Neural networks provide powerful new tools for modeling language, and have been used both to improve the state-of-the-art in a number of tasks and to tackle new problems that were not easy in the past. This class will start with a brief overview of neural networks, then spend the majority of the class demonstrating how to apply neural networks to natural language problems. Each section will introduce a particular problem or phenomenon in natural language, describe why it is difficult to model, and demonstrate several models that were designed to tackle this problem. In the process of doing so, the class will cover different techniques that are useful in creating neural network models, including handling variably sized and structured sentences, efficient handling of large data, semi-supervised and unsupervised learning, structured prediction, and multilingual modeling.
Suggested pre-requisite: 11-711 “Algorithms for NLP”.
I wasn’t able to find videos for the algorithms for NLP course but you can explore the following as supplemental materials:
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Each of these courses can be found in two places: YouTube and Academic Torrents. The advantage of Academic Torrents is that you can also download the supplementary course materials, like transcripts, PDFs, or PPTs.
- Natural Language Processing: Dan Jurafsky and Christopher Manning, Stanford University. YouTube | Academic Torrents
- Natural Language Processing: Michael Collins, Columbia University. YouTube | Academic Torrents
- Introduction to Natural Language Processing: Dragomir Radev, University of Michigan. YouTube | Academic Torrents
… (From 9 popular online courses that are gone forever… and how you can still find them)
Enjoyable but not as suited to binge watching as Stranger Things. 😉
Enjoy!