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[Tutorialsplanet NET] Udemy - Modern Deep Learning in Python

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Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
A60897BB8C3664734CA9D28BCDE90147C4A410C2
Source
Unverified
Total Size
1.45 GB
Total Files
100
Seeders
0
Leechers
1
Health
Score
1
Type
Bookware

File List

FileSize
1. Introduction and Outline/1. Outline - what did you learn previously, and what will you learn in this course.mp414.43 MB
1. Introduction and Outline/1. Outline - what did you learn previously, and what will you learn in this course.vtt9.77 KB
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp46 MB
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.vtt5.02 KB
10. Transition to the 2nd Half of the Course/1. Transition to the 2nd Half of the Course.mp49.45 MB
10. Transition to the 2nd Half of the Course/1. Transition to the 2nd Half of the Course.vtt5.83 KB
11. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp49.82 MB
11. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.vtt5.69 KB
11. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp421.44 MB
11. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.vtt14.31 KB
11. Project Facial Expression Recognition/3. The class imbalance problem.mp410.11 MB
11. Project Facial Expression Recognition/3. The class imbalance problem.vtt7.16 KB
11. Project Facial Expression Recognition/4. Utilities walkthrough.mp413.49 MB
11. Project Facial Expression Recognition/4. Utilities walkthrough.vtt5.24 KB
11. Project Facial Expression Recognition/5. Class-Based ANN in Theano.mp443.98 MB
11. Project Facial Expression Recognition/5. Class-Based ANN in Theano.vtt13.38 KB
11. Project Facial Expression Recognition/6. Class-Based ANN in TensorFlow.mp437.39 MB
11. Project Facial Expression Recognition/6. Class-Based ANN in TensorFlow.vtt11.54 KB
11. Project Facial Expression Recognition/7. Facial Expression Recognition Project Summary.mp42.91 MB
11. Project Facial Expression Recognition/7. Facial Expression Recognition Project Summary.vtt1.47 KB
12. Modern Regularization Techniques/1. Modern Regularization Techniques Section Introduction.mp44.27 MB
12. Modern Regularization Techniques/1. Modern Regularization Techniques Section Introduction.vtt2.67 KB
12. Modern Regularization Techniques/2. Dropout Regularization.mp422.7 MB
12. Modern Regularization Techniques/2. Dropout Regularization.vtt12.67 KB
12. Modern Regularization Techniques/3. Dropout Intuition.mp46.14 MB
12. Modern Regularization Techniques/3. Dropout Intuition.vtt4.02 KB
12. Modern Regularization Techniques/4. Noise Injection.mp48.64 MB
12. Modern Regularization Techniques/4. Noise Injection.vtt6.15 KB
12. Modern Regularization Techniques/5. Modern Regularization Techniques Section Summary.mp43.88 MB
12. Modern Regularization Techniques/5. Modern Regularization Techniques Section Summary.vtt2.36 KB
13. Batch Normalization/1. Batch Normalization Introduction.mp43.51 MB
13. Batch Normalization/1. Batch Normalization Introduction.vtt2.23 KB
13. Batch Normalization/2. Exponentially-Smoothed Averages.mp47.38 MB
13. Batch Normalization/2. Exponentially-Smoothed Averages.vtt4.8 KB
13. Batch Normalization/3. Batch Normalization Theory.mp418.61 MB
13. Batch Normalization/3. Batch Normalization Theory.vtt12.37 KB
13. Batch Normalization/4. Batch Normalization Tensorflow (part 1).mp49.44 MB
13. Batch Normalization/4. Batch Normalization Tensorflow (part 1).vtt5.92 KB
13. Batch Normalization/5. Batch Normalization Tensorflow (part 2).mp414.92 MB
13. Batch Normalization/5. Batch Normalization Tensorflow (part 2).vtt5.92 KB
13. Batch Normalization/6. Batch Normalization Theano (part 1).mp47.65 MB
13. Batch Normalization/6. Batch Normalization Theano (part 1).vtt4.82 KB
13. Batch Normalization/7. Batch Normalization Theano (part 2).mp416.54 MB
