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CS224N: Natural Language Processing with Deep Learning. Instructor: Prof. Chris Manning, Department of Computer Science and Linguistics, Stanford University.

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September 25, 2023

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September 25, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

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Unlimited Duration

Description

CS224N: Natural Language Processing with Deep Learning. Instructor: Prof. Chris Manning, Department of Computer Science and Linguistics, Stanford University.

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Course Curriculum

  • Lecture 01 – Introduction and Word Vectors Unlimited
  • Lecture 02 – Word Vectors and Word Senses Unlimited
  • Lecture 03 – Neural Networks Unlimited
  • Lecture 04 – Backpropagation Unlimited
  • Lecture 05 – Dependency Parsing Unlimited
  • Lecture 06 – Language Models and RNNs Unlimited
  • Lecture 07 – Vanishing Gradients, Fancy RNNs Unlimited
  • Lecture 08 – Translation, Seq2Seq, Attention Unlimited
  • Lecture 09 – Practical Tips for Projects Unlimited
  • Lecture 10 – Question Answering Unlimited
  • Lecture 11 – Convolutional Networks for NLP Unlimited
  • Lecture 12 – Subword Models Unlimited
  • Lecture 13 – Contextual Word Embeddings Unlimited
  • Lecture 14 – Transformers and Self-Attention Unlimited
  • Lecture 15 – Natural Language Generation Unlimited
  • Lecture 16 – Coreference Resolution Unlimited
  • Lecture 17 – Multitask Learning Unlimited
  • Lecture 18 – Constituency Parsing, TreeRNNs Unlimited
  • Lecture 19 – Bias in AI Unlimited
  • Lecture 20 – Future of NLP + Deep Learning Unlimited
  • Lecture 21 – Low Resource Machine Translation Unlimited
  • Lecture 22 – BERT and Other Pre-trained Language Models Unlimited

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