GigaCourse Com Udemy Neural Networks in Python Deep Learning for Beginners

GigaCourse Com Udemy Neural Networks in Python Deep Learning for Beginners

Name	[GigaCourse.Com] Udemy - Neural Networks in Python Deep Learning for Beginners	DOWNLOAD Copy Link
Total Size	3.3 GB
Total Files	139
Last Seen	2024-11-15 00:06
Hash	80AA6AF974E64390EA56891569DC41B0A7F127DD

/0. Websites you may like/
[CourseClub.ME].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/1. Introduction/
1. Welcome to the course.mp4	22.5 MB
1. Welcome to the course.srt	3.2 KB
2. Introduction to Neural Networks and Course flow.mp4	30.5 MB
2. Introduction to Neural Networks and Course flow.srt	4.7 KB
3. Course Resources.html	0.3 KB
4. This is a milestone!.mp4	21.7 MB
4. This is a milestone!.srt	3.9 KB
/10. Python - Building and training the Model/
1. Different ways to create ANN using Keras.mp4	11.3 MB
1. Different ways to create ANN using Keras.srt	1.9 KB
2. Building the Neural Network using Keras.mp4	83.0 MB
2. Building the Neural Network using Keras.srt	12.2 KB
3. Compiling and Training the Neural Network model.mp4	85.7 MB
3. Compiling and Training the Neural Network model.srt	9.8 KB
4. Evaluating performance and Predicting using Keras.mp4	73.3 MB
4. Evaluating performance and Predicting using Keras.srt	9.2 KB
/11. Python - Solving a Regression problem using ANN/
1. Building Neural Network for Regression Problem.mp4	163.4 MB
1. Building Neural Network for Regression Problem.srt	22.2 KB
/12. Complex ANN Architectures using Functional API/
1. Using Functional API for complex architectures.mp4	96.6 MB
1. Using Functional API for complex architectures.srt	11.8 KB
/13. Saving and Restoring Models/
1. Saving - Restoring Models and Using Callbacks.mp4	158.9 MB
1. Saving - Restoring Models and Using Callbacks.srt	19.2 KB
/14. Hyperparameter Tuning/
1. Hyperparameter Tuning.mp4	63.6 MB
1. Hyperparameter Tuning.srt	9.7 KB
/15. Add-on 1 Data Preprocessing/
1. Gathering Business Knowledge.mp4	23.4 MB
1. Gathering Business Knowledge.srt	4.0 KB
10. Missing Value Imputation.mp4	26.2 MB
10. Missing Value Imputation.srt	4.2 KB
11. Missing Value Imputation in Python.mp4	24.6 MB
11. Missing Value Imputation in Python.srt	4.2 KB
12. Seasonality in Data.mp4	17.9 MB
12. Seasonality in Data.srt	3.9 KB
13. Bi-variate analysis and Variable transformation.mp4	105.3 MB
13. Bi-variate analysis and Variable transformation.srt	18.7 KB
14. Variable transformation and deletion in Python.mp4	46.2 MB
14. Variable transformation and deletion in Python.srt	7.7 KB
15. Non-usable variables.mp4	21.2 MB
15. Non-usable variables.srt	5.5 KB
16. Dummy variable creation Handling qualitative data.mp4	38.6 MB
16. Dummy variable creation Handling qualitative data.srt	5.0 KB
17. Dummy variable creation in Python.mp4	27.8 MB
17. Dummy variable creation in Python.srt	5.6 KB
18. Correlation Analysis.mp4	75.1 MB
18. Correlation Analysis.srt	11.3 KB
19. Correlation Analysis in Python.mp4	58.0 MB
19. Correlation Analysis in Python.srt	6.7 KB
2. Data Exploration.mp4	21.5 MB
2. Data Exploration.srt	3.7 KB
3. The Dataset and the Data Dictionary.mp4	72.7 MB
3. The Dataset and the Data Dictionary.srt	8.0 KB
4. Add-on Resources.html	0.1 KB
4.1 Files_linear_py.zip	9.3 MB
5. Importing Data in Python.mp4	29.2 MB
5. Importing Data in Python.srt	5.7 KB
6. Univariate analysis and EDD.mp4	25.4 MB
6. Univariate analysis and EDD.srt	3.5 KB
7. EDD in Python.mp4	64.8 MB
7. EDD in Python.srt	10.6 KB
8. Outlier Treatment.mp4	25.7 MB
8. Outlier Treatment.srt	4.6 KB
9. Outlier Treatment in Python.mp4	73.6 MB
9. Outlier Treatment in Python.srt	13.3 KB
/16. Add-on 2 Classic ML models - Linear Regression/
1. The Problem Statement.mp4	9.8 MB
