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

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

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文件大小： 3.0G
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收录时间：2020-05-09
最近下载：2025-09-30

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文件列表

  • 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4 163.5 MB
  • 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 158.9 MB
  • 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
  • 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4 105.3 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 96.6 MB
  • 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4 96.6 MB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 90.8 MB
  • 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 85.7 MB
  • 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 83.0 MB
  • 15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp4 75.1 MB
  • 15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp4 73.6 MB
  • 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 73.3 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4 73.1 MB
  • 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 72.7 MB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 68.3 MB
  • 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 67.6 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4 66.5 MB
  • 5. Important concepts Common Interview questions/1. Some Important Concepts.mp4 65.2 MB
  • 15. Add-on 1 Data Preprocessing/6. EDD in Python.mp4 64.8 MB
  • 14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 63.6 MB
  • 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 63.3 MB
  • 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 63.3 MB
  • 9. Python - Dataset for classification problem/1. Dataset for classification.mp4 58.9 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4 58.7 MB
  • 15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.mp4 58.0 MB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 49.2 MB
  • 6. Standard Model Parameters/1. Hyperparameters.mp4 47.5 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4 47.0 MB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 46.9 MB
  • 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp4 46.3 MB
  • 15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp4 46.2 MB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 46.0 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 45.7 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 45.5 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4 43.9 MB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 42.9 MB
  • 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 42.4 MB
  • 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 42.3 MB
  • 15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp4 38.6 MB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 36.3 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4 36.0 MB
  • 1. Introduction/2. Introduction to Neural Networks and Course flow.mp4 30.5 MB
  • 15. Add-on 1 Data Preprocessing/4. Importing Data in Python.mp4 29.2 MB
  • 15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp4 27.8 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4 26.3 MB
  • 15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp4 26.2 MB
  • 15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp4 25.7 MB
  • 15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp4 25.4 MB
  • 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp4 24.6 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4 23.6 MB
  • 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4 23.4 MB
  • 1. Introduction/1. Welcome to the course.mp4 22.5 MB
  • 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4 21.5 MB
  • 15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp4 21.2 MB
  • 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4 21.0 MB
  • 15. Add-on 1 Data Preprocessing/11. Seasonality in Data.mp4 17.9 MB
  • 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 17.1 MB
  • 8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 15.6 MB
  • 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 13.4 MB
  • 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 11.3 MB
  • 1. Introduction/3.1 Files_ANN_Py.zip 11.0 MB
  • 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4 9.8 MB
  • 4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23.3 kB
  • 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt 22.2 kB
  • 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 19.2 kB
  • 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.srt 18.7 kB
  • 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17.4 kB
  • 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16.8 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt 16.2 kB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 14.9 kB
  • 5. Important concepts Common Interview questions/1. Some Important Concepts.srt 13.4 kB
  • 15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.srt 13.3 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt 12.6 kB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12.6 kB
  • 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12.2 kB
  • 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12.2 kB
  • 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt 11.8 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt 11.6 kB
  • 15. Add-on 1 Data Preprocessing/17. Correlation Analysis.srt 11.3 kB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10.7 kB
  • 15. Add-on 1 Data Preprocessing/6. EDD in Python.srt 10.6 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt 10.3 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10.1 kB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 9.9 kB
  • 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 9.8 kB
  • 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 9.7 kB
  • 14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.7 kB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9.4 kB
  • 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9.2 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt 9.2 kB
  • 6. Standard Model Parameters/1. Hyperparameters.srt 9.2 kB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8.3 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt 8.2 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8.2 kB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8.0 kB
  • 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt 8.0 kB
  • 15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.srt 7.7 kB
  • 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 7.7 kB
  • 9. Python - Dataset for classification problem/1. Dataset for classification.srt 7.3 kB
  • 15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.srt 6.7 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt 6.5 kB
  • 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt 5.9 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt 5.9 kB
  • 15. Add-on 1 Data Preprocessing/4. Importing Data in Python.srt 5.7 kB
  • 15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.srt 5.6 kB
  • 15. Add-on 1 Data Preprocessing/14. Non-usable variables.srt 5.5 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt 5.4 kB
  • 15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt 5.0 kB
  • 1. Introduction/2. Introduction to Neural Networks and Course flow.srt 4.7 kB
  • 15. Add-on 1 Data Preprocessing/7. Outlier Treatment.srt 4.6 kB
  • 15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.srt 4.2 kB
  • 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.srt 4.2 kB
  • 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4.1 kB
  • 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt 4.0 kB
  • 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt 3.9 kB
  • 15. Add-on 1 Data Preprocessing/11. Seasonality in Data.srt 3.9 kB
  • 15. Add-on 1 Data Preprocessing/2. Data Exploration.srt 3.7 kB
  • 8. Tensorflow and Keras/1. Keras and Tensorflow.srt 3.6 kB
  • 15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.srt 3.5 kB
  • 1. Introduction/1. Welcome to the course.srt 3.2 kB
  • 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 2.6 kB
  • 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt 1.9 kB
  • 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt 1.7 kB
  • Readme.txt 962 Bytes
  • 17. Practice Assignment/1. Neural Networks Classification Assignment.html 173 Bytes
  • 5. Important concepts Common Interview questions/2. Quiz.html 169 Bytes
  • 7. Practice Test/1. Test your conceptual understanding.html 169 Bytes
  • 1. Introduction/3. Course resources.html 117 Bytes
  • [GigaCourse.com].url 49 Bytes

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