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[] Udemy - Artificial Neural Networks for Business Managers in R Studio

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文件列表

  1. 13 - Saving and Restoring Models/29 - Saving Restoring Models and Using Callbacks.mp4 216MB
  2. 12 - R Complex ANN Architectures using Functional API/27 - Building Regression Model with Functional AP.mp4 131MB
  3. 10 - R Building and training the Model/24 - BuildingCompiling and Training.mp4 131MB
  4. 4 - Neural Networks Stacking cells to create network/17 - Back Propagation.mp4 122MB
  5. 9 - R Dataset for classification problem/22 - Data Normalization and TestTrain Split.mp4 112MB
  6. 15 - Addon 1 Data Preprocessing/42 - Bivariate Analysis and Variable Transformation.mp4 100MB
  7. 10 - R Building and training the Model/25 - Evaluating and Predicting.mp4 99MB
  8. 15 - Addon 1 Data Preprocessing/36 - EDD in R.mp4 97MB
  9. 2 - Setting Up R Studio and R crash course/11 - Creating Barplots in R.mp4 97MB
  10. 16 - Linear Regression Model/51 - Assessing Accuracy of predicted coefficients.mp4 92MB
  11. 11 - The NeuralNets Package/26 - ANN with NeuralNets Package.mp4 84MB
  12. 15 - Addon 1 Data Preprocessing/48 - Correlation Matrix in R.mp4 83MB
  13. 2 - Setting Up R Studio and R crash course/7 - Packages in R.mp4 83MB
  14. 12 - R Complex ANN Architectures using Functional API/28 - Complex Architectures using Functional API.mp4 80MB
  15. 16 - Linear Regression Model/60 - TestTrain Split in R.mp4 76MB
  16. 15 - Addon 1 Data Preprocessing/47 - Correlation Matrix and causeeffect relationship.mp4 72MB
  17. 15 - Addon 1 Data Preprocessing/33 - The Data and the Data Dictionary.mp4 69MB
  18. 16 - Linear Regression Model/57 - Multiple Linear Regression in R.mp4 62MB
  19. 5 - Important concepts Common Interview questions/18 - Some Important Concepts.mp4 62MB
  20. 14 - Hyperparameter Tuning/30 - Hyperparameter Tuning.mp4 61MB
  21. 4 - Neural Networks Stacking cells to create network/16 - Gradient Descent.mp4 60MB
  22. 2 - Setting Up R Studio and R crash course/10 - Inputting data part 3 Importing from CSV or Text files.mp4 60MB
  23. 16 - Linear Regression Model/55 - The F statistic.mp4 56MB
  24. 15 - Addon 1 Data Preprocessing/43 - Variable transformation in R.mp4 55MB
  25. 6 - Standard Model Parameters/19 - Hyperparameters.mp4 45MB
  26. 3 - Single Cells Perceptron and Sigmoid Neuron/13 - Perceptron.mp4 45MB
  27. 15 - Addon 1 Data Preprocessing/46 - Dummy variable creation in R.mp4 44MB
  28. 16 - Linear Regression Model/52 - Assessing Model Accuracy RSE and R squared.mp4 44MB
  29. 16 - Linear Regression Model/50 - Basic equations and Ordinary Least Squared OLS method.mp4 43MB
  30. 2 - Setting Up R Studio and R crash course/12 - Creating Histograms in R.mp4 42MB
  31. 16 - Linear Regression Model/58 - TestTrain split.mp4 42MB
  32. 16 - Linear Regression Model/53 - Simple Linear Regression in R.mp4 41MB
  33. 2 - Setting Up R Studio and R crash course/8 - Inputting data part 1 Inbuilt datasets of R.mp4 41MB
  34. 4 - Neural Networks Stacking cells to create network/15 - Basic Terminologies.mp4 40MB
  35. 2 - Setting Up R Studio and R crash course/6 - Basics of R and R studio.mp4 39MB
  36. 15 - Addon 1 Data Preprocessing/45 - Dummy variable creation Handling qualitative data.mp4 37MB
  37. 2 - Setting Up R Studio and R crash course/5 - Installing R and R studio.mp4 36MB
  38. 3 - Single Cells Perceptron and Sigmoid Neuron/14 - Activation Functions.mp4 35MB
  39. 16 - Linear Regression Model/54 - Multiple Linear Regression.mp4 34MB
  40. 15 - Addon 1 Data Preprocessing/38 - Outlier Treatment in R.mp4 31MB
