Starting this week, I'll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I'll be summarizing and explaining research papers in specific subfields of deep learning. This week I'll begin with Generative Adversarial Networks. According to Yann LeCun, "adversarial training is the coolest thing since sliced bread". I'm inclined to believe so because I don't think sliced bread ever created this much buzz and excitement within the deep learning community.

The traditional approach to NLP involved a lot of domain knowledge of linguistics itself. Understanding terms such as phonemes and morphemes were pretty standard as there are whole linguistic classes dedicated to their study. Let's look at how traditional NLP would try to understand the following word.

If we had a million words (not really a lot in NLP standards), we'd have a million by million sized matrix which would be extremely sparse (lots of 0's). The basic idea behind word vector initialization techniques is that we want to store as much information as we can in this word vector while still keeping the dimensionality at a manageable scale (25 – 1000 dimensions is ideal). Formally, our function seeks to maximize the log probability of any context word given the current center word. One Sentence Summary: Word2Vec seeks to find vector representations of different words by maximizing the log probability of context words given a center word and modifying the vectors through SGD.

Starting this week, I'll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I'll be summarizing and explaining research papers in specific subfields of deep learning. This week I'll begin with Generative Adversarial Networks. According to Yann LeCun, "adversarial training is the coolest thing since sliced bread". I'm inclined to believe so because I don't think sliced bread ever created this much buzz and excitement within the deep learning community.

Natural language processing (NLP) is all about creating systems that process or "understand" language in order to perform certain tasks. The traditional approach to NLP involved a lot of domain knowledge of linguistics itself. Understanding terms such as phonemes and morphemes were pretty standard as there are whole linguistic classes dedicated to their study. Let's look at how traditional NLP would try to understand the following word. Let's say our goal is to gather some information about this word (characterize its sentiment, find its definition, etc).

This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I'll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning. Before getting into the papers, let's first talk about what reinforcement learning is. The field of machine learning can be separated into 3 main categories.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.