We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.
What you'll learn
- 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.
Course Syllabus
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