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I'm still not clear on the difference between deep learning and machine learning. Also are there good primer books on machine learning fundamentals?
It's free to browse online if your interested:
Earlier quoted context omitted.
It _is_ a subfield of machine learning, based on neural networks and usually the features are learned and not engineered.
Could it be said machine learning is more surface AI like quality scores. Whereas deep learning is going down the creating consciousness route?
Earlier quoted context omitted.
It _is_ a subfield of machine learning, based on neural networks and usually the features are learned and not engineered.
Could it be said machine learning is more surface AI like quality scores. Whereas deep learning is going down the creating consciousness route?
Earlier quoted context omitted.
It _is_ a subfield of machine learning, based on neural networks and usually the features are learned and not engineered.
Could it be said machine learning is more surface AI like quality scores. Whereas deep learning is going down the creating consciousness route?
Earlier quoted context omitted.
Could it be said machine learning is more surface AI like quality scores. Whereas deep learning is going down the creating consciousness route?
That's a little breathless - I'd just think of it as a particular subset of machine learning that's produced some promising results, and leave talk of consciousness out of it.
I found that talking about it along the lines or neural networking seems to be more accurate
Instead, I did a simple project on searching using language processing and just read Foundations of Statistical Natural Language Processing [1], which is not too difficult, and Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition [2], which is a pretty heavy read but a great reference. I was able to find a used copy of the second book for $0.30.
I also put a bit of study into articulatory phonetics and speech recognition as part of a graduate study-abroad, which is an interesting field on its own, but I always wanted to come back to computational linguistics.
[1] http://www.amazon.com/Foundations-Statistical-Natural-Langua...
[2] http://www.amazon.com/Speech-Language-Processing-Introductio...
I have a folder to bookmark machine learning resources. Here's another good one from the creator of coursera (Stanford grad I think) https://www.coursera.org/learn/machine-learning/home/info?ut...
Its Bengio's (very well known deep learning researcher) upcoming textbook. I would highly recommend to anyone interested in deep learning/neural net subset of machine learning.
A firetruck is _____
Try typing this in Google and you'll get "red", "moving" and "made". During the course you build a network that trains next-word completions using arbitrary bodies of text. You can train it for hours, days or weeks...and it just gets better and better. Eventually you will max out the capacity of your network, but then you can fiddle with the number of nodes and other hyperparameters. In the end you're just training a "black-box" nonlinear function to best approximate an unknown function defined by training data.
I was fortunate to take this class the first time it was offered. I found it a great introduction to the material, but a bit over my head. Deep learning requires a strong grasp of linear algebra - and particularly at the "Stanford" level. My undergrad didn't prepare me well for visualizing outer products and matrix / tensor derivatives. Once you get over those hurdles, deep learning is quite fun . It often works like…
I was fortunate to take this class the first time it was offered. I found it a great introduction to the material, but a bit over my head. Deep learning requires a strong grasp of linear algebra - and particularly at the "Stanford" level. My undergrad didn't prepare me well for visualizing outer products and matrix / tensor derivatives. Once you get over those hurdles, deep learning is quite fun . It often works like…
I agree that the math can be complex, but I think it boils down to probability and the notation of presenting the ideas more than the underlying concepts. I feel like the most advanced math used in NLP is the log function, personally. Along with working with big arrays of data, or structures like Markov models and neural nets, which tend to be just arrays of numbers.
In a normal AI course, we had to form write-ups of contemporary AI articles, and one I found interesting was a model for summarizing text, including chapters, books, and other writing. The key idea was finding the most significant sentences in any given paragraph or unit and then using that verbatim.
It might be interesting to take some of these simple ideas and flesh them out with some of these advanced AI methods. For example, finding a more complete meaning of a book chapter and rewriting the summary.
That's the kind of AI work that I think people expect and are looking for from the NLP field, and it's not necessarily out of reach currently.