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[] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python

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  • 文件大小:3GB
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文件列表

  1. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4 156MB
  2. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 152MB
  3. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122MB
  4. 15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.mp4 100MB
  5. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 92MB
  6. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4 92MB
  7. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 87MB
  8. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 82MB
  9. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 79MB
  10. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis.mp4 72MB
  11. 15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.mp4 70MB
  12. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 70MB
  13. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4 70MB
  14. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 69MB
  15. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 65MB
  16. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 64MB
  17. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4 63MB
  18. 5. Important concepts Common Interview questions/1. Some Important Concepts.mp4 62MB
  19. 15. Add-on 1 Data Preprocessing/7. EDD in Python.mp4 62MB
  20. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 61MB
  21. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60MB
  22. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 60MB
  23. 9. Python - Dataset for classification problem/1. Dataset for classification.mp4 56MB
  24. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4 56MB
  25. 15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.mp4 55MB
  26. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 47MB
  27. 6. Standard Model Parameters/1. Hyperparameters.mp4 45MB
  28. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4 45MB
  29. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 45MB
  30. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp4 44MB
  31. 15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.mp4 44MB
  32. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 44MB
  33. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 44MB
  34. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 43MB
  35. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4 42MB
  36. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 41MB
  37. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40MB
  38. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 40MB
  39. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.mp4 37MB
  40. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 35MB
  41. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4 34MB
  42. 1. Introduction/2. Introduction to Neural Networks and Course flow.mp4 29MB
  43. 15. Add-on 1 Data Preprocessing/5. Importing Data in Python.mp4 28MB
  44. 15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.mp4 27MB
  45. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4 25MB
  46. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.mp4 25MB
  47. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment.mp4 24MB
  48. 15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.mp4 24MB
  49. 15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.mp4 23MB
  50. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4 23MB
  51. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4 22MB
  52. 1. Introduction/1. Welcome to the course.mp4 21MB
  53. 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4 21MB
  54. 15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp4 20MB
  55. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4 20MB
  56. 15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp4 17MB
  57. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16MB
  58. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 15MB
  59. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 13MB
  60. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 11MB
  61. 1. Introduction/3.1 Files_ANN_Py.zip 11MB
  62. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4 9MB
  63. 15. Add-on 1 Data Preprocessing/4.1 Files_linear_py.zip 9MB
  64. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23KB
  65. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt 22KB
  66. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 19KB
  67. 15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.srt 18KB
  68. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17KB
  69. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16KB
  70. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt 16KB
  71. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 15KB
  72. 5. Important concepts Common Interview questions/1. Some Important Concepts.srt 13KB
  73. 15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.srt 13KB
  74. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt 12KB
  75. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12KB
  76. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12KB
  77. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12KB
  78. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt 12KB
  79. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt 11KB
  80. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis.srt 11KB
  81. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10KB
  82. 15. Add-on 1 Data Preprocessing/7. EDD in Python.srt 10KB
  83. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt 10KB
  84. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10KB
  85. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 10KB
  86. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 10KB
  87. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 10KB
  88. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9KB
  89. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9KB
  90. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9KB
  91. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt 9KB
  92. 6. Standard Model Parameters/1. Hyperparameters.srt 9KB
  93. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8KB
  94. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt 8KB
  95. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8KB
  96. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8KB
  97. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8KB
  98. 15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.srt 8KB
  99. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 8KB
  100. 9. Python - Dataset for classification problem/1. Dataset for classification.srt 7KB
  101. 15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.srt 7KB
  102. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt 6KB
  103. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt 6KB
  104. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt 6KB
  105. 15. Add-on 1 Data Preprocessing/5. Importing Data in Python.srt 6KB
  106. 15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.srt 6KB
  107. 15. Add-on 1 Data Preprocessing/15. Non-usable variables.srt 5KB
  108. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt 5KB
  109. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.srt 5KB
  110. 1. Introduction/2. Introduction to Neural Networks and Course flow.srt 5KB
  111. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment.srt 4KB
  112. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.srt 4KB
  113. 15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.srt 4KB
  114. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4KB
  115. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt 4KB
  116. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt 4KB
  117. 15. Add-on 1 Data Preprocessing/12. Seasonality in Data.srt 4KB
  118. 15. Add-on 1 Data Preprocessing/2. Data Exploration.srt 4KB
  119. 8. Tensorflow and Keras/1. Keras and Tensorflow.srt 4KB
  120. 15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.srt 3KB
  121. 1. Introduction/1. Welcome to the course.srt 3KB
  122. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 3KB
  123. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt 2KB
  124. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt 2KB
  125. 18. Bonus Section/1. Congratulations & About your certificate.html 2KB
  126. 17. Practice Assignment/1. Neural Networks Classification Assignment.html 173B
  127. 5. Important concepts Common Interview questions/2. Quiz.html 169B
  128. 7. Practice Test/1. Test your conceptual understanding.html 169B
  129. 15. Add-on 1 Data Preprocessing/4. Add-on Resources.html 131B
  130. 1. Introduction/3. Course resources.html 117B
  131. 0. Websites you may like/[DesireCourse.Net].url 51B
  132. 1. Introduction/[DesireCourse.Net].url 51B
  133. 10. Python - Building and training the Model/[DesireCourse.Net].url 51B
  134. 14. Hyperparameter Tuning/[DesireCourse.Net].url 51B
  135. 17. Practice Assignment/[DesireCourse.Net].url 51B
  136. 5. Important concepts Common Interview questions/[DesireCourse.Net].url 51B
  137. [DesireCourse.Net].url 51B
  138. 0. Websites you may like/[CourseClub.Me].url 48B
  139. 1. Introduction/[CourseClub.Me].url 48B
  140. 10. Python - Building and training the Model/[CourseClub.Me].url 48B
  141. 14. Hyperparameter Tuning/[CourseClub.Me].url 48B
  142. 17. Practice Assignment/[CourseClub.Me].url 48B
  143. 5. Important concepts Common Interview questions/[CourseClub.Me].url 48B
  144. [CourseClub.Me].url 48B