589689.xyz

[] Udemy - Neural Networks in Python Deep Learning for Beginners

  • 收录时间:2021-12-16 13:50:50
  • 文件大小:3GB
  • 下载次数:1
  • 最近下载:2021-12-16 13:50:50
  • 磁力链接:
立即下载 复制链接

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 22MB
  51. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4 22MB
  52. 1. Introduction/1. Welcome to the course.mp4 21MB
  53. 1. Introduction/4. This is a milestone!.mp4 21MB
  54. 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4 21MB
  55. 15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp4 20MB
  56. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4 20MB
  57. 15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp4 17MB
  58. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16MB
  59. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 15MB
  60. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 13MB
  61. 18. Bonus Section/1. The final milestone!.mp4 12MB
  62. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 11MB
  63. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4 9MB
  64. 15. Add-on 1 Data Preprocessing/4.1 Files_linear_py.zip 9MB
  65. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23KB
  66. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt 22KB
  67. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 19KB
  68. 15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.srt 18KB
  69. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17KB
  70. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16KB
  71. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt 16KB
  72. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 15KB
  73. 5. Important concepts Common Interview questions/1. Some Important Concepts.srt 13KB
  74. 15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.srt 13KB
  75. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt 12KB
  76. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12KB
  77. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12KB
  78. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12KB
  79. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt 12KB
  80. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt 11KB
  81. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis.srt 11KB
  82. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10KB
  83. 15. Add-on 1 Data Preprocessing/7. EDD in Python.srt 10KB
  84. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt 10KB
  85. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10KB
  86. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 10KB
  87. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 10KB
  88. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 10KB
  89. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9KB
  90. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9KB
  91. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9KB
  92. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt 9KB
  93. 6. Standard Model Parameters/1. Hyperparameters.srt 9KB
  94. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8KB
  95. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt 8KB
  96. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8KB
  97. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8KB
  98. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8KB
  99. 15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.srt 8KB
  100. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 8KB
  101. 9. Python - Dataset for classification problem/1. Dataset for classification.srt 7KB
  102. 15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.srt 7KB
  103. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt 6KB
  104. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt 6KB
  105. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt 6KB
  106. 15. Add-on 1 Data Preprocessing/5. Importing Data in Python.srt 6KB
  107. 15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.srt 6KB
  108. 15. Add-on 1 Data Preprocessing/15. Non-usable variables.srt 5KB
  109. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt 5KB
  110. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.srt 5KB
  111. 1. Introduction/2. Introduction to Neural Networks and Course flow.srt 5KB
  112. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment.srt 4KB
  113. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.srt 4KB
  114. 15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.srt 4KB
  115. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4KB
  116. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt 4KB
  117. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt 4KB
  118. 1. Introduction/4. This is a milestone!.srt 4KB
  119. 15. Add-on 1 Data Preprocessing/12. Seasonality in Data.srt 4KB
  120. 15. Add-on 1 Data Preprocessing/2. Data Exploration.srt 4KB
  121. 8. Tensorflow and Keras/1. Keras and Tensorflow.srt 4KB
  122. 15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.srt 3KB
  123. 1. Introduction/1. Welcome to the course.srt 3KB
  124. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 3KB
  125. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt 2KB
  126. 18. Bonus Section/1. The final milestone!.srt 2KB
  127. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt 2KB
  128. 18. Bonus Section/2. Congratulations & About your certificate.html 2KB
  129. 9. Python - Dataset for classification problem/3. More about test-train split.html 559B
  130. 1. Introduction/3. Course Resources.html 319B
  131. 17. Practice Assignment/1. Neural Networks Classification Assignment.html 213B
  132. 5. Important concepts Common Interview questions/2. Quiz.html 209B
  133. 6. Standard Model Parameters/2. Quiz.html 209B
  134. 7. Practice Test/1. Test your conceptual understanding.html 209B
  135. 15. Add-on 1 Data Preprocessing/4. Add-on Resources.html 131B
  136. 0. Websites you may like/[CourseClub.ME].url 122B
  137. [CourseClub.Me].url 122B
  138. 0. Websites you may like/[GigaCourse.Com].url 49B
  139. [GigaCourse.Com].url 49B