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[] Udemy - Deep Learning with Keras and Tensorflow in Python and R

  • 收录时间:2020-10-13 14:56:12
  • 文件大小:4GB
  • 下载次数:16
  • 最近下载:2021-01-22 16:08:16
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文件列表

  1. 15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 216MB
  2. 12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.mp4 156MB
  3. 14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 152MB
  4. 13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.mp4 131MB
  5. 11. R - Building and training the Model/1. Building,Compiling and Training.mp4 131MB
  6. 5. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122MB
  7. 9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.mp4 112MB
  8. 18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4 100MB
  9. 11. R - Building and training the Model/2. Evaluating and Predicting.mp4 99MB
  10. 18. Add on Data Preprocessing/8. EDD in R.mp4 97MB
  11. 3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.mp4 97MB
  12. 12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.mp4 92MB
  13. 4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 87MB
  14. 3. Setting up R Studio and R Crash Course/3. Packages in R.mp4 83MB
  15. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 82MB
  16. 13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.mp4 80MB
  17. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 79MB
  18. 19. Test Train Split/4. Test train split in R.mp4 76MB
  19. 18. Add on Data Preprocessing/10. Outlier Treatment in Python.mp4 70MB
  20. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 70MB
  21. 18. Add on Data Preprocessing/3. The Data and the Data Dictionary.mp4 69MB
  22. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 65MB
  23. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 64MB
  24. 6. Important concepts Common Interview questions/1. Some Important Concepts.mp4 62MB
  25. 18. Add on Data Preprocessing/7. EDD in Python.mp4 62MB
  26. 16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 61MB
  27. 17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 61MB
  28. 5. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60MB
  29. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 60MB
  30. 3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.mp4 60MB
  31. 9. Dataset for classification problem/1. Python - Dataset for classification problem.mp4 56MB
  32. 18. Add on Data Preprocessing/18. Variable transformation in R.mp4 55MB
  33. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 47MB
  34. 7. Standard Model Parameters/1. Hyperparameters.mp4 45MB
  35. 19. Test Train Split/3. Test train split in Python.mp4 45MB
  36. 4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 45MB
  37. 9. Dataset for classification problem/2. Python - Normalization and Test-Train split.mp4 44MB
  38. 18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.mp4 44MB
  39. 18. Add on Data Preprocessing/22. Dummy variable creation in R.mp4 44MB
  40. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 44MB
  41. 3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.mp4 42MB
  42. 19. Test Train Split/1. Test-train split.mp4 42MB
  43. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 41MB
  44. 3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.mp4 41MB
  45. 5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40MB
  46. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 40MB
  47. 3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.mp4 39MB
  48. 18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 37MB
  49. 3. Setting up R Studio and R Crash Course/1. Installing R and R studio.mp4 36MB
  50. 4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 35MB
  51. 18. Add on Data Preprocessing/11. Outlier Treatment in R.mp4 31MB
  52. 1. Introduction/1. Introduction.mp4 29MB
  53. 18. Add on Data Preprocessing/4. Importing Data in Python.mp4 28MB
  54. 18. Add on Data Preprocessing/21. Dummy variable creation in Python.mp4 27MB
  55. 18. Add on Data Preprocessing/14. Missing Value imputation in R.mp4 26MB
  56. 3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.mp4 26MB
  57. 19. Test Train Split/2. Bias Variance trade-off.mp4 25MB
  58. 18. Add on Data Preprocessing/12. Missing Value imputation.mp4 25MB
  59. 18. Add on Data Preprocessing/9. Outlier Treatment.mp4 24MB
  60. 18. Add on Data Preprocessing/6. Univariate Analysis and EDD.mp4 24MB
  61. 18. Add on Data Preprocessing/13. Missing Value Imputation in Python.mp4 23MB
  62. 8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.mp4 23MB
  63. 18. Add on Data Preprocessing/1. Gathering Business Knowledge.mp4 22MB
  64. 18. Add on Data Preprocessing/2. Data Exploration.mp4 21MB
  65. 18. Add on Data Preprocessing/19. Non Usable Variables.mp4 20MB
  66. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.mp4 20MB
  67. 18. Add on Data Preprocessing/15. Seasonality in Data.mp4 17MB
  68. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16MB
  69. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 15MB
  70. 18. Add on Data Preprocessing/5. Importing the dataset into R.mp4 13MB
  71. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 13MB
  72. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 11MB
  73. 1. Introduction/2.1 keras.zip 6MB
