Deep Learning for Natural Language Processing | Stanford University

Dive deep into cutting-edge research in deep learning for natural language processing (NLP). Implement, train, and invent your own neural network models for a variety of NLP tasks.

Introduction

This course provides a deep dive into cutting-edge research in deep learning applied to natural language processing (NLP). Students will learn to implement, train, debug, visualize and invent their own neural network models for a variety of NLP tasks.

Highlights

• Covers a wide range of deep learning models for NLP, including word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks.
• Emphasizes practical engineering techniques for making neural networks work on real-world NLP problems.
• Culminates in a final project involving training a complex recurrent neural network and applying it to a large-scale NLP problem.

Recommendation

This course is highly recommended for students interested in the latest advancements in deep learning and its applications to natural language processing. It provides a unique opportunity to gain hands-on experience with implementing and working with state-of-the-art neural network models for a variety of NLP tasks.