UdemyCourseDownloader Deep Learning Recurrent Neural Networks in Python

1. 8. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.44MB
2. 1. Introduction and Outline/1. Outline of this Course.mp44.93MB
3. 1. Introduction and Outline/2. Review of Important Deep Learning Concepts.mp45.68MB
4. 1. Introduction and Outline/3. Where to get the Code and Data.mp43.12MB
5. 1. Introduction and Outline/4. How to Succeed in this Course.mp43.3MB
6. 2. The Simple Recurrent Unit/1. Architecture of a Recurrent Unit.mp47.75MB
7. 2. The Simple Recurrent Unit/2. Prediction and Relationship to Markov Models.mp48.97MB
8. 2. The Simple Recurrent Unit/3. Unfolding a Recurrent Network.mp43.21MB
9. 2. The Simple Recurrent Unit/4. Backpropagation Through Time (BPTT).mp47.15MB
10. 2. The Simple Recurrent Unit/5. The Parity Problem - XOR on Steroids.mp47.79MB
11. 2. The Simple Recurrent Unit/6. The Parity Problem in Code using a Feedforward ANN.mp438.33MB
12. 2. The Simple Recurrent Unit/7. Theano Scan Tutorial.mp423.77MB
13. 2. The Simple Recurrent Unit/8. The Parity Problem in Code using a Recurrent Neural Network.mp437.48MB
14. 2. The Simple Recurrent Unit/9. On Adding Complexity.mp42.39MB
15. 3. Recurrent Neural Networks for NLP/1. Word Embeddings and Recurrent Neural Networks.mp48.69MB
16. 3. Recurrent Neural Networks for NLP/2. Word Analogies with Word Embeddings.mp44.18MB
17. 3. Recurrent Neural Networks for NLP/3. Representing a sequence of words as a sequence of word embeddings.mp45.44MB
18. 3. Recurrent Neural Networks for NLP/4. Generating Poetry.mp47.53MB
19. 3. Recurrent Neural Networks for NLP/5. Generating Poetry in Code (part 1).mp452.43MB
20. 3. Recurrent Neural Networks for NLP/6. Generating Poetry in Code (part 2).mp413.59MB
21. 3. Recurrent Neural Networks for NLP/7. Classifying Poetry.mp46.28MB
22. 3. Recurrent Neural Networks for NLP/8. Classifying Poetry in Code.mp445.87MB
23. 4. Advanced RNN Units/1. Rated RNN Unit.mp46.05MB
24. 4. Advanced RNN Units/2. RRNN in Code - Revisiting Poetry Generation.mp425.41MB
25. 4. Advanced RNN Units/3. Gated Recurrent Unit (GRU).mp49.03MB
26. 4. Advanced RNN Units/4. GRU in Code.mp415.07MB
27. 4. Advanced RNN Units/5. Long Short-Term Memory (LSTM).mp47.62MB
28. 4. Advanced RNN Units/6. LSTM in Code.mp419.39MB
29. 4. Advanced RNN Units/7. Learning from Wikipedia Data.mp412.76MB
30. 4. Advanced RNN Units/8. Alternative to Wikipedia Data Brown Corpus.mp412.49MB
31. 4. Advanced RNN Units/9. Learning from Wikipedia Data in Code (part 1).mp448.69MB
32. 4. Advanced RNN Units/10. Learning from Wikipedia Data in Code (part 2).mp425.61MB
33. 4. Advanced RNN Units/11. Visualizing the Word Embeddings.mp423.49MB
34. 5. Batch Training/1. Batch Training for Simple RNN.mp416.56MB
35. 6. TensorFlow/1. Simple RNN in TensorFlow.mp412MB
36. 7. Basics Review/1. (Review) Theano Basics.mp493.47MB
37. 7. Basics Review/2. (Review) Theano Neural Network in Code.mp487.03MB
38. 7. Basics Review/3. (Review) Tensorflow Basics.mp481.44MB
39. 7. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp497.34MB
40. 8. Appendix/1. What is the Appendix.mp45.45MB
41. 8. Appendix/2. How to install wp2txt or WikiExtractor.py.mp43.77MB
42. 8. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
43. 8. Appendix/5. How to Code by Yourself (part 1).mp424.53MB
44. 8. Appendix/6. How to Code by Yourself (part 2).mp414.81MB
45. 8. Appendix/7. How to Succeed in this Course (Long Version).mp412.99MB
46. 8. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
47. 8. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.31MB
48. 8. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.02MB
49. 8. Appendix/11. Python 2 vs Python 3.mp47.83MB
50. 8. Appendix/12. Is Theano Dead.mp417.82MB
51. 8. Appendix/13. What order should I take your courses in (part 1).mp429.33MB
52. 8. Appendix/14. What order should I take your courses in (part 2).mp437.62MB