Natural Language Processing with Deep Learning
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
- Proficiency in Python: All coding assignments will be written in Python. You should be familiar with numpy and matplotlib, as well as basic shell commands (ssh, scp, ls, cd, rm, mv, cp, zip, etc.).
- Calculus and Linear Algebra: You should understand the following concepts from multivariable calculus and linear algebra: chain rule, gradients, matrix multiplication, matrix inverse.
- Probability: You should be familiar with basic probability distributions and be able to define the following concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.
- Foundations of Machine Learning (Recommended): Knowledge of basic machine learning and/or deep learning is helpful, but not required.
This professional online course, based on the Winter 2019 on-campus Stanford graduate course CS224N, features:
- Classroom lecture 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.
You may also earn a Professional Certificate in Artificial Intelligence by completing three courses in the Artificial Intelligence Professional Program.
Continuing Education Units
By completing this course, you'll earn 10 Continuing Education Units (CEUs). CEUs cannot be applied toward any Stanford degree. CEU transferability is subject to the receiving institution’s policies.
Prior to enrolling in your first course in the AI Professional Program, you must complete a short application (15-20 minutes). 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. If you have previously completed the application, you will not be prompted to do so again.
Contact us at 650-204-3984
This is a cohort-based program that will run from SEPTEMBER 28, 2020 - DECEMBER 6, 2020.
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 multiple choice quizzes 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.