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[GigaCourse com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python
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Torrent Files List
1. Introduction (Size: 3.00 GB) (Files: 129)
1. Introduction
1. Welcome to the course.mp4
1. Welcome to the course.srt
2. Introduction to Neural Networks and Course flow.mp4
2. Introduction to Neural Networks and Course flow.srt
3. Course resources.html
3.1 Files_ANN_Py.zip
10. Python - Building and training the Model
1. Different ways to create ANN using Keras.mp4
1. Different ways to create ANN using Keras.srt
2. Building the Neural Network using Keras.mp4
2. Building the Neural Network using Keras.srt
3. Compiling and Training the Neural Network model.mp4
3. Compiling and Training the Neural Network model.srt
4. Evaluating performance and Predicting using Keras.mp4
4. Evaluating performance and Predicting using Keras.srt
11. Python - Solving a Regression problem using ANN
1. Building Neural Network for Regression Problem.mp4
1. Building Neural Network for Regression Problem.srt
12. Complex ANN Architectures using Functional API
1. Using Functional API for complex architectures.mp4
1. Using Functional API for complex architectures.srt
13. Saving and Restoring Models
1. Saving - Restoring Models and Using Callbacks.mp4
1. Saving - Restoring Models and Using Callbacks.srt
14. Hyperparameter Tuning
1. Hyperparameter Tuning.mp4
1. Hyperparameter Tuning.srt
15. Add-on 1 Data Preprocessing
1. Gathering Business Knowledge.mp4
1. Gathering Business Knowledge.srt
10. Missing Value Imputation in Python.mp4
10. Missing Value Imputation in Python.srt
11. Seasonality in Data.mp4
11. Seasonality in Data.srt
12. Bi-variate analysis and Variable transformation.mp4
12. Bi-variate analysis and Variable transformation.srt
13. Variable transformation and deletion in Python.mp4
13. Variable transformation and deletion in Python.srt
14. Non-usable variables.mp4
14. Non-usable variables.srt
15. Dummy variable creation Handling qualitative data.mp4
15. Dummy variable creation Handling qualitative data.srt
16. Dummy variable creation in Python.mp4
16. Dummy variable creation in Python.srt
17. Correlation Analysis.mp4
17. Correlation Analysis.srt
18. Correlation Analysis in Python.mp4
18. Correlation Analysis in Python.srt
2. Data Exploration.mp4
2. Data Exploration.srt
3. The Dataset and the Data Dictionary.mp4
3. The Dataset and the Data Dictionary.srt
4. Importing Data in Python.mp4
4. Importing Data in Python.srt
5. Univariate analysis and EDD.mp4
5. Univariate analysis and EDD.srt
6. EDD in Python.mp4
6. EDD in Python.srt
7. Outlier Treatment.mp4
7. Outlier Treatment.srt
8. Outlier Treatment in Python.mp4
8. Outlier Treatment in Python.srt
9. Missing Value Imputation.mp4
9. Missing Value Imputation.srt
16. Add-on 2 Classic ML models - Linear Regression
1. The Problem Statement.mp4
1. The Problem Statement.srt
10. Test-train split.mp4
10. Test-train split.srt
11. Bias Variance trade-off.mp4
11. Bias Variance trade-off.srt
12. Test train split in Python.mp4
12. Test train split in Python.srt
2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
2. Basic Equations and Ordinary Least Squares (OLS) method.srt
3. Assessing accuracy of predicted coefficients.mp4
3. Assessing accuracy of predicted coefficients.srt
4. Assessing Model Accuracy RSE and R squared.mp4
4. Assessing Model Accuracy RSE and R squared.srt
5. Simple Linear Regression in Python.mp4
5. Simple Linear Regression in Python.srt
6. Multiple Linear Regression.mp4
6. Multiple Linear Regression.srt
7. The F - statistic.mp4
7. The F - statistic.srt
8. Interpreting results of Categorical variables.mp4
8. Interpreting results of Categorical variables.srt
9. Multiple Linear Regression in Python.mp4
9. Multiple Linear Regression in Python.srt
17. Practice Assignment
1. Neural Networks Classification Assignment.html
2. Setting up Python and Jupyter Notebook
1. Installing Python and Anaconda.mp4
1. Installing Python and Anaconda.srt
2. Opening Jupyter Notebook.mp4
2. Opening Jupyter Notebook.srt
3. Introduction to Jupyter.mp4
3. Introduction to Jupyter.srt
4. Arithmetic operators in Python Python Basics.mp4
4. Arithmetic operators in Python Python Basics.srt
5. Strings in Python Python Basics.mp4
5. Strings in Python Python Basics.srt
6. Lists, Tuples and Directories Python Basics.mp4
6. Lists, Tuples and Directories Python Basics.srt
7. Working with Numpy Library of Python.mp4
7. Working with Numpy Library of Python.srt
8. Working with Pandas Library of Python.mp4
8. Working with Pandas Library of Python.srt
9. Working with Seaborn Library of Python.mp4
9. Working with Seaborn Library of Python.srt
3. Single Cells - Perceptron and Sigmoid Neuron
1. Perceptron.mp4
1. Perceptron.srt
2. Activation Functions.mp4
2. Activation Functions.srt
3. Python - Creating Perceptron model.mp4
3. Python - Creating Perceptron model.srt
4. Neural Networks - Stacking cells to create network
1. Basic Terminologies.mp4
1. Basic Terminologies.srt
2. Gradient Descent.mp4
2. Gradient Descent.srt
3. Back Propagation.mp4
3. Back Propagation.srt
5. Important concepts Common Interview questions
1. Some Important Concepts.mp4
1. Some Important Concepts.srt
2. Quiz.html
6. Standard Model Parameters
1. Hyperparameters.mp4
1. Hyperparameters.srt
7. Practice Test
1. Test your conceptual understanding.html
8. Tensorflow and Keras
1. Keras and Tensorflow.mp4
1. Keras and Tensorflow.srt
2. Installing Tensorflow and Keras.mp4
2. Installing Tensorflow and Keras.srt
9. Python - Dataset for classification problem
1. Dataset for classification.mp4
1. Dataset for classification.srt
2. Normalization and Test-Train split.mp4
2. Normalization and Test-Train split.srt
Readme.txt
[GigaCourse.com].url
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