FreeCoursesOnline Me PacktPub - Master Deep Learning with TensorFlow 2 0 in Python 2019 Video

1. 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp484.75MB
2. 01.Welcome! Course introduction/0102.What does the course cover.mp439.08MB
3. 02.Introduction to neural networks/0201.Introduction to neural networks.mp445.75MB
4. 02.Introduction to neural networks/0202.Training the model.mp426.82MB
5. 02.Introduction to neural networks/0203.Types of machine learning.mp440.85MB
6. 02.Introduction to neural networks/0204.The linear model.mp426.04MB
7. 02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp423.69MB
8. 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp442.21MB
9. 02.Introduction to neural networks/0207.Graphical representation.mp421.96MB
10. 02.Introduction to neural networks/0208.The objective function.mp417.7MB
11. 02.Introduction to neural networks/0209.L2-norm loss.mp421.4MB
12. 02.Introduction to neural networks/0210.Cross-entropy loss.mp433.4MB
13. 02.Introduction to neural networks/0211.One parameter gradient descent.mp456.41MB
14. 02.Introduction to neural networks/0212.N-parameter gradient descent.mp457.61MB
15. 03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp46.91MB
16. 03.Setting up the working environment/0302.Why Python and why Jupyter.mp434.69MB
17. 03.Setting up the working environment/0303.Installing Anaconda.mp431.33MB
18. 03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp49.24MB
19. 03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp420.37MB
20. 03.Setting up the working environment/0306.Installing TensorFlow 2.mp451.17MB
21. 04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp436.36MB
22. 04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp423.74MB
23. 04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp420.43MB
24. 04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp430.41MB
25. 05.TensorFlow - An introduction/0501.TensorFlow outline.mp441.97MB
26. 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp437.84MB
27. 05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp48.14MB
28. 05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp413.28MB
29. 05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp432.94MB
30. 05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp431.38MB
31. 05.TensorFlow - An introduction/0507.Customizing your model.mp421.62MB
32. 06.Going deeper Introduction to deep neural networks/0601.Layers.mp420.55MB
33. 06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp432.6MB
34. 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp458.18MB
35. 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp437.97MB
36. 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp437.97MB
37. 06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp424.98MB
38. 06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp452.73MB
39. 06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp424.39MB
40. 07.Overfitting/0701.Underfitting and overfitting.mp434.06MB
41. 07.Overfitting/0702.Underfitting and overfitting - classification.mp432.48MB
42. 07.Overfitting/0703.Training and validation.mp437.52MB
43. 07.Overfitting/0704.Training, validation, and test.mp431.32MB
44. 07.Overfitting/0705.N-fold cross validation.mp425.57MB
45. 07.Overfitting/0706.Early stopping.mp428.33MB
46. 08.Initialization/0801.Initialization - Introduction.mp426.17MB
47. 08.Initialization/0802.Types of simple initializations.mp412.29MB
48. 08.Initialization/0803.Xavier initialization.mp419.12MB
49. 09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp434.48MB
50. 09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp414.35MB
51. 09.Gradient descent and learning rates/0903.Momentum.mp418.96MB
52. 09.Gradient descent and learning rates/0904.Learning rate schedules.mp437.08MB
53. 09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp410.93MB
54. 09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp429.83MB
55. 09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp429.08MB
56. 10.Preprocessing/1001.Preprocessing introduction.mp425.55MB
57. 10.Preprocessing/1002.Basic preprocessing.mp411.11MB
58. 10.Preprocessing/1003.Standardization.mp440.37MB
59. 10.Preprocessing/1004.Dealing with categorical data.mp418.22MB
60. 10.Preprocessing/1005.One-hot and binary encoding.mp432.26MB
61. 11.The MNIST example/1101.The dataset.mp420.74MB
62. 11.The MNIST example/1102.How to tackle the MNIST.mp433.29MB
63. 11.The MNIST example/1103.Importing the relevant packages and load the data.mp415.85MB
64. 11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp427.05MB
65. 11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp436.58MB
66. 11.The MNIST example/1106.Outline the model.mp427.36MB
67. 11.The MNIST example/1107.Select the loss and the optimizer.mp412.71MB
68. 11.The MNIST example/1108.Learning.mp420.43MB
69. 11.The MNIST example/1109.Testing the model.mp415.26MB
70. 12.Business case/1201.Exploring the dataset and identifying predictors.mp430.16MB
71. 12.Business case/1202.Outlining the business case solution.mp49.52MB
72. 12.Business case/1203.Balancing the dataset.mp413.75MB
73. 12.Business case/1204.Preprocessing the data.mp444.52MB
74. 12.Business case/1205.Load the preprocessed data.mp418.22MB
75. 12.Business case/1206.Learning and interpreting the result.mp426.4MB
76. 12.Business case/1207.Setting an early stopping mechanism.mp421.45MB
77. 12.Business case/1208.Testing the model.mp49.63MB
78. 13.Conclusion/1301.See how much you have learned.mp438.88MB
79. 13.Conclusion/1302.What's further out there in the machine and deep learning world.mp417.51MB
80. 13.Conclusion/1303.An overview of CNNs.mp418.62MB
81. 13.Conclusion/1304.An overview of RNNs.mp427.42MB
82. 13.Conclusion/1305.An overview of non-NN approaches.mp440.17MB