13. Batch Normalization/7. Batch Normalization Theano (part 2).vtt6.98 KB
13. Batch Normalization/8. Noise Perspective.mp43.15 MB
13. Batch Normalization/8. Noise Perspective.vtt2.22 KB
13. Batch Normalization/9. Batch Normalization Summary.mp42.6 MB
13. Batch Normalization/9. Batch Normalization Summary.vtt1.87 KB
14. Keras/1. Keras Discussion.mp411.25 MB
14. Keras/1. Keras Discussion.vtt8.05 KB
14. Keras/2. Keras in Code.mp414.76 MB
14. Keras/2. Keras in Code.vtt6.46 KB
14. Keras/3. Keras Functional API.mp438.63 MB
14. Keras/3. Keras Functional API.vtt4.7 KB
15. PyTorch/1. PyTorch Basics.mp4116.8 MB
15. PyTorch/1. PyTorch Basics.vtt12.89 KB
15. PyTorch/2. PyTorch Dropout.mp432.69 MB
15. PyTorch/2. PyTorch Dropout.vtt2.62 KB
15. PyTorch/3. PyTorch Batch Norm.mp433.85 MB
15. PyTorch/3. PyTorch Batch Norm.vtt2.61 KB
16. PyTorch, CNTK, and MXNet/1. PyTorch, CNTK, and MXNet.mp41.31 MB
16. PyTorch, CNTK, and MXNet/1. PyTorch, CNTK, and MXNet.vtt947 B
17. Appendix/1. What is the Appendix.mp45.45 MB
17. Appendix/1. What is the Appendix.vtt3.28 KB
17. Appendix/10. Proof that using Jupyter Notebook is the same as not using it.mp478.29 MB
17. Appendix/10. Proof that using Jupyter Notebook is the same as not using it.vtt12.22 KB
17. Appendix/11. How to Uncompress a .tar.gz file.mp45.43 MB
17. Appendix/11. How to Uncompress a .tar.gz file.vtt3.66 KB
17. Appendix/12. Python 2 vs Python 3.mp47.83 MB
17. Appendix/12. Python 2 vs Python 3.vtt5.35 KB
17. Appendix/13. What order should I take your courses in (part 1).mp429.33 MB
17. Appendix/13. What order should I take your courses in (part 1).vtt14.09 KB
17. Appendix/14. What order should I take your courses in (part 2).mp437.62 MB
17. Appendix/14. What order should I take your courses in (part 2).vtt20.24 KB
17. Appendix/2. What's the difference between neural networks and deep learning.mp445.12 MB
17. Appendix/2. What's the difference between neural networks and deep learning.vtt8.91 KB
17. Appendix/3. Manually Choosing Learning Rate and Regularization Penalty.mp47.77 MB
17. Appendix/3. Manually Choosing Learning Rate and Regularization Penalty.vtt5.03 KB
17. Appendix/4. Windows-Focused Environment Setup 2018.mp4186.34 MB
17. Appendix/4. Windows-Focused Environment Setup 2018.vtt17.39 KB
17. Appendix/5. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92 MB
17. Appendix/5. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt12.4 KB
17. Appendix/6. How to Succeed in this Course (Long Version).mp412.99 MB
17. Appendix/6. How to Succeed in this Course (Long Version).vtt12.86 KB
17. Appendix/7. How to Code by Yourself (part 1).mp424.54 MB
17. Appendix/7. How to Code by Yourself (part 1).vtt19.78 KB
17. Appendix/8. How to Code by Yourself (part 2).mp414.81 MB
17. Appendix/8. How to Code by Yourself (part 2).vtt11.62 KB
17. Appendix/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96 MB
17. Appendix/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt27.77 KB
2. Review/1. Review of Basic Concepts.mp423.36 MB
2. Review/1. Review of Basic Concepts.vtt16.04 KB
2. Review/2. Where to get the MNIST dataset and Establishing a Linear Benchmark.mp411.12 MB
2. Review/2. Where to get the MNIST dataset and Establishing a Linear Benchmark.vtt4.19 KB
3. Gradient Descent Full vs Batch vs Stochastic/1. What are full, batch, and stochastic gradient descent.mp45.83 MB
3. Gradient Descent Full vs Batch vs Stochastic/1. What are full, batch, and stochastic gradient descent.vtt3.54 KB
3. Gradient Descent Full vs Batch vs Stochastic/2. Full vs Batch vs Stochastic Gradient Descent in code.mp413.99 MB
3. Gradient Descent Full vs Batch vs Stochastic/2. Full vs Batch vs Stochastic Gradient Descent in code.vtt5.81 KB
4. Momentum and adaptive learning rates/1. Using Momentum to Speed Up Training.mp410.67 MB
4. Momentum and adaptive learning rates/1. Using Momentum to Speed Up Training.vtt6.86 KB

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