Apply deep learning models to solve machine translation and conversation problems.
Apply deep structured semantic models on information retrieval and natural language applications.
Apply deep reinforcement learning models on natural language applications.
Apply deep learning models on image captioning and visual question answering.
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.
In this course, you will be given a thorough overviewof Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications.
We will also discuss deep reinforcement learning techniques applied in NLP andVision-Language Multimodal Intelligence.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
Module 1: Introduction to NLP and Deep Learning
An overview of Natural Language Processing using classic machine learning methods and cutting-edge deep learning methods.
Module 2: Neural models for machine translation and conversation
Introduction to Statistical Machine Translation and neural models for translation and conversation
Module 3: Deep Semantic Similarity Models (DSSM)
Introduction to Deep Semantic Similarity Model (DSSM) and its applications.
Module 4: Natural Language Understanding
Introduction to methods applied in Natural Language Understanding, such as continuous word representations and neural knowledge base embedding.
Module 5: Deep reinforcement learning in NLP
Introduction to deep reinforcement learning techniques applied in NLP
Module 6: Vision-Language Multimodal Intelligence
Introduction to neural models applied in Image captioning and visual question answering
Students need to have math and computer programming skills and fundamental knowledge on machine learning and deep learning before taking this course.