CourseClub NET UDACITY NLP Foundations Nanodegree |
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21. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt |
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22. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt |
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24. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt |
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29. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt |
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30. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt |
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35. Batch Vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt |
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/Part 02-Module 02-Lesson 01_Convolutional Neural Networks/ |
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/Part 02-Module 03-Lesson 01_Intro to TensorFlow/ |
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/Part 02-Module 04-Lesson 01_Recurrent Neural Networks/ |
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05. Section 2 Motivating and Modelling Recursive Sequences.html |
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06. How do we model an ordered sequence-LYcB8iV2bGE.zh-CN.vtt |
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11. Graphical model representations of recursive sequences-OS9yQCTzCkg.en.vtt |
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12. A simple model for my savings account balance-JQ2Nzzxx5oQ.en.vtt |
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14. Section 3 Injecting recursivity into a learner (the lazy wa.html |
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15. Injecting Recursivity into a Learner (the lazy way).html |
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17. 3 1 2 Introducing A Parameterized Formula-I72EOcAroFk.en.vtt |
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27. Section 4 Injecting Recursivity into Learners the Smart Way.html |
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31. RNNs 4 3 Formulating A Least Squares Loss-F5PVwVrEVHY.en.vtt |
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33. RNNs 4 5 RNN Properties- Graphical Models-LON9wniFUiE.en.vtt |
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