1. The Problem Statement.srt	1.7 KB
10. Test-train split.mp4	43.9 MB
10. Test-train split.srt	10.3 KB
11. Bias Variance trade-off.mp4	26.3 MB
11. Bias Variance trade-off.srt	6.5 KB
12. Test train split in Python.mp4	47.0 MB
12. Test train split in Python.srt	8.2 KB
2. Basic Equations and Ordinary Least Squares (OLS) method.mp4	45.5 MB
2. Basic Equations and Ordinary Least Squares (OLS) method.srt	10.1 KB
3. Assessing accuracy of predicted coefficients.mp4	96.6 MB
3. Assessing accuracy of predicted coefficients.srt	16.2 KB
4. Assessing Model Accuracy RSE and R squared.mp4	45.7 MB
4. Assessing Model Accuracy RSE and R squared.srt	8.2 KB
5. Simple Linear Regression in Python.mp4	66.5 MB
5. Simple Linear Regression in Python.srt	11.6 KB
6. Multiple Linear Regression.mp4	36.0 MB
6. Multiple Linear Regression.srt	5.9 KB
7. The F - statistic.mp4	58.7 MB
7. The F - statistic.srt	9.2 KB
8. Interpreting results of Categorical variables.mp4	23.6 MB
8. Interpreting results of Categorical variables.srt	5.4 KB
9. Multiple Linear Regression in Python.mp4	73.1 MB
9. Multiple Linear Regression in Python.srt	12.6 KB
/17. Practice Assignment/
1. Neural Networks Classification Assignment.html	0.2 KB
/18. Bonus Section/
1. The final milestone!.mp4	12.4 MB
1. The final milestone!.srt	1.8 KB
2. Congratulations & About your certificate.html	1.6 KB
/2. Setting up Python and Jupyter Notebook/
1. Installing Python and Anaconda.mp4	17.1 MB
1. Installing Python and Anaconda.srt	2.6 KB
2. Opening Jupyter Notebook.mp4	68.3 MB
2. Opening Jupyter Notebook.srt	9.4 KB
3. Introduction to Jupyter.mp4	42.9 MB
3. Introduction to Jupyter.srt	12.6 KB
4. Arithmetic operators in Python Python Basics.mp4	13.4 MB
4. Arithmetic operators in Python Python Basics.srt	4.1 KB
5. Strings in Python Python Basics.mp4	67.6 MB
5. Strings in Python Python Basics.srt	16.8 KB
6. Lists, Tuples and Directories Python Basics.mp4	63.3 MB
6. Lists, Tuples and Directories Python Basics.srt	17.4 KB
7. Working with Numpy Library of Python.mp4	46.0 MB
7. Working with Numpy Library of Python.srt	10.7 KB
8. Working with Pandas Library of Python.mp4	49.2 MB
8. Working with Pandas Library of Python.srt	8.3 KB
9. Working with Seaborn Library of Python.mp4	42.3 MB
9. Working with Seaborn Library of Python.srt	7.7 KB
/3. Single Cells - Perceptron and Sigmoid Neuron/
1. Perceptron.mp4	46.9 MB
1. Perceptron.srt	9.9 KB
2. Activation Functions.mp4	36.3 MB
2. Activation Functions.srt	8.0 KB
3. Python - Creating Perceptron model.mp4	90.8 MB
3. Python - Creating Perceptron model.srt	14.9 KB
/4. Neural Networks - Stacking cells to create network/
1. Basic Terminologies.mp4	42.4 MB
1. Basic Terminologies.srt	9.7 KB
2. Gradient Descent.mp4	63.3 MB
2. Gradient Descent.srt	12.2 KB
3. Back Propagation.mp4	128.1 MB
3. Back Propagation.srt	23.3 KB
/5. Important concepts Common Interview questions/
1. Some Important Concepts.mp4	65.2 MB
1. Some Important Concepts.srt	13.4 KB
2. Quiz.html	0.2 KB
/6. Standard Model Parameters/
1. Hyperparameters.mp4	47.5 MB
1. Hyperparameters.srt	9.2 KB
2. Quiz.html	0.2 KB
/7. Practice Test/
1. Test your conceptual understanding.html	0.2 KB
/8. Tensorflow and Keras/
1. Keras and Tensorflow.mp4	15.7 MB
1. Keras and Tensorflow.srt	3.6 KB
2. Installing Tensorflow and Keras.mp4	21.0 MB
2. Installing Tensorflow and Keras.srt	3.9 KB
/9. Python - Dataset for classification problem/
1. Dataset for classification.mp4	58.8 MB
1. Dataset for classification.srt	7.3 KB
2. Normalization and Test-Train split.mp4	46.3 MB
2. Normalization and Test-Train split.srt	5.9 KB
3. More about test-train split.html	0.6 KB
/
[CourseClub.Me].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
Total files 139