Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford course on the topic of Natural Language Processing with Deep Learning methods. This course is free and I encourage you to make use of this excellent resource. After completing this post, you will know: Let’s get started. This post is divided into 5 parts; they are: The course is taught by Chris Manning and Richard Socher. Chris Manning is an author of at least two top textbooks on Natural Language Processing: Richard Socher is the guy behind MetaMind and is the Chief Scientist at Salesforce. Natural Language Processing is the study of computational methods for working with voice and text data. Since the 1990s, the field has been focused on statistical methods. More recently, the field is switching to deep learning methods given the demonstrably improved capabilities they offer. This course is focused on teaching statistical natural language processing with deep learning methods. From the course description on the website: Goals of the Course This course is taught at Stanford, although the lectures used in the course have been recorded and made public, and we will focus on these freely available materials. The course assumes some mathematical and programming skill. Nevertheless, refresher materials are provided in case the requisite skills are rusty. Specifically: Code examples are in Python and make use of the NumPy and TensorFlow Python libraries. The lectures and material seem to change a little each time the course is taught. This is not unsurprising given the speed that things are changing the field. Here, we will look at the CS224n Winter 2017 syllabus and lectures that are publicly available. I recommend watching the YouTube videos of the lectures, and access the slides, papers, and further reading on the syllabus only if needed. The course is broken down into the following 18 lectures and one review: I watched them all on YouTube at double playback speed with the slides open while taking notes. Students of the course are expected to complete assignments. You may want to complete the assessments yourself to test your knowledge from working through the lectures. You can see the assignments here: CS224n Assignments Importantly, students must submit a final project report using deep learning on a natural language processing problem. These projects can be fun to read if you are looking for ideas for how to test out your new found skills. Directories of submitted student reports are available here: If you find some great reports, please post your discoveries in the comments. This course is designed for students and the goal is to teach enough NLP and Deep Learning theory for the students to start developing their own methods. This may not be your goal. You may be a developer. You may be only interested in using the tools of deep learning on NLP problems to get a result on a current project. In fact, this is the situation of most of my readers. If this sounds like you, I would caution you to be very careful in the way you work through the material. There is a lot of gold in this material for practitioners, but you must keep your wits and not fall into the “I must understand everything” trap. As a practitioner, your goals are very different and you must ruthlessly stay on target. This section provides more resources on the topic if you are looking go deeper. In this post, you discovered the Stanford course on Deep Learning for Natural Language Processing. Specifically, you learned: Did you work through some or all of this course material?本帖隐藏的内容
Course Summary
Goal: for computers to process or “understand” natural language in order to perform tasks that are useful
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.
Reasons for Exploring Deep Learning, from the Stanford Deep Learning for NLP course
Goals of the Stanford Deep Learning for NLP Course
Older Related Material
Summary
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