Deep Learning for Natural Language 2nd

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4:59:14

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01 - Deep Learning for Natural Language Processing - Introduction.mp4

02:35

02 - Topics.mp4

00:35

03 - 1.1 Introduction to Deep Learning for Natural Language Processing.mp4

10:44

04 - 1.2 Running the Hands-On Code Examples in Jupyter Notebooks.mp4

13:19

05 - 1.3 Review of Prerequisite Deep Learning Theory.mp4

14:51

06 - 1.4 A Sneak Peak.mp4

06:43

07 - Topics.mp4

00:23

08 - 2.1 Computational Representations of Natural Language Elements.mp4

14:37

09 - 2.2 Visualizing Word Vectors with word2viz.mp4

06:21

10 - 2.3 Localist versus Distributed Representations.mp4

03:36

11 - 2.4 Elements of Natural Human Language.mp4

07:32

12 - 2.5 The word2vec Algorithm.mp4

09:01

13 - 2.6 Creating Word Vectors with word2vec.mp4

21:56

14 - 2.7 Pre-Trained Word Vectors and doc2vec.mp4

03:47

15 - Topics.mp4

00:51

16 - 3.1 Best Practices for Preprocessing Natural Language Data.mp4

27:11

17 - 3.2 The Area Under the ROC Curve.mp4

19:40

18 - 3.3 Document Classification with a Dense Neural Net.mp4

40:16

19 - 3.4 Classification with a Convolutional Neural Net.mp4

14:51

20 - Topics.mp4

00:37

21 - 4.1 Essential Theory of RNNs.mp4

06:26

22 - 4.2 RNNs in Practice.mp4

07:48

23 - 4.3 Essential Theory of LSTMs and GRUs.mp4

03:06

24 - 4.4 LSTMs and GRUs in Practice.mp4

07:01

25 - Topics.mp4

00:43

26 - 5.1 Bidirectional LSTMs.mp4

04:30

27 - 5.2 Stacked LSTMs.mp4

06:27

28 - 5.3 Datasets for NLP.mp4

05:26

29 - 5.4 Sequence Generation.mp4

12:48

30 - 5.5 seq2seq and Attention.mp4

03:23

31 - 5.6 Transfer Learning in NLP - BERT, ELMo, GPT-2 and Other Characters.mp4

04:05

32 - 5.7 Non-Sequential Architectures - The Keras Functional API.mp4

15:43

33 - 5.8 (Financial) Time Series Applications.mp4

01:21

34 - Deep Learning for Natural Language Processing - Summary.mp4

01:01

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