DesireCourse Net Udemy - Natural Language Processing with Deep Learning in Python

1. 1. Outline, Review, and Logistical Things/1. Introduction, Outline, and Review.mp437.12MB
2. 1. Outline, Review, and Logistical Things/2. How to Succeed in this Course.mp43.3MB
3. 1. Outline, Review, and Logistical Things/3. Tensorflow or Theano - Your Choice!.mp418.58MB
4. 1. Outline, Review, and Logistical Things/4. Where to get the code data for this course.mp46.5MB
5. 1. Outline, Review, and Logistical Things/5. Preprocessed Wikipedia Data.mp421.56MB
6. 10. Legacy Word2vec Lectures/1. (Legacy) What is a word embedding.mp418MB
7. 10. Legacy Word2vec Lectures/2. (Legacy) Using pre-trained word embeddings.mp44.06MB
8. 10. Legacy Word2vec Lectures/3. (Legacy) Word analogies using word embeddings.mp46.61MB
9. 10. Legacy Word2vec Lectures/4. (Legacy) TF-IDF and t-SNE experiment.mp426.75MB
10. 10. Legacy Word2vec Lectures/5. (Legacy) Word2Vec introduction.mp48.83MB
11. 11. Appendix FAQ/1. What is the Appendix.mp45.45MB
12. 11. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp478.27MB
13. 11. Appendix FAQ/11. BONUS Where to get Udemy coupons and FREE deep learning material.mp437.83MB
14. 11. Appendix FAQ/12. Python 2 vs Python 3.mp47.84MB
15. 11. Appendix FAQ/13. Is Theano Dead.mp417.81MB
16. 11. Appendix FAQ/14. What order should I take your courses in (part 1).mp429.33MB
17. 11. Appendix FAQ/15. What order should I take your courses in (part 2).mp437.62MB
18. 11. Appendix FAQ/2. How to install wp2txt or WikiExtractor.py.mp43.78MB
19. 11. Appendix FAQ/3. How to Uncompress a .tar.gz file.mp412.89MB
20. 11. Appendix FAQ/4. Windows-Focused Environment Setup 2018.mp4186.38MB
21. 11. Appendix FAQ/5. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
22. 11. Appendix FAQ/6. How to Code by Yourself (part 1).mp424.53MB
23. 11. Appendix FAQ/7. How to Code by Yourself (part 2).mp414.8MB
24. 11. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp412.99MB
25. 11. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB
26. 2. Beginner's Corner Working with Word Vectors/1. What are vectors.mp435.34MB
27. 2. Beginner's Corner Working with Word Vectors/2. What is a word analogy.mp431.21MB
28. 2. Beginner's Corner Working with Word Vectors/3. Trying to find and assess word vectors using TF-IDF and t-SNE.mp432.13MB
29. 2. Beginner's Corner Working with Word Vectors/4. Pretrained word vectors from GloVe.mp497.5MB
30. 2. Beginner's Corner Working with Word Vectors/5. Pretrained word vectors from word2vec.mp463.25MB
31. 2. Beginner's Corner Working with Word Vectors/6. Text Classification with word vectors.mp420.62MB
32. 2. Beginner's Corner Working with Word Vectors/7. Text Classification in Code.mp454.59MB
33. 2. Beginner's Corner Working with Word Vectors/8. Using pretrained vectors later in the course.mp413.88MB
34. 3. Review of Language Modeling and Neural Networks/1. Review Section Intro.mp45.54MB
35. 3. Review of Language Modeling and Neural Networks/10. Review Section Summary.mp42.97MB
36. 3. Review of Language Modeling and Neural Networks/2. Bigrams and Language Models.mp412.23MB
37. 3. Review of Language Modeling and Neural Networks/3. Bigrams in Code.mp417.88MB
38. 3. Review of Language Modeling and Neural Networks/4. Neural Bigram Model.mp439.72MB
39. 3. Review of Language Modeling and Neural Networks/5. Neural Bigram Model in Code.mp48.31MB
40. 3. Review of Language Modeling and Neural Networks/6. Neural Network Bigram Model.mp48.04MB
41. 3. Review of Language Modeling and Neural Networks/7. Neural Network Bigram Model in Code.mp45.17MB
42. 3. Review of Language Modeling and Neural Networks/8. Improving Efficiency.mp411.93MB
43. 3. Review of Language Modeling and Neural Networks/9. Improving Efficiency in Code.mp46.74MB
44. 4. Word Embeddings and Word2Vec/1. Return of the Bigram.mp411.5MB
45. 4. Word Embeddings and Word2Vec/10. Word2Vec in Code with Numpy.mp4108.24MB
46. 4. Word Embeddings and Word2Vec/11. Word2Vec Tensorflow Implementation Details.mp412.83MB
47. 4. Word Embeddings and Word2Vec/12. Word2Vec Tensorflow in Code.mp444.15MB
48. 4. Word Embeddings and Word2Vec/13. How to update only part of a Theano shared variable.mp49.18MB
49. 4. Word Embeddings and Word2Vec/14. Word2Vec in Code with Theano.mp431.05MB
50. 4. Word Embeddings and Word2Vec/15. Alternative to Wikipedia Data Brown Corpus.mp412.5MB
51. 4. Word Embeddings and Word2Vec/2. CBOW.mp436.51MB
52. 4. Word Embeddings and Word2Vec/3. Skip-Gram.mp416.07MB
53. 4. Word Embeddings and Word2Vec/4. Hierarchical Softmax.mp431.25MB
54. 4. Word Embeddings and Word2Vec/5. Negative Sampling.mp459.18MB
