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[] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]

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  • 文件大小:2GB
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文件列表

  1. 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4 85MB
  2. 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4 58MB
  3. 02.Introduction to neural networks/0212.N-parameter gradient descent.mp4 58MB
  4. 02.Introduction to neural networks/0211.One parameter gradient descent.mp4 56MB
  5. 06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp4 53MB
  6. 03.Setting up the working environment/0306.Installing TensorFlow 2.mp4 51MB
  7. 02.Introduction to neural networks/0201.Introduction to neural networks.mp4 46MB
  8. 12.Business case/1204.Preprocessing the data.mp4 45MB
  9. 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4 42MB
  10. 05.TensorFlow - An introduction/0501.TensorFlow outline.mp4 42MB
  11. 02.Introduction to neural networks/0203.Types of machine learning.mp4 41MB
  12. 10.Preprocessing/1003.Standardization.mp4 40MB
  13. 13.Conclusion/1305.An overview of non-NN approaches.mp4 40MB
  14. 01.Welcome! Course introduction/0102.What does the course cover.mp4 39MB
  15. 13.Conclusion/1301.See how much you have learned.mp4 39MB
  16. 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4 38MB
  17. 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4 38MB
  18. 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4 38MB
  19. 07.Overfitting/0703.Training and validation.mp4 38MB
  20. 09.Gradient descent and learning rates/0904.Learning rate schedules.mp4 37MB
  21. 11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp4 37MB
  22. 04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp4 36MB
  23. 03.Setting up the working environment/0302.Why Python and why Jupyter.mp4 35MB
  24. 09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp4 34MB
  25. 07.Overfitting/0701.Underfitting and overfitting.mp4 34MB
  26. 02.Introduction to neural networks/0210.Cross-entropy loss.mp4 33MB
  27. 11.The MNIST example/1102.How to tackle the MNIST.mp4 33MB
  28. 05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 33MB
  29. 06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp4 33MB
  30. 07.Overfitting/0702.Underfitting and overfitting - classification.mp4 32MB
  31. 10.Preprocessing/1005.One-hot and binary encoding.mp4 32MB
  32. 05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp4 31MB
  33. 03.Setting up the working environment/0303.Installing Anaconda.mp4 31MB
  34. 07.Overfitting/0704.Training, validation, and test.mp4 31MB
  35. 04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp4 30MB
  36. 12.Business case/1201.Exploring the dataset and identifying predictors.mp4 30MB
  37. 09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp4 30MB
  38. 09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp4 29MB
  39. 07.Overfitting/0706.Early stopping.mp4 28MB
  40. 13.Conclusion/1304.An overview of RNNs.mp4 27MB
  41. 11.The MNIST example/1106.Outline the model.mp4 27MB
  42. 11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp4 27MB
  43. 02.Introduction to neural networks/0202.Training the model.mp4 27MB
  44. 12.Business case/1206.Learning and interpreting the result.mp4 26MB
  45. 08.Initialization/0801.Initialization - Introduction.mp4 26MB
  46. 02.Introduction to neural networks/0204.The linear model.mp4 26MB
  47. 07.Overfitting/0705.N-fold cross validation.mp4 26MB
  48. 10.Preprocessing/1001.Preprocessing introduction.mp4 26MB
  49. 06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp4 25MB
  50. 06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp4 24MB
  51. 04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp4 24MB
  52. 02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp4 24MB
  53. 02.Introduction to neural networks/0207.Graphical representation.mp4 22MB
  54. 05.TensorFlow - An introduction/0507.Customizing your model.mp4 22MB
  55. 12.Business case/1207.Setting an early stopping mechanism.mp4 21MB
  56. 02.Introduction to neural networks/0209.L2-norm loss.mp4 21MB
  57. 11.The MNIST example/1101.The dataset.mp4 21MB
  58. 06.Going deeper Introduction to deep neural networks/0601.Layers.mp4 21MB
  59. 11.The MNIST example/1108.Learning.mp4 20MB
  60. 04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp4 20MB
  61. 03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp4 20MB
  62. 08.Initialization/0803.Xavier initialization.mp4 19MB
  63. 09.Gradient descent and learning rates/0903.Momentum.mp4 19MB
  64. 13.Conclusion/1303.An overview of CNNs.mp4 19MB
  65. 10.Preprocessing/1004.Dealing with categorical data.mp4 18MB
  66. 12.Business case/1205.Load the preprocessed data.mp4 18MB
  67. 02.Introduction to neural networks/0208.The objective function.mp4 18MB
  68. 13.Conclusion/1302.What's further out there in the machine and deep learning world.mp4 18MB
  69. 11.The MNIST example/1103.Importing the relevant packages and load the data.mp4 16MB
  70. 11.The MNIST example/1109.Testing the model.mp4 15MB
  71. 09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp4 14MB
  72. 12.Business case/1203.Balancing the dataset.mp4 14MB
  73. 05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp4 13MB
  74. 11.The MNIST example/1107.Select the loss and the optimizer.mp4 13MB
  75. 08.Initialization/0802.Types of simple initializations.mp4 12MB
  76. 10.Preprocessing/1002.Basic preprocessing.mp4 11MB
  77. 09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp4 11MB
  78. 12.Business case/1208.Testing the model.mp4 10MB
  79. 12.Business case/1202.Outlining the business case solution.mp4 10MB
  80. 03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp4 9MB
  81. 05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp4 8MB
  82. 03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp4 7MB
  83. [FCS Forum].url 133B
  84. [FreeCourseSite.com].url 127B
  85. [CourseClub.ME].url 122B