Stanford Artificial Intelligence Course

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Need An Introductory Level Artificial Intelligence Course?

The growing field of artificial intelligence offers so much opportunity for students that have the foresight and interest in natural language programming. As the internet of things becomes a reality the number of devices that can benefit from some level of built in intelligence is also growing. 

The field of domestic and commercial robots ( androids? ) is truly fascinating and for those programmers that master the science or better yet develop a new more successful approach to AI programming, the sky is the limit as far as career choices. Here is an introduction artificial intelligence course that will wet your appetite for the field. It is free from the prestigious Stanford University in California.

Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed.

Artificial Intelligence Course in Natural Language Processing with Deep Learning Instructors: – Chris Manning – Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.

Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec.

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