DesireCourse Net Udemy - Master Deep Learning with TensorFlow in Python

1. 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4105.79MB
2. 1. Welcome! Course introduction/2. What does the course cover.mp416.36MB
3. 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp49.38MB
4. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp44.31MB
5. 10. Gradient descent and learning rates/3. Momentum.mp46.11MB
6. 10. Gradient descent and learning rates/4. Learning rate schedules.mp410.3MB
7. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp43.15MB
8. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp48.86MB
9. 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp47.78MB
10. 11. Preprocessing/1. Preprocessing introduction.mp48.42MB
11. 11. Preprocessing/2. Basic preprocessing.mp43.65MB
12. 11. Preprocessing/3. Standardization.mp48.33MB
13. 11. Preprocessing/4. Dealing with categorical data.mp46.08MB
14. 11. Preprocessing/5. One-hot and binary encoding.mp46.24MB
15. 12. The MNIST example/1. The dataset.mp47.37MB
16. 12. The MNIST example/2. How to tackle the MNIST.mp47.3MB
17. 12. The MNIST example/3. Importing the relevant packages.mp45.46MB
18. 12. The MNIST example/4. Outlining the model.mp418.37MB
19. 12. The MNIST example/5. Declaring the loss and the optimization algorithm.mp47.14MB
20. 12. The MNIST example/6. Accuracy of prediction.mp412.38MB
21. 12. The MNIST example/7. Batching and early stopping.mp44.58MB
22. 12. The MNIST example/8. Learning.mp415.9MB
23. 12. The MNIST example/9. Discuss the results and test.mp421.97MB
24. 13. Business case/1. Exploring the dataset and identifying predictors.mp423.26MB
25. 13. Business case/10. Testing the model.mp44.29MB
26. 13. Business case/11. A comment on the homework.mp413.01MB
27. 13. Business case/2. Outlining the business case solution.mp43.84MB
28. 13. Business case/3. Balancing the dataset.mp413.81MB
29. 13. Business case/4. Preprocessing the data.mp434.33MB
30. 13. Business case/6. Create a class for batching.mp427.65MB
31. 13. Business case/7. Outlining the model.mp419.46MB
32. 13. Business case/8. Optimizing the algorithm.mp412.22MB
33. 13. Business case/9. Interpreting the result.mp45.35MB
34. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp433.59MB
35. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp449.38MB
36. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4144.33MB
37. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp433.84MB
38. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp449.8MB
39. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp426.67MB
40. 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp422.52MB
41. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp432.61MB
42. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp411.17MB
43. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp438.08MB
44. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp423.99MB
45. 15. Conclusion/1. See how much you have learned.mp413.96MB
46. 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp46.27MB
47. 15. Conclusion/3. An overview of CNNs.mp410.93MB
48. 15. Conclusion/5. An overview of RNNs.mp44.86MB
49. 15. Conclusion/6. An overview of non-NN approaches.mp47.84MB
50. 2. Introduction to neural networks/1. Introduction to neural networks.mp413.56MB
51. 2. Introduction to neural networks/10. The linear model. Multiple inputs.mp47.5MB
52. 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp438.29MB
53. 2. Introduction to neural networks/14. Graphical representation.mp46.35MB
54. 2. Introduction to neural networks/16. The objective function.mp45.72MB
55. 2. Introduction to neural networks/18. L2-norm loss.mp47.27MB
56. 2. Introduction to neural networks/20. Cross-entropy loss.mp411.36MB
57. 2. Introduction to neural networks/22. One parameter gradient descent.mp417.76MB
58. 2. Introduction to neural networks/24. N-parameter gradient descent.mp439.46MB
59. 2. Introduction to neural networks/3. Training the model.mp48.81MB
60. 2. Introduction to neural networks/5. Types of machine learning.mp412.21MB
61. 2. Introduction to neural networks/7. The linear model.mp49.13MB
62. 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp42.62MB
63. 3. Setting up the working environment/2. Why Python and why Jupyter.mp413.63MB
64. 3. Setting up the working environment/4. Installing Anaconda.mp49.39MB
65. 3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp45.59MB
66. 3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp410.92MB
67. 3. Setting up the working environment/9. Installing the TensorFlow package.mp44.86MB
68. 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp46.54MB
69. 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp410.71MB
70. 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp49.76MB
71. 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp420.8MB
72. 5. TensorFlow - An introduction/1. TensorFlow outline.mp414.47MB
73. 5. TensorFlow - An introduction/2. TensorFlow intro.mp47.54MB
74. 5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.mp45.83MB
75. 5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.mp412.95MB
76. 5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.mp49.7MB
77. 5. TensorFlow - An introduction/6. Model output.mp414.33MB
78. 6. Going deeper Introduction to deep neural networks/1. Layers.mp44.74MB
79. 6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp46.72MB
80. 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp413.41MB
81. 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp48.96MB
82. 6. Going deeper Introduction to deep neural networks/5. Activation functions.mp48.74MB
83. 6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp47.37MB
84. 6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp411.06MB
85. 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp46.85MB
86. 8. Overfitting/1. Underfitting and overfitting.mp411.06MB
87. 8. Overfitting/2. Underfitting and overfitting - classification.mp46.76MB
88. 8. Overfitting/3. Training and validation.mp49.24MB
89. 8. Overfitting/4. Training, validation, and test.mp47.44MB
90. 8. Overfitting/5. N-fold cross validation.mp46.99MB
91. 8. Overfitting/6. Early stopping.mp49.43MB
92. 9. Initialization/1. Initialization - Introduction.mp48.04MB
93. 9. Initialization/2. Types of simple initializations.mp45.62MB
94. 9. Initialization/3. Xavier initialization.mp45.82MB