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TORRENT DETAILS

[DesireCourse.Net] Udemy - Neural Networks (ANN) Using Keras And TensorFlow In Python

TORRENT SUMMARY

	Status:	This torrent has NOT been verified.
Category:	Other
Infohash	14d6d6ef2db6317ba959d356df9a36b2e4a3ef7a
Size:	3 GB in 89 files
Release Date(s):	Torrent Release: 23-06-2020 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

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FILE LIST

Filename		Size
	1. Introduction/1. Welcome to the course.mp4	21.4 MB
	1. Introduction/1. Welcome to the course.srt	3.1 KB
	1. Introduction/2. Introduction to Neural Networks and Course flow.mp4	29.1 MB
	1. Introduction/2. Introduction to Neural Networks and Course flow.srt	4.6 KB
	1. Introduction/3. Course resources.html	117 B
	1. Introduction/3.1 Files_ANN_Py.zip	10.5 MB
	10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4	10.8 MB
	10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt	1.9 KB
	10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4	79.1 MB
	10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt	12 KB
	10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4	81.7 MB
	10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt	9.6 KB
	10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4	69.9 MB
	10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt	9 KB
	11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4	155.9 MB
	11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt	21.7 KB
	12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4	92.1 MB
	12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt	11.5 KB
	13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4	151.6 MB
	13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt	18.8 KB
	14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4	60.6 MB
	14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt	9.4 KB
	15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4	22.3 MB
	15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt	3.9 KB
	15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.mp4	25 MB
	15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.srt	4.1 KB
	15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.mp4	23.4 MB
	15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.srt	4.1 KB
	15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp4	17 MB
	15. Add-on 1 Data Preprocessing/12. Seasonality in Data.srt	3.8 KB
	15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.mp4	100.4 MB
	15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.srt	18.3 KB
	15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.mp4	44.1 MB
	15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.srt	7.5 KB
	15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp4	20.2 MB
	15. Add-on 1 Data Preprocessing/15. Non-usable variables.srt	5.4 KB
	15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.mp4	36.8 MB
	15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.srt	4.9 KB
	15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.mp4	26.5 MB
	15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.srt	5.5 KB
	15. Add-on 1 Data Preprocessing/18. Correlation Analysis.mp4	71.6 MB
	15. Add-on 1 Data Preprocessing/18. Correlation Analysis.srt	11 KB
	15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.mp4	55.3 MB
	15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.srt	6.6 KB
	15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4	20.5 MB
	15. Add-on 1 Data Preprocessing/2. Data Exploration.srt	3.6 KB
	15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4	69.4 MB
	15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt	7.8 KB
	15. Add-on 1 Data Preprocessing/4. Add-on Resources.html	131 B
	15. Add-on 1 Data Preprocessing/4.1 Files_linear_py.zip	8.9 MB
	15. Add-on 1 Data Preprocessing/5. Importing Data in Python.mp4	27.8 MB
	15. Add-on 1 Data Preprocessing/5. Importing Data in Python.srt	5.6 KB
	15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.mp4	24.2 MB
	15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.srt	3.4 KB
	15. Add-on 1 Data Preprocessing/7. EDD in Python.mp4	61.8 MB
	15. Add-on 1 Data Preprocessing/7. EDD in Python.srt	10.4 KB
	15. Add-on 1 Data Preprocessing/8. Outlier Treatment.mp4	24.5 MB
	15. Add-on 1 Data Preprocessing/8. Outlier Treatment.srt	4.5 KB
	15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.mp4	70.2 MB
	15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.srt	13 KB
	16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4	9.4 MB
	16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt	1.6 KB
	16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4	41.9 MB
	16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt	10.1 KB
	16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4	25.1 MB
	16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt	6.4 KB
	16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4	44.9 MB
	16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt	8.1 KB
	16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4	43.4 MB
	16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt	9.9 KB
	16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4	92.1 MB
	16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt	15.9 KB
	16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4	43.6 MB
	16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt	8 KB
	16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4	63.4 MB
	16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt	11.4 KB
	16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4	34.3 MB
	16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt	5.7 KB
	16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4	56 MB
	16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt	9 KB
	16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4	22.5 MB
	16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt	5.3 KB
	16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4	69.7 MB
	16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt	12.3 KB
	17. Practice Assignment/1. Neural Networks Classification Assignment.html	173 B
	18. Bonus Section/1. Congratulations & About your certificate.html	1.6 KB
	2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4	16.3 MB
	2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt	2.6 KB
	2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4	65.2 MB

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