  41. 1 - Introduction/2 - Introduction to Neural Networks and Course flow.mp4 29MB
  42. 15 - Addon 1 Data Preprocessing/40 - Missing Value imputation in R.mp4 26MB
  43. 2 - Setting Up R Studio and R crash course/9 - Inputting data part 2 Manual data entry.mp4 26MB
  44. 16 - Linear Regression Model/59 - Bias Variance tradeoff.mp4 25MB
  45. 15 - Addon 1 Data Preprocessing/39 - Missing Value imputation.mp4 25MB
  46. 15 - Addon 1 Data Preprocessing/37 - Outlier Treatment.mp4 24MB
  47. 15 - Addon 1 Data Preprocessing/35 - Univariate Analysis and EDD.mp4 24MB
  48. 8 - Tensorflow and Keras/21 - Installing Keras and Tensorflow.mp4 23MB
  49. 16 - Linear Regression Model/56 - Interpreting result for categorical Variable.mp4 23MB
  50. 1 - Introduction/1 - Welcome to the course.mp4 21MB
  51. 1 - Introduction/4 - This is a milestone.mp4 21MB
  52. 15 - Addon 1 Data Preprocessing/44 - Non Usable Variables.mp4 20MB
  53. 15 - Addon 1 Data Preprocessing/32 - Data Exploration.mp4 20MB
  54. 15 - Addon 1 Data Preprocessing/41 - Seasonality in Data.mp4 17MB
  55. 8 - Tensorflow and Keras/20 - Keras and Tensorflow.mp4 15MB
  56. 15 - Addon 1 Data Preprocessing/31 - Gathering Business Knowledge.mp4 15MB
  57. 15 - Addon 1 Data Preprocessing/34 - Importing the dataset into R.mp4 13MB
  58. 18 - Congratulations & about your certificate/61 - The final milestone.mp4 12MB
  59. 16 - Linear Regression Model/49 - The problem statement.mp4 9MB
  60. 4 - Neural Networks Stacking cells to create network/17 - Back Propagation English.vtt 22KB
  61. 13 - Saving and Restoring Models/29 - Saving Restoring Models and Using Callbacks English.vtt 19KB
  62. 16 - Linear Regression Model/51 - Assessing Accuracy of predicted coefficients English.vtt 18KB
  63. 15 - Addon 1 Data Preprocessing/42 - Bivariate Analysis and Variable Transformation English.vtt 18KB
  64. 2 - Setting Up R Studio and R crash course/11 - Creating Barplots in R English.vtt 16KB
  65. 10 - R Building and training the Model/24 - BuildingCompiling and Training English.vtt 15KB
  66. 2 - Setting Up R Studio and R crash course/7 - Packages in R English.vtt 13KB
  67. 2 - Setting Up R Studio and R crash course/6 - Basics of R and R studio English.vtt 13KB
  68. 5 - Important concepts Common Interview questions/18 - Some Important Concepts English.vtt 12KB
  69. 12 - R Complex ANN Architectures using Functional API/27 - Building Regression Model with Functional AP English.vtt 12KB
  70. 15 - Addon 1 Data Preprocessing/36 - EDD in R English.vtt 12KB
  71. 16 - Linear Regression Model/58 - TestTrain split English.vtt 11KB
  72. 9 - R Dataset for classification problem/22 - Data Normalization and TestTrain Split English.vtt 11KB
  73. 4 - Neural Networks Stacking cells to create network/16 - Gradient Descent English.vtt 11KB
  74. 16 - Linear Regression Model/50 - Basic equations and Ordinary Least Squared OLS method English.vtt 11KB
  75. 15 - Addon 1 Data Preprocessing/47 - Correlation Matrix and causeeffect relationship English.vtt 11KB
  76. 16 - Linear Regression Model/55 - The F statistic English.vtt 10KB
  77. 4 - Neural Networks Stacking cells to create network/15 - Basic Terminologies English.vtt 10KB
  78. 3 - Single Cells Perceptron and Sigmoid Neuron/13 - Perceptron English.vtt 9KB
  79. 10 - R Building and training the Model/25 - Evaluating and Predicting English.vtt 9KB
  80. 14 - Hyperparameter Tuning/30 - Hyperparameter Tuning English.vtt 9KB
  81. 15 - Addon 1 Data Preprocessing/48 - Correlation Matrix in R English.vtt 9KB
  82. 6 - Standard Model Parameters/19 - Hyperparameters English.vtt 8KB
  83. 16 - Linear Regression Model/52 - Assessing Model Accuracy RSE and R squared English.vtt 8KB
