasized enough from my point of few: AI is academic (as a synonym for 'theoretical' and 'math-intensive'). Once you look beyond purely symbolic AI, which proved to be infeasible as @curuinor pointed out somewhere here, you will need to build up at least basic knowledge in probability theory and linear algebra. The path I'm following at the moment is a quite rigorous…
To preface, I'm currently learning several disciplines in tandem along a route suggested by the link, so kudos to them for putting together a solid list of resources. Now, from the link: "Few universities offer an education that is on par with what you can find online these days. The people pioneering the field from industry and academia so openly and competently share their knowledge that the best curriculum is an o…
The Google Brain Residency is a cool program for non-academics to get into deep learning research, and you can always get into AI on the applications side, but in both cases you're going to have to really try.
Re: Ask HN: Best books on AI?
#34You cannot really go practical in AI without academic rigor. You can do recipes of what's already been done using a TensorFlow book, but that's how far one can go. If one is serious in getting in AI today, a great way to do is read the following books, in order: 1. AI: A Modern Approach by Stuart Russell and Peter Norvig. 2. Deep Learning by Ian Goodfellow and Yoshua Bengio. It is amazing how approachable both books…
Sutton & Barto's Reinforment Learning complete this triumvirate
https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.htm...
David Silver's Reinforcement Course is based on Sutton & Barto
Re: Ask HN: Best books on AI?
#35You cannot really go practical in AI without academic rigor. You can do recipes of what's already been done using a TensorFlow book, but that's how far one can go. If one is serious in getting in AI today, a great way to do is read the following books, in order: 1. AI: A Modern Approach by Stuart Russell and Peter Norvig. 2. Deep Learning by Ian Goodfellow and Yoshua Bengio. It is amazing how approachable both books…
I love Paradigms of Artificial Intelligence by Peter Norvig much more than AIMA, which I found excessively encyclopedic and shallow.
While some will argue it is dated, I think it presents many timeless ideas that will get in vogue soon with little tweaks to their inference schemes.
Same for The Art of Prolog.
Re: Ask HN: Best books on AI?
#36Earlier quoted context omitted.
Anecdotal. Do you have specific records of such failures?
Less symbolic failures. There was a huge and abiding torrent of neural net stuff that dealt with evolving topologies in late 90's. I see very little of it in any way shape or form in industry or academia today, because it's a lot of computation for basically no gain. They thought that layerwise pretraining of neural nets was the way to go in 2006, before they realized that initializations, normalization, and better a…
A nitpick on the buzzer point:
The skill-cap in Jeopardy is sort of low. The top players can all answer almost all questions, so victory comes down to the buzzer even between Jennings and Rutter.
The important thing is that Watson hit that skill cap. From there it wins on tie-breaks every time. I think we'll see this dynamic in many human/AI contests. If both competitors' skills are at the saturation point, the contest is decided either by luck, or some strategically unsatisfying thing like diligence or mechanics. I don't see why humans will ever have an advantage at this.
Re: Ask HN: Best books on AI?
#37Earlier quoted context omitted.
Anecdotal. Do you have specific records of such failures?
Less symbolic failures. There was a huge and abiding torrent of neural net stuff that dealt with evolving topologies in late 90's. I see very little of it in any way shape or form in industry or academia today, because it's a lot of computation for basically no gain. They thought that layerwise pretraining of neural nets was the way to go in 2006, before they realized that initializations, normalization, and better a…
Is pretraining really all that much of a failure? I haven't really found an authoritative answer on whether or not pretraining is worth it these days. Hinton's 2012(?) Coursera course still focuses pretty deeply on generative/layer-by-layer pretraining with RBMs but I'm just not really sure if that's fallen by the wayside today. Or maybe it's still useful only in specific circumstances?
Re: Ask HN: Best books on AI?
#38Firstly, I don't think you can dive straight into coding without understanding the fundamentals. AI is such a broad and rich field, and there's a lot you need to know before you start.
It also depends on what you're going to focus on. Are you looking to implement a game-playing agent? An object recognition algorithm? More of a logic focus?
If you just want Deep Learning and statistical methods, then Bishop's Pattern Recognition and Machine Learning is a good start. Otherwise, Russel and Norvig's Artificial Intelligence or Patrick Winston's similarly titled book are great starting points. For more big-picture stuff,
Marvin Minsky's Society of Mind is great, and Hofstader's Gödel, Escher, Bach is a classic too. Both are a lot less practical though, which seems to be what you're looking for.
Re: Ask HN: Best books on AI?
#39Some on this thread have recommended Norvig's PAIP, but that's kind of an old school AI book in that it focuses on heuristic search and logic (implementing prolog in lisp at one point, very impressive stuff actually); but is lacking any coverage of statistical machine learning, which is the approach that underlies most of the cool stuff these days. It's still a great book, but I'd instead recommend a path that focuse…
You don't need to focus on just one side or the other. AlphaGo wasn't made by just hacking together a couple neural nets, it built on heuristic search, MCTS, and the "old-school" AI.
I agree that basic statistics + Bishop's book is a great way to start getting into machine learning -- but AI is a much broader field than that.
Re: Ask HN: Best books on AI?
#40You cannot really go practical in AI without academic rigor. You can do recipes of what's already been done using a TensorFlow book, but that's how far one can go. If one is serious in getting in AI today, a great way to do is read the following books, in order: 1. AI: A Modern Approach by Stuart Russell and Peter Norvig. 2. Deep Learning by Ian Goodfellow and Yoshua Bengio. It is amazing how approachable both books…
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