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The basic idea behind word embeddings and the skip gram model is something called distributional semantics: we can define words by the company they keep. Skip-gram is a technique that uses correlations between center words and their context words to define word vectors that capture the meaning of those words. The mathematics center around maximizing the log of the probability of words appearing together (where the probability is defined by the softmax function), to the extent that they actually do in the text. The cool thing is that we can start with unstructured text and teach computers relationships between words. For instance, the algorithm can actually learn countries and capitals, based on the distances between these vectors in their underlying space. Pretty cool stuff. The paper we analyze is here: https://papers.nips.cc/paper/2013/fil... Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Prioritized Experience Replay course, with my subscription service. $29 a month gives you instant access to 42 hours of instructional content plus access to future updates, added monthly. Discounts available for Udemy students (enrolled longer than 30 days). Just send an email to [email protected] https://www.neuralnet.ai/courses Or, pickup my Udemy courses here: Deep Q Learning: https://www.udemy.com/course/deep-q-l... Actor Critic Methods: https://www.udemy.com/course/actor-cr... Curiosity Driven Deep Reinforcement Learning https://www.udemy.com/course/curiosit... Natural Language Processing from First Principles: https://www.udemy.com/course/natural-... Reinforcement Learning Fundamentals https://www.manning.com/livevideo/rei... Here are some books / courses I recommend (affiliate links): Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W Grokking Deep Learning: https://bit.ly/3yJ14gT Grokking Deep Reinforcement Learning: https://bit.ly/2VNAXql Come hang out on Discord here: / discord Need personalized tutoring? Help on a programming project? Shoot me an email! [email protected] Website: https://www.neuralnet.ai Github: https://github.com/philtabor Twitter: / mlwithphil