  84. 16 - Linear Regression Model/53 - Simple Linear Regression in R English.vtt 8KB
  85. 16 - Linear Regression Model/57 - Multiple Linear Regression in R English.vtt 8KB
  86. 16 - Linear Regression Model/60 - TestTrain Split in R English.vtt 8KB
  87. 12 - R Complex ANN Architectures using Functional API/28 - Complex Architectures using Functional API English.vtt 8KB
  88. 15 - Addon 1 Data Preprocessing/43 - Variable transformation in R English.vtt 8KB
  89. 11 - The NeuralNets Package/26 - ANN with NeuralNets Package English.vtt 8KB
  90. 2 - Setting Up R Studio and R crash course/10 - Inputting data part 3 Importing from CSV or Text files English.vtt 8KB
  91. 16 - Linear Regression Model/59 - Bias Variance tradeoff English.vtt 7KB
  92. 3 - Single Cells Perceptron and Sigmoid Neuron/14 - Activation Functions English.vtt 7KB
  93. 15 - Addon 1 Data Preprocessing/33 - The Data and the Data Dictionary English.vtt 7KB
  94. 2 - Setting Up R Studio and R crash course/12 - Creating Histograms in R English.vtt 7KB
  95. 16 - Linear Regression Model/54 - Multiple Linear Regression English.vtt 7KB
  96. 2 - Setting Up R Studio and R crash course/5 - Installing R and R studio English.vtt 7KB
  97. 16 - Linear Regression Model/56 - Interpreting result for categorical Variable English.vtt 6KB
  98. 15 - Addon 1 Data Preprocessing/44 - Non Usable Variables English.vtt 5KB
  99. 15 - Addon 1 Data Preprocessing/46 - Dummy variable creation in R English.vtt 5KB
  100. 2 - Setting Up R Studio and R crash course/8 - Inputting data part 1 Inbuilt datasets of R English.vtt 5KB
  101. 15 - Addon 1 Data Preprocessing/45 - Dummy variable creation Handling qualitative data English.vtt 5KB
  102. 15 - Addon 1 Data Preprocessing/37 - Outlier Treatment English.vtt 4KB
  103. 1 - Introduction/2 - Introduction to Neural Networks and Course flow English.vtt 4KB
  104. 15 - Addon 1 Data Preprocessing/38 - Outlier Treatment in R English.vtt 4KB
  105. 15 - Addon 1 Data Preprocessing/39 - Missing Value imputation English.vtt 4KB
  106. 15 - Addon 1 Data Preprocessing/41 - Seasonality in Data English.vtt 4KB
  107. 15 - Addon 1 Data Preprocessing/31 - Gathering Business Knowledge English.vtt 4KB
  108. 15 - Addon 1 Data Preprocessing/40 - Missing Value imputation in R English.vtt 3KB
  109. 1 - Introduction/4 - This is a milestone English.vtt 3KB
  110. 8 - Tensorflow and Keras/20 - Keras and Tensorflow English.vtt 3KB
  111. 15 - Addon 1 Data Preprocessing/32 - Data Exploration English.vtt 3KB
  112. 2 - Setting Up R Studio and R crash course/9 - Inputting data part 2 Manual data entry English.vtt 3KB
  113. 15 - Addon 1 Data Preprocessing/35 - Univariate Analysis and EDD English.vtt 3KB
  114. 1 - Introduction/1 - Welcome to the course English.vtt 3KB
  115. 8 - Tensorflow and Keras/21 - Installing Keras and Tensorflow English.vtt 3KB
  116. 15 - Addon 1 Data Preprocessing/34 - Importing the dataset into R English.vtt 2KB
  117. 18 - Congratulations & about your certificate/62 - Bonus lecture.html 2KB
  118. 16 - Linear Regression Model/49 - The problem statement English.vtt 2KB
  119. 18 - Congratulations & about your certificate/61 - The final milestone English.vtt 2KB
  120. 9 - R Dataset for classification problem/23 - More about testtrain split.html 559B
  121. 1 - Introduction/3 - Course Resources.html 345B
  122. 0. Websites you may like/[FreeCourseSite.com].url 127B
  123. 0. Websites you may like/[CourseClub.Me].url 122B
  124. 0. Websites you may like/[GigaCourse.Com].url 49B
  125. 5 - Important concepts Common Interview questions/1 - Quiz.html 0B
  126. 6 - Standard Model Parameters/2 - Quiz.html 0B
  127. 7 - Practice Test/1 - Test your conceptual understanding.html 0B