UdemyCourseDownloader Deep Learning Prerequisites Logistic Regression in Python

1. 07 Appendix/036 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp443.92MB
2. 01 Start Here/001 Introduction and Outline.mp47.52MB
3. 01 Start Here/002 How to Succeed in this Course.mp48.78MB
4. 01 Start Here/003 Review of the classification problem.mp42.97MB
5. 01 Start Here/004 Introduction to the E-Commerce Course Project.mp414.79MB
6. 02 Basics What is linear classification Whats the relation to neural networks/005 Linear Classification.mp47.54MB
7. 02 Basics What is linear classification Whats the relation to neural networks/006 Biological inspiration - the neuron.mp44.17MB
8. 02 Basics What is linear classification Whats the relation to neural networks/007 How do we calculate the output of a neuron logistic classifier - Theory.mp47.48MB
9. 02 Basics What is linear classification Whats the relation to neural networks/008 How do we calculate the output of a neuron logistic classifier - Code.mp45.82MB
10. 02 Basics What is linear classification Whats the relation to neural networks/009 E-Commerce Course Project Pre-Processing the Data.mp411.16MB
11. 02 Basics What is linear classification Whats the relation to neural networks/010 E-Commerce Course Project Making Predictions.mp45.7MB
12. 03 Solving for the optimal weights/011 A closed-form solution to the Bayes classifier.mp49.99MB
13. 03 Solving for the optimal weights/012 What do all these symbols mean X Y N D L J PY1X etc..mp46.36MB
14. 03 Solving for the optimal weights/013 The cross-entropy error function - Theory.mp44.49MB
15. 03 Solving for the optimal weights/014 The cross-entropy error function - Code.mp49.1MB
16. 03 Solving for the optimal weights/015 Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp45.27MB
17. 03 Solving for the optimal weights/016 Maximizing the likelihood.mp412.67MB
18. 03 Solving for the optimal weights/017 Updating the weights using gradient descent - Theory.mp49.35MB
19. 03 Solving for the optimal weights/018 Updating the weights using gradient descent - Code.mp47.25MB
20. 03 Solving for the optimal weights/019 E-Commerce Course Project Training the Logistic Model.mp417.06MB
21. 04 Practical concerns/020 Interpreting the Weights.mp46.34MB
22. 04 Practical concerns/021 L2 Regularization - Theory.mp414.7MB
23. 04 Practical concerns/022 L2 Regularization - Code.mp44.46MB
24. 04 Practical concerns/023 L1 Regularization - Theory.mp44.42MB
25. 04 Practical concerns/024 L1 Regularization - Code.mp412.01MB
26. 04 Practical concerns/025 L1 vs L2 Regularization.mp44.8MB
27. 04 Practical concerns/026 The donut problem.mp424.68MB
28. 04 Practical concerns/027 The XOR problem.mp414.2MB
29. 05 Checkpoint and applications How to make sure you know your stuff/028 BONUS Sentiment Analysis.mp411.4MB
30. 05 Checkpoint and applications How to make sure you know your stuff/029 BONUS Where to get Udemy coupons and FREE deep learning material.mp44.02MB
31. 05 Checkpoint and applications How to make sure you know your stuff/030 BONUS Exercises how to get good at this.mp45.26MB
32. 06 Project Facial Expression Recognition/031 Facial Expression Recognition Problem Description.mp421.43MB
33. 06 Project Facial Expression Recognition/032 The class imbalance problem.mp410.11MB
34. 06 Project Facial Expression Recognition/033 Utilities walkthrough.mp413.48MB
35. 06 Project Facial Expression Recognition/034 Facial Expression Recognition in Code.mp424.04MB
36. 07 Appendix/035 Gradient Descent Tutorial.mp48.43MB
37. 07 Appendix/037 How to Code by Yourself part 1.mp424.53MB
38. 07 Appendix/038 How to Code by Yourself part 2.mp414.8MB