GigaCourse com Udemy - Deep Learning Prerequisites Logistic Regression in Python

1. 1. Start Here/1. Introduction and Outline.mp446.92MB
2. 1. Start Here/2. How to Succeed in this Course.mp46.41MB
3. 1. Start Here/3. Review of the classification problem.mp42.97MB
4. 1. Start Here/4. Introduction to the E-Commerce Course Project.mp414.79MB
5. 2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp47.55MB
6. 2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp49.39MB
7. 2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.mp415.22MB
8. 2. Basics What is linear classification What's the relation to neural networks/4. How do we calculate the output of a neuron logistic classifier - Code.mp45.82MB
9. 2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp427.89MB
10. 2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp411.17MB
11. 2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp45.7MB
12. 2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp42.27MB
13. 2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp42.22MB
14. 3. Solving for the optimal weights/1. Training Section Introduction.mp42.81MB
15. 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp417.06MB
16. 3. Solving for the optimal weights/11. Training Section Summary.mp43.39MB
17. 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp49.11MB
18. 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp46.37MB
19. 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp44.5MB
20. 3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp49.1MB
21. 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp45.27MB
22. 3. Solving for the optimal weights/7. Maximizing the likelihood.mp425.22MB
23. 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp49.35MB
24. 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp47.26MB
25. 4. Practical concerns/1. Practical Section Introduction.mp44.73MB
26. 4. Practical concerns/10. Why Divide by Square Root of D.mp423.48MB
27. 4. Practical concerns/11. Practical Section Summary.mp43.41MB
28. 4. Practical concerns/2. Interpreting the Weights.mp46.34MB
29. 4. Practical concerns/3. L2 Regularization - Theory.mp414.7MB
30. 4. Practical concerns/4. L2 Regularization - Code.mp44.47MB
31. 4. Practical concerns/5. L1 Regularization - Theory.mp44.42MB
32. 4. Practical concerns/6. L1 Regularization - Code.mp412.01MB
33. 4. Practical concerns/7. L1 vs L2 Regularization.mp44.8MB
34. 4. Practical concerns/8. The donut problem.mp424.69MB
35. 4. Practical concerns/9. The XOR problem.mp414.21MB
36. 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.mp411.41MB
37. 5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03MB
38. 5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.mp45.26MB
39. 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp49.82MB
40. 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp421.44MB
41. 6. Project Facial Expression Recognition/3. The class imbalance problem.mp410.11MB
42. 6. Project Facial Expression Recognition/4. Utilities walkthrough.mp413.49MB
43. 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp424.05MB
44. 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp42.92MB
45. 7. Appendix FAQ/1. What is the Appendix.mp45.45MB
46. 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp478.28MB
47. 7. Appendix FAQ/11. Python 2 vs Python 3.mp47.84MB
48. 7. Appendix FAQ/12. What order should I take your courses in (part 1).mp429.32MB
49. 7. Appendix FAQ/13. What order should I take your courses in (part 2).mp437.62MB
50. 7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB
51. 7. Appendix FAQ/2. Gradient Descent Tutorial.mp422.82MB
52. 7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4186.28MB
53. 7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
54. 7. Appendix FAQ/5. How to Code by Yourself (part 1).mp424.53MB
55. 7. Appendix FAQ/6. How to Code by Yourself (part 2).mp414.8MB
56. 7. Appendix FAQ/7. How to Uncompress a .tar.gz file.mp45.43MB
57. 7. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp412.99MB
58. 7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB