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

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2025-02-27 01:32:203.00 GB129023489E261F71D8D732DF009E55D6FF2895BF056
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    • Files
    1. 1. Introduction/1. Welcome to the course.mp421.42 MB
    2. 1. Introduction/1. Welcome to the course.srt3.15 KB
    3. 1. Introduction/2. Introduction to Neural Networks and Course flow.mp429.07 MB
    4. 1. Introduction/2. Introduction to Neural Networks and Course flow.srt4.60 KB
    5. 1. Introduction/3. Course resources.html117 B
    6. 1. Introduction/3.1 Files_ANN_Py.zip10.51 MB
    7. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp410.80 MB
    8. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt1.87 KB
    9. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp479.14 MB
    10. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt11.96 KB
    11. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp481.71 MB
    12. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt9.59 KB
    13. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp469.93 MB
    14. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt9.02 KB
    15. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4155.88 MB
    16. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt21.71 KB
    17. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp492.12 MB
    18. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt11.50 KB
    19. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4151.57 MB
    20. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt18.79 KB
    21. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.63 MB
    22. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt9.43 KB
    23. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp422.29 MB
    24. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt3.90 KB
    25. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp423.42 MB
    26. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.srt4.06 KB
    27. 15. Add-on 1 Data Preprocessing/11. Seasonality in Data.mp417.03 MB
    28. 15. Add-on 1 Data Preprocessing/11. Seasonality in Data.srt3.78 KB
    29. 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4100.42 MB
    30. 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.srt18.29 KB
    31. 15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp444.08 MB
    32. 15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.srt7.54 KB
    33. 15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp420.24 MB
    34. 15. Add-on 1 Data Preprocessing/14. Non-usable variables.srt5.39 KB
    35. 15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp436.83 MB
    36. 15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt4.86 KB
    37. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp426.54 MB
    38. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.srt5.51 KB
    39. 15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp471.60 MB
    40. 15. Add-on 1 Data Preprocessing/17. Correlation Analysis.srt11.04 KB
    41. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.mp455.31 MB
    42. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.srt6.55 KB
    43. 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp420.51 MB
    44. 15. Add-on 1 Data Preprocessing/2. Data Exploration.srt3.60 KB
    45. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.38 MB
    46. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt7.82 KB
    47. 15. Add-on 1 Data Preprocessing/4. Importing Data in Python.mp427.83 MB
    48. 15. Add-on 1 Data Preprocessing/4. Importing Data in Python.srt5.58 KB
    49. 15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp424.20 MB
    50. 15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.srt3.44 KB
    51. 15. Add-on 1 Data Preprocessing/6. EDD in Python.mp461.78 MB
    52. 15. Add-on 1 Data Preprocessing/6. EDD in Python.srt10.36 KB
    53. 15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp424.48 MB
    54. 15. Add-on 1 Data Preprocessing/7. Outlier Treatment.srt4.46 KB
    55. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp470.23 MB
    56. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.srt13 KB
    57. 15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp425.01 MB
    58. 15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.srt4.08 KB
    59. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp49.38 MB
    60. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt1.61 KB
    61. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp441.87 MB
    62. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt10.05 KB
    63. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp425.11 MB
    64. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt6.37 KB
    65. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp444.87 MB
    66. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt8.05 KB
    67. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.35 MB
    68. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt9.89 KB
    69. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.14 MB
    70. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt15.85 KB
    71. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.63 MB
    72. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt8.02 KB
    73. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp463.43 MB
    74. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt11.36 KB
    75. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp434.32 MB
    76. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt5.73 KB
    77. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp456.01 MB
    78. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt9.02 KB
    79. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp422.51 MB
    80. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt5.29 KB
    81. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp469.74 MB
    82. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt12.34 KB
    83. 17. Practice Assignment/1. Neural Networks Classification Assignment.html173 B
    84. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.26 MB
    85. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt2.58 KB
    86. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp465.18 MB
    87. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt9.14 KB
    88. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp440.91 MB
    89. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt12.31 KB
    90. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp412.75 MB
    91. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt3.99 KB
    92. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp464.43 MB
    93. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt16.43 KB
    94. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp460.33 MB
    95. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt17.01 KB
    96. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp443.87 MB
    97. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt10.47 KB
    98. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp446.89 MB
    99. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt8.15 KB
    100. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp440.36 MB
    101. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt7.53 KB
    102. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76 MB
    103. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt9.69 KB
    104. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.62 MB
    105. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt7.85 KB
    106. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.60 MB
    107. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt14.53 KB
    108. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42 MB
    109. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt9.52 KB
    110. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.33 MB
    111. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt11.93 KB
    112. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.20 MB
    113. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt22.78 KB
    114. 5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.17 MB
    115. 5. Important concepts Common Interview questions/1. Some Important Concepts.srt13.10 KB
    116. 5. Important concepts Common Interview questions/2. Quiz.html169 B
    117. 6. Standard Model Parameters/1. Hyperparameters.mp445.35 MB
    118. 6. Standard Model Parameters/1. Hyperparameters.srt8.95 KB
    119. 7. Practice Test/1. Test your conceptual understanding.html169 B
    120. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp414.92 MB
    121. 8. Tensorflow and Keras/1. Keras and Tensorflow.srt3.56 KB
    122. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp420.07 MB
    123. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt3.79 KB
    124. 9. Python - Dataset for classification problem/1. Dataset for classification.mp456.13 MB
    125. 9. Python - Dataset for classification problem/1. Dataset for classification.srt7.16 KB
    126. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp444.20 MB
    127. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt5.73 KB
    128. Readme.txt962 B
    129. [GigaCourse.com].url49 B
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