Advances in of Natural Language Processing and Machine Learning are broadening the scope of what technology can do in people's everyday lives, and because of this, there is an unprecedented number of people developing a curiosity in the fields. And with the availability of educational content online, it has never been easier to go from curiosity to proficiency. We gathered some of our favorite resources together so you will have a jumping off point into studying these fields on your own. Some of the resources here are suitable for absolute beginners in either Natural Language Processing or Machine Learning, and others are suitable for those with an understanding of one who wish to learn more about the other. The resources on this post are 12 of the best, not the 12 best, and as such should be taken as suggestions on where to start learning without spending a cent, nothing more!
Machine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must find some other way to learn. I'll describe a novel algorithm for learning from interactions, and several problems of interest, most notably machine simultaneous interpretation (translation while someone is still speaking). This is all joint work with some amazing (former) students He He, Alvin Grissom II, John Morgan, Mohit Iyyer, Sudha Rao and Leonardo Claudino, as well as colleagues Jordan Boyd-Graber, Kai-Wei Chang, John Langford, Akshay Krishnamurthy, Alekh Agarwal, Stéphane Ross, Alina Beygelzimer and Paul Mineiro.
Machine translation is not some exotic future technology in its beta testing phase. It is already on your smartphone, laptop and tablet. And it is widely used. Take Google, for instance: it does a billion translations a day for online users. Microsoft introduced automatic, instant translation into Outlook email; Twitter offers translation on most foreign-language tweets.
This article was posted by SmileJet on Dev Battles. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing. What is artificial intelligence, or AI?
What is the difference between AI, Machine Learning, NLP, and Deep Learning? AI (Artificial intelligence) is a subfield of computer science that was created in the 1960s, and it was/is concerned with solving tasks that are easy for humans but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic and includes all kinds of tasks such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. NLP (Natural language processing) is simply the part of AI that has to do with language (usually written). Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g.