Natural Language Processing with Deep Learning
Fee may apply
The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is undergoing rapid evolution as new methods and toolsets converge with an ever-expanding availability of data. In this course you will explore the fundamental concepts of NLP and its role in current and emerging technologies. You will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, you will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models for question answering, machine translation, and other language understanding tasks.
What you will learn
- Computational properties of natural languages
- Neural network models for language understanding tasks
- Word vectors, syntactic, and semantic processing
- Coreference, question answering, and machine translation
- College Calculus, Linear Algebra: You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
- Basic Probability and Statistics: You should know basics of probabilities, gaussian distributions, mean, and standard deviation.
- Foundations of Machine Learning (recommended but not required): Knowledge of basic machine learning and/or deep learning is helpful, but not required.
This professional online course, based on the on-campus Stanford graduate course CS224N, features:
- Classroom videos edited and segmented to focus on essential content
- Coding assignments enhanced with added inline support and milestone code checks
- Office hours and support from Stanford-affiliated Course Assistants
- Cohort structure providing opportunities to network and collaborate with motivated learners from diverse locations and professional backgrounds
Expect to commit 8-12 hours/week for the duration of the 10-week program.
Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)
PhD Candidate, Computer Science
Head TA, CS224: Natural Language Processing with Deep Learning
Upon completing this course, you will earn a Certificate of Achievement in Natural Language Processing with Deep Learning from the Stanford Center for Professional Development.
This is the first course in a series of Artificial Intelligence professional courses to be offered by the Stanford Center for Professional Development. You may earn a Professional Certificate in Artificial Intelligence by completing three courses in the program. The next course in the series is Natural Language Understanding, a project-focused course that is complementary to Natural Language Processing with Deep Learning. The Natural Language Understanding course will be offered Winter 2019-2020.
Prior to enrolling in the course, you will be asked to complete a short application. The application allows you to share more about your interest in joining this cohort-based course, as well as verify that you meet the prerequisite requirements needed to make the most of the experience.
Contact us at 650-204-3984
|Dates:||December 2 - February 23, 2020|
$1,595 gives you access to online course materials. Online program materials (slides etc.) are available for download from the course page. All materials are available for printing and review upon enrollment. Videos in the course are not downloadable however full length versions of the classroom videos are available on YouTube.
****SEPTEMBER 2019 COHORT NOW FULL****
This is a cohort-based program that will run from December 2, 2019 - February 23, 2020. The 10-week course will be spread over 12 weeks with a two week Stanford University winter break beginning close of business Friday, December 20 and returning on Monday, January 6, 2020. During this time all course materials and forums will be available for continued study, but no assignments will be due and student support will be unavailable.
Online Program Materials
Online program materials (slides etc.) are available for download from the course page. All materials are available for printing and review upon enrollment. Videos in the course are not downloadable however full length versions of the classroom videos are available on YouTube.
To successfully complete the program, participants will complete five assignments (mix of written questions and programming prompts).
Participants are required to complete the program evaluation.
This course may not currently be available to learners in some states and territories.