11–20 of 20 posts
also, for folks who just want to their feet wet, oreilly's programming collective intelligence is a good start.
I ask this question keeping the current state of AI in mind.
Great book list. I strongly recommend getting a strong gasp of linear algebra as matrices are the a great way to think of large data in a manageable way. also, for folks who just want to their feet wet, oreilly's programming collective intelligence is a good start.
No, it is not a good start.
Great book list. I strongly recommend getting a strong gasp of linear algebra as matrices are the a great way to think of large data in a manageable way. also, for folks who just want to their feet wet, oreilly's programming collective intelligence is a good start.
"for folks who just want to their feet wet, oreilly's programming collective intelligence is a good start." No, it is not a good start.
I'm just curious. Have you read most of these books or did you get these recommendations from people you've networked with?
I own them all, and I am perpetually at different stages of working through each of them. :-) I work in the way I explained in the intro - when I don't know something, I step back and go learn the background I need to move forward.
I'm a noob and so here is my question: What will I be able to create after I go through all the books mentioned on the list? I ask this question keeping the current state of AI in mind.
The stuff mentioned on this page is largely about methods to learn from structured or unstructured data, and this is a field that has become highly relevant of late due to the data deluge. Research in these areas has progressed immensely as well, and we now have methods to mine many different types and volumes of data. If you have a good grip of statistical techniques and some basic ML ideas, you will be able to single out and pick the right technique that fits your problem, given your data type, SNR ratio, structured-ness, volume, your resource constraints, etc. Knowing a little more about ML will also allow you to change/invent new methods to suit your own problems better (e.g., a new way to compress your feature space).
I'm a noob and so here is my question: What will I be able to create after I go through all the books mentioned on the list? I ask this question keeping the current state of AI in mind.
IMO, AI is logically quite different from Machine Learning / Statistical Learning. The stuff mentioned on this page is largely about methods to learn from structured or unstructured data, and this is a field that has become highly relevant of late due to the data deluge. Research in these areas has progressed immensely as well, and we now have methods to mine many different types and volumes of data. If you have a go…
Earlier quoted context omitted.
I own them all, and I am perpetually at different stages of working through each of them. :-) I work in the way I explained in the intro - when I don't know something, I step back and go learn the background I need to move forward.
Do you find your time spent committed to learning conflicting with your time spend doing product development for Flightcaster?
I am a professional committed to both practicing my craft and consistently increasing my skill at my craft. For machine learning researchers, computer scientists, and engineers, a healthy ongoing dose of theory and practice is a great way to proceed.
I'm a noob and so here is my question: What will I be able to create after I go through all the books mentioned on the list? I ask this question keeping the current state of AI in mind.
IMO, AI is logically quite different from Machine Learning / Statistical Learning. The stuff mentioned on this page is largely about methods to learn from structured or unstructured data, and this is a field that has become highly relevant of late due to the data deluge. Research in these areas has progressed immensely as well, and we now have methods to mine many different types and volumes of data. If you have a go…
http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdfEarlier quoted context omitted.
"for folks who just want to their feet wet, oreilly's programming collective intelligence is a good start." No, it is not a good start.
can you explain why not?
see http://news.ycombinator.com/item?id=208811
I said there "PCI takes (in my opinion, feel free to differ) a math-lite, "dummies guide" approach to AI algorithms. "
Brad's approach and recommendations are in the opposite direction.