55. 4. Word Embeddings and Word2Vec/6. Negative Sampling - Important Details.mp418.67MB
56. 4. Word Embeddings and Word2Vec/7. Why do I have 2 word embedding matrices and what do I do with them.mp45.49MB
57. 4. Word Embeddings and Word2Vec/8. Word2Vec implementation tricks.mp415.68MB
58. 4. Word Embeddings and Word2Vec/9. Word2Vec implementation outline.mp414.45MB
59. 5. Word Embeddings using GloVe/1. GloVe Section Introduction.mp417.23MB
60. 5. Word Embeddings using GloVe/10. GloVe in Code - Theano Gradient Descent.mp444.03MB
61. 5. Word Embeddings using GloVe/11. GloVe in Tensorflow with Gradient Descent.mp474.9MB
62. 5. Word Embeddings using GloVe/12. Visualizing country analogies with t-SNE.mp49.18MB
63. 5. Word Embeddings using GloVe/13. Hyperparameter Challenge.mp44.05MB
64. 5. Word Embeddings using GloVe/14. Training GloVe with SVD (Singular Value Decomposition).mp451.98MB
65. 5. Word Embeddings using GloVe/2. Matrix Factorization for Recommender Systems - Basic Concepts.mp490.17MB
66. 5. Word Embeddings using GloVe/3. Matrix Factorization Training.mp428.75MB
67. 5. Word Embeddings using GloVe/4. Expanding the Matrix Factorization Model.mp440.94MB
68. 5. Word Embeddings using GloVe/5. Regularization for Matrix Factorization.mp422.3MB
69. 5. Word Embeddings using GloVe/6. GloVe - Global Vectors for Word Representation.mp47.06MB
70. 5. Word Embeddings using GloVe/7. Recap of ways to train GloVe.mp415.99MB
71. 5. Word Embeddings using GloVe/8. GloVe in Code - Numpy Gradient Descent.mp441.86MB
72. 5. Word Embeddings using GloVe/9. GloVe in Code - Alternating Least Squares.mp411.65MB
73. 6. Unifying Word2Vec and GloVe/1. Pointwise Mutual Information - Word2Vec as Matrix Factorization.mp453.27MB
74. 6. Unifying Word2Vec and GloVe/2. PMI in Code.mp473.11MB
75. 7. Using Neural Networks to Solve NLP Problems/1. Parts-of-Speech (POS) Tagging.mp47.75MB
76. 7. Using Neural Networks to Solve NLP Problems/10. Named Entity Recognition Baseline.mp414.91MB
77. 7. Using Neural Networks to Solve NLP Problems/11. Named Entity Recognition RNN in Theano.mp45.39MB
78. 7. Using Neural Networks to Solve NLP Problems/12. Named Entity Recognition RNN in Tensorflow.mp428.53MB
79. 7. Using Neural Networks to Solve NLP Problems/13. Hyperparameter Challenge II.mp43.83MB
80. 7. Using Neural Networks to Solve NLP Problems/2. How can neural networks be used to solve POS tagging.mp416.88MB
81. 7. Using Neural Networks to Solve NLP Problems/3. Parts-of-Speech Tagging Baseline.mp439.86MB
82. 7. Using Neural Networks to Solve NLP Problems/4. Parts-of-Speech Tagging Recurrent Neural Network in Theano.mp434.81MB
83. 7. Using Neural Networks to Solve NLP Problems/5. Parts-of-Speech Tagging Recurrent Neural Network in Tensorflow.mp4129.45MB
84. 7. Using Neural Networks to Solve NLP Problems/6. How does an HMM solve POS tagging.mp445.23MB
85. 7. Using Neural Networks to Solve NLP Problems/7. Parts-of-Speech Tagging Hidden Markov Model (HMM).mp414.39MB
86. 7. Using Neural Networks to Solve NLP Problems/8. Named Entity Recognition (NER).mp44.95MB
87. 7. Using Neural Networks to Solve NLP Problems/9. Comparing NER and POS tagging.mp414.31MB
88. 8. Recursive Neural Networks (Tree Neural Networks)/1. Recursive Neural Networks Section Introduction.mp437.4MB
89. 8. Recursive Neural Networks (Tree Neural Networks)/10. RNTN in Tensorflow (Code).mp4133.42MB
90. 8. Recursive Neural Networks (Tree Neural Networks)/11. Recursive Neural Network in TensorFlow with Recursion.mp412.75MB
91. 8. Recursive Neural Networks (Tree Neural Networks)/2. Sentences as Trees.mp424.19MB
92. 8. Recursive Neural Networks (Tree Neural Networks)/3. Data Description for Recursive Neural Networks.mp411.54MB
93. 8. Recursive Neural Networks (Tree Neural Networks)/4. What are Recursive Neural Networks Tree Neural Networks (TNNs).mp49.51MB
94. 8. Recursive Neural Networks (Tree Neural Networks)/5. Building a TNN with Recursion.mp48.09MB
95. 8. Recursive Neural Networks (Tree Neural Networks)/6. Trees to Sequences.mp411.16MB
96. 8. Recursive Neural Networks (Tree Neural Networks)/7. Recursive Neural Network in Theano.mp452.51MB
97. 8. Recursive Neural Networks (Tree Neural Networks)/8. Recursive Neural Tensor Networks.mp411.1MB
98. 8. Recursive Neural Networks (Tree Neural Networks)/9. RNTN in Tensorflow (Tips).mp449.5MB
99. 9. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp493.44MB
100. 9. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp487.03MB
101. 9. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp481.45MB
102. 9. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp497.36MB