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@ -624,6 +624,10 @@ Courses
- [Lectures](https://work.caltech.edu/lectures.html)
- [Homework](https://work.caltech.edu/homeworks.html)
- [Textbook](https://work.caltech.edu/textbook.html)
- [CS 189](http://www.eecs189.org/) **Introduction To Machine Learning** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
- Introductory ML course covering a wide range of topics: ranging from least squares to convolutional neural networks
- [Notes](http://www.eecs189.org/)
- [Homework](http://www.eecs189.org/)
- [CS 224d](http://cs224d.stanford.edu/) **Deep Learning for Natural Language Processing** *Stanford University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
- Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
- [Syllabus](http://cs224d.stanford.edu/syllabus.html)