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种子名称： [UdemyCourseDownloader] Deep Learning Advanced NLP and RNNs
文件类型： 视频
文件数目： 62个文件
文件大小： 3.01 GB
收录时间： 2019-2-20 20:13
已经下载： 3次
资源热度： 94
最近下载： 2024-6-26 10:08

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[UdemyCourseDownloader] Deep Learning Advanced NLP and RNNs.torrent

8. Appendix/2. Windows-Focused Environment Setup 2018.mp4193.26MB

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/2. What is a word embedding.mp457.53MB

2. Review/3. Using word embeddings.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

2. Review/10. Different Types of RNN Tasks.mp456.79MB

2. Review/11. A Simple RNN Experiment.mp456MB

2. Review/12. RNN Code.mp431.1MB

2. Review/13. Review Section Summary.mp419.64MB

3. Bidirectional RNNs/1. Bidirectional RNNs Motivation.mp433.35MB

3. Bidirectional RNNs/2. Bidirectional RNN Experiment.mp448.7MB

3. Bidirectional RNNs/3. Bidirectional RNN Code.mp422.72MB

3. Bidirectional RNNs/4. Image Classification with Bidirectional RNNs.mp432.78MB

3. Bidirectional RNNs/5. Image Classification Code.mp448.62MB

3. Bidirectional RNNs/6. Bidirectional RNNs Section Summary.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 Summary.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.mp441.6MB

5. Attention/7. Visualizing Attention.mp410.43MB

5. Attention/8. Building a Chatbot without any more Code.mp476.15MB

5. Attention/9. Attention Section Summary.mp413.88MB

6. Memory Networks/1. Memory Networks Section Introduction.mp439.23MB

6. Memory Networks/2. Memory Networks Theory.mp430.4MB

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 Summary.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.mp438.6MB

8. Appendix/1. What is the Appendix.mp417.68MB

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

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 material.mp413.07MB