  74. 2. Setting up Python and Jupyter Notebook/8.1 Product.txt 139KB
  75. 2. Setting up Python and Jupyter Notebook/8.2 Customer.csv 64KB
  76. 5. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23KB
  77. 12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.srt 22KB
  78. 15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 20KB
  79. 14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 19KB
  80. 18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.srt 18KB
  81. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17KB
  82. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16KB
  83. 11. R - Building and training the Model/1. Building,Compiling and Training.srt 15KB
  84. 4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 15KB
  85. 3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.srt 13KB
  86. 6. Important concepts Common Interview questions/1. Some Important Concepts.srt 13KB
  87. 13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.srt 13KB
  88. 18. Add on Data Preprocessing/10. Outlier Treatment in Python.srt 13KB
  89. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12KB
  90. 9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.srt 12KB
  91. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12KB
  92. 5. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12KB
  93. 18. Add on Data Preprocessing/8. EDD in R.srt 12KB
  94. 12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.srt 12KB
  95. 3. Setting up R Studio and R Crash Course/3. Packages in R.srt 11KB
  96. 3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.srt 11KB
  97. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10KB
  98. 18. Add on Data Preprocessing/7. EDD in Python.srt 10KB
  99. 19. Test Train Split/1. Test-train split.srt 10KB
  100. 4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 10KB
  101. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 10KB
  102. 5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 10KB
  103. 16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9KB
  104. 17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9KB
  105. 11. R - Building and training the Model/2. Evaluating and Predicting.srt 9KB
  106. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9KB
  107. 18. Add on Data Preprocessing/18. Variable transformation in R.srt 9KB
  108. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9KB
  109. 7. Standard Model Parameters/1. Hyperparameters.srt 9KB
  110. 19. Test Train Split/4. Test train split in R.srt 8KB
  111. 13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.srt 8KB
  112. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8KB
  113. 19. Test Train Split/3. Test train split in Python.srt 8KB
  114. 4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8KB
  115. 18. Add on Data Preprocessing/3. The Data and the Data Dictionary.srt 8KB
  116. 18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.srt 8KB
  117. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 8KB
  118. 9. Dataset for classification problem/1. Python - Dataset for classification problem.srt 7KB
  119. 3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.srt 6KB
  120. 19. Test Train Split/2. Bias Variance trade-off.srt 6KB
  121. 3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.srt 6KB
  122. 9. Dataset for classification problem/2. Python - Normalization and Test-Train split.srt 6KB
  123. 3. Setting up R Studio and R Crash Course/1. Installing R and R studio.srt 6KB
  124. 18. Add on Data Preprocessing/4. Importing Data in Python.srt 6KB
  125. 18. Add on Data Preprocessing/21. Dummy variable creation in Python.srt 6KB
  126. 18. Add on Data Preprocessing/19. Non Usable Variables.srt 5KB
  127. 18. Add on Data Preprocessing/22. Dummy variable creation in R.srt 5KB
  128. 18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 5KB
  129. 1. Introduction/1. Introduction.srt 5KB
  130. 18. Add on Data Preprocessing/9. Outlier Treatment.srt 4KB
  131. 18. Add on Data Preprocessing/11. Outlier Treatment in R.srt 4KB
  132. 18. Add on Data Preprocessing/12. Missing Value imputation.srt 4KB
  133. 18. Add on Data Preprocessing/13. Missing Value Imputation in Python.srt 4KB
  134. 3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.srt 4KB
  135. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4KB
  136. 18. Add on Data Preprocessing/1. Gathering Business Knowledge.srt 4KB
  137. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.srt 4KB
  138. 18. Add on Data Preprocessing/15. Seasonality in Data.srt 4KB
  139. 18. Add on Data Preprocessing/2. Data Exploration.srt 4KB
  140. 8. Tensorflow and Keras/1. Keras and Tensorflow.srt 4KB
  141. 18. Add on Data Preprocessing/14. Missing Value imputation in R.srt 3KB
  142. 18. Add on Data Preprocessing/6. Univariate Analysis and EDD.srt 3KB
  143. 8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.srt 3KB
  144. 3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.srt 3KB
  145. 18. Add on Data Preprocessing/5. Importing the dataset into R.srt 3KB
  146. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 3KB
  147. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt 2KB
  148. Readme.txt 962B
  149. 5. Neural Networks - Stacking cells to create network/4. Quiz.html 166B
  150. 1. Introduction/2. Course Resources.html 117B
  151. [GigaCourse.com].url 49B