Udemy-Deep-Learning-Adva-1Pm2e
- 1. Welcome\\/1. Introduction.mp414.42MB
- 1. Welcome\\/2. Outline.mp416.14MB
- 1. Welcome\\/3. Where to get the code.mp419.58MB
- 1. Welcome\\/4. How to Succeed in this Course.mp417.41MB
- 2. Review\\/1. Review Section Introduction.mp420.75MB
- 2. Review\\/10. Different Types of RNN Tasks.mp456.79MB
- 2. Review\\/11. A Simple RNN Experiment.mp456.00MB
- 2. Review\\/12. RNN Code.mp431.10MB
- 2. Review\\/13. Review Section Sumry.mp419.64MB
- 2. Review\\/2. What is a word em<x>bedding.mp457.53MB
- 2. Review\\/3. Using word em<x>beddings.mp418.67MB
- 2. Review\\/4. What is a CNN.mp461.97MB
- 2. Review\\/5. Where to get the data.mp427.15MB
- 2. Review\\/6. CNN Code (part 1).mp4148.61MB
- 2. Review\\/7. CNN Code (part 2).mp459.29MB
- 2. Review\\/8. What is an RNN.mp456.61MB
- 2. Review\\/9. GRUs and LSTMs.mp449.64MB
- 3. Bidirectional RNNs\\/1. Bidirectional RNNs Motivation.mp433.35MB
- 3. Bidirectional RNNs\\/2. Bidirectional RNN Experiment.mp448.70MB
- 3. Bidirectional RNNs\\/3. Bidirectional RNN Code.mp422.72MB
- 3. Bidirectional RNNs\\/4. Ige Classification with Bidirectional RNNs.mp432.78MB
- 3. Bidirectional RNNs\\/5. Ige Classification Code.mp448.62MB
- 3. Bidirectional RNNs\\/6. Bidirectional RNNs Section Sumry.mp413.99MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/1. Seq2Seq Theory.mp432.51MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/2. Seq2Seq Applications.mp416.12MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/3. Decoding in Detail and Teacher Forcing.mp431.13MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/4. Poetry Revisited.mp412.47MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/5. Poetry Revisited Code 1.mp482.59MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/6. Poetry Revisited Code 2.mp466.56MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/7. Seq2Seq in Code 1.mp484.34MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/8. Seq2Seq in Code 2.mp450.59MB
- 4. Sequence-to-sequence models (Seq2Seq)\\/9. Seq2Seq Section Sumry.mp413.63MB
- 5. Attention\\/1. Attention Section Introduction.mp48.41MB
- 5. Attention\\/2. Attention Theory.mp461.82MB
- 5. Attention\\/3. Teacher Forcing.mp47.18MB
- 5. Attention\\/4. Helpful Implementation Details.mp440.88MB
- 5. Attention\\/5. Attention Code 1.mp499.94MB
- 5. Attention\\/6. Attention Code 2.mp441.60MB
- 5. Attention\\/7. Visualizing Attention.mp410.43MB
- 5. Attention\\/8. Building a Chatbot without any more Code.mp476.15MB
- 5. Attention\\/9. Attention Section Sumry.mp413.88MB
- 6. Memory Networks\\/1. Memory Networks Section Introduction.mp439.23MB
- 6. Memory Networks\\/2. Memory Networks Theory.mp430.40MB
- 6. Memory Networks\\/3. Memory Networks Code 1.mp479.62MB
- 6. Memory Networks\\/4. Memory Networks Code 2.mp453.55MB
- 6. Memory Networks\\/5. Memory Networks Code 3.mp455.85MB
- 6. Memory Networks\\/6. Memory Networks Section Sumry.mp416.11MB
- 7. Basics Review\\/1. (Review) Keras Discussion.mp427.64MB
- 7. Basics Review\\/2. (Review) Keras Neural Network in Code.mp466.11MB
- 7. Basics Review\\/3. (Review) Keras Functional API.mp438.60MB
- 8. Appendix\\/1. What is the Appendix.mp417.68MB
- 8. Appendix\\/10. What order should I take your courses in (part 2).mp4122.65MB
- 8. Appendix\\/11. Python 2 vs Python 3.mp418.79MB
- 8. Appendix\\/12. BONUS Where to get discount coupons and FREE deep learning terial.mp413.07MB
- 8. Appendix\\/2. Windows-Focused Environment Setup 2018.mp4193.26MB
- 8. Appendix\\/3. How to How to install Numpy Theano Tensorflow etc....mp4166.45MB
- 8. Appendix\\/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4115.85MB
- 8. Appendix\\/5. How to Succeed in this Course (Long Version).mp439.01MB
- 8. Appendix\\/6. How to Code by Yourself (part 1).mp482.11MB
- 8. Appendix\\/7. How to Code by Yourself (part 2).mp456.22MB
- 8. Appendix\\/8. Proof that using Jupyter Notebook is the same as not using it.mp478.26MB
- 8. Appendix\\/9. What order should I take your courses in (part 1).mp488.04MB
- CreateTime2023-01-02
- UpdateTime2023-01-08
- FileTotalCount127
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