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Home » Applied Sciences » Computer Science » Natural Language Processing with Deep Learning (Winter 2019) (Stanford) » Lecture 1 – Introduction and Word Vectors (Stanford)

Lecture 1 – Introduction and Word Vectors (Stanford)

Course:Natural Language Processing with Deep Learning (Winter 2019) (Stanford)
Discipline:Applied Sciences
Institute:Stanford
Instructor(s): Prof. Dr. Christopher Manning, Prof. Dr. Thomas M. Siebel
Level:Graduate

Natural Language Processing with Deep Learning (Winter 2019) (Stanford)

Lecture 1 – Introduction and Word Vectors (Stanford)
Lecture 10 – Question Answering (Stanford)
Lecture 11 – Convolutional Networks for NLP (Stanford)
Lecture 12 – Subword Models (Stanford)
Lecture 13 – Contextual Word Embeddings (Stanford)
Lecture 14 – Transformers and Self-Attention (Stanford)
Lecture 15 – Natural Language Generation (Stanford)
Lecture 16 – Coreference Resolution (Stanford)
Lecture 17 – Multitask Learning (Stanford)
Lecture 18 – Constituency Parsing, TreeRNNs (Stanford)
Lecture 19 – Bias in AI (Stanford)
Lecture 20 – Future of NLP + Deep Learning (Stanford)
Lecture 3 – Neural Networks (Stanford)
Lecture 6 – Language Models and RNNs (Stanford)
Lecture 8 – Translation, Seq2Seq, Attention (Stanford)
Lecture 9 – Practical Tips for Projects (Stanford)
Lecture 2 – Word Vectors and Word Senses (Stanford)
Lecture 4 – Backpropagation (Stanford)
Lecture 5 – Dependency Parsing (Stanford)
Lecture 7 – Vanishing Gradients, Fancy RNNs (Stanford)

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