UdemyCourseDownloader Deep Learning with TensorFlow 2 0 2019

1. 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4144.34MB
2. 01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4105.79MB
3. 01. Welcome! Course introduction/2. What does the course cover.mp416.36MB
4. 02. Introduction to neural networks/1. Introduction to neural networks.mp413.56MB
5. 02. Introduction to neural networks/3. Training the model.mp48.82MB
6. 02. Introduction to neural networks/5. Types of machine learning.mp412.2MB
7. 02. Introduction to neural networks/7. The linear model.mp49.13MB
8. 02. Introduction to neural networks/10. The linear model. Multiple inputs.mp47.5MB
9. 02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp438.29MB
10. 02. Introduction to neural networks/14. Graphical representation.mp46.35MB
11. 02. Introduction to neural networks/16. The objective function.mp45.72MB
12. 02. Introduction to neural networks/18. L2-norm loss.mp47.26MB
13. 02. Introduction to neural networks/20. Cross-entropy loss.mp411.36MB
14. 02. Introduction to neural networks/22. One parameter gradient descent.mp417.77MB
15. 02. Introduction to neural networks/24. N-parameter gradient descent.mp439.45MB
16. 03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp47.18MB
17. 03. Setting up the working environment/2. Why Python and why Jupyter.mp441.02MB
18. 03. Setting up the working environment/4. Installing Anaconda.mp434.91MB
19. 03. Setting up the working environment/5. The Jupyter dashboard - part 1.mp49.5MB
20. 03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp421.08MB
21. 03. Setting up the working environment/9. Installing TensorFlow 2.mp442.94MB
22. 04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp46.53MB
23. 04. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp410.71MB
24. 04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp49.77MB
25. 04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp420.81MB
26. 05. TensorFlow - An introduction/1. TensorFlow outline.mp438.32MB
27. 05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp425.07MB
28. 05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp47.13MB
29. 05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp418.5MB
30. 05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp438.22MB
31. 05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp432.82MB
32. 05. TensorFlow - An introduction/7. Cutomizing your model.mp424.66MB
33. 06. Going deeper Introduction to deep neural networks/1. Layers.mp44.74MB
34. 06. Going deeper Introduction to deep neural networks/2. What is a deep net.mp46.73MB
35. 06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp413.41MB
36. 06. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp48.96MB
37. 06. Going deeper Introduction to deep neural networks/5. Activation functions.mp48.74MB
38. 06. Going deeper Introduction to deep neural networks/6. Softmax activation.mp47.38MB
39. 06. Going deeper Introduction to deep neural networks/7. Backpropagation.mp411.06MB
40. 06. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp46.85MB
41. 08. Overfitting/1. Underfitting and overfitting.mp411.06MB
42. 08. Overfitting/2. Underfitting and overfitting - classification.mp46.77MB
43. 08. Overfitting/3. Training and validation.mp49.23MB
44. 08. Overfitting/4. Training, validation, and test.mp47.45MB
45. 08. Overfitting/5. N-fold cross validation.mp46.99MB
46. 08. Overfitting/6. Early stopping.mp49.44MB
47. 09. Initialization/1. Initialization - Introduction.mp48.03MB
48. 09. Initialization/2. Types of simple initializations.mp45.61MB
49. 09. Initialization/3. Xavier initialization.mp45.83MB
50. 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp49.39MB
51. 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp44.31MB
52. 10. Gradient descent and learning rates/3. Momentum.mp46.11MB
53. 10. Gradient descent and learning rates/4. Learning rate schedules.mp410.31MB
54. 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp43.14MB
55. 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp48.86MB
56. 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp47.78MB
57. 11. Preprocessing/1. Preprocessing introduction.mp48.42MB
58. 11. Preprocessing/2. Basic preprocessing.mp43.65MB
59. 11. Preprocessing/3. Standardization.mp48.33MB
60. 11. Preprocessing/4. Dealing with categorical data.mp46.08MB
61. 11. Preprocessing/5. One-hot and binary encoding.mp46.24MB
62. 12. The MNIST example/1. The dataset.mp415.67MB
63. 12. The MNIST example/2. How to tackle the MNIST.mp420.4MB
64. 12. The MNIST example/3. Importing the relevant packages and load the data.mp417.77MB
65. 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp431.94MB
66. 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp445.93MB
67. 12. The MNIST example/8. Outline the model.mp431.17MB
68. 12. The MNIST example/9. Select the loss and the optimizer.mp415.26MB
69. 12. The MNIST example/10. Learning.mp444.47MB
70. 12. The MNIST example/13. Testing the model.mp432.49MB
71. 13. Business case/1. Exploring the dataset and identifying predictors.mp478.16MB
72. 13. Business case/2. Outlining the business case solution.mp47.95MB
73. 13. Business case/3. Balancing the dataset.mp435.19MB
74. 13. Business case/4. Preprocessing the data.mp492MB
75. 13. Business case/6. Load the preprocessed data.mp419.38MB
76. 13. Business case/8. Learning and interpreting the result.mp434.6MB
77. 13. Business case/9. Setting an early stopping mechanism.mp453.36MB
78. 13. Business case/11. Testing the model.mp412.07MB
79. 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp433.59MB
80. 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp433.84MB
81. 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp449.79MB
82. 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp426.67MB
83. 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp422.51MB
84. 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp432.61MB
85. 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp411.17MB
86. 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp438.09MB
87. 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp423.99MB
88. 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp449.38MB
89. 15. Conclusion/1. See how much you have learned.mp413.96MB
90. 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp46.26MB
91. 15. Conclusion/3. An overview of CNNs.mp410.93MB
92. 15. Conclusion/5. An overview of RNNs.mp44.86MB
93. 15. Conclusion/6. An overview of non-NN approaches.mp47.85MB