[GigaCourse.com] Udemy - Deep Learning with Keras and Tensorflow in Python and R

Creation Time	May 21, 2024, 10:38 a.m.
Last Access Time	Nov. 18, 2024, 7:44 a.m.
File Size	4.0 GB
Keywords	and Keras Python with - Deep Udemy R GigaCourse Learning in Tensorflow com
Total Requests	18
Total Files	151
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15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 216.1 MB
12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.mp4 155.9 MB
14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 151.6 MB
13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.mp4 131.1 MB
11. R - Building and training the Model/1. Building,Compiling and Training.mp4 130.7 MB
5. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122.2 MB
9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.mp4 111.8 MB
18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4 100.5 MB
11. R - Building and training the Model/2. Evaluating and Predicting.mp4 99.3 MB
18. Add on Data Preprocessing/8. EDD in R.mp4 97.0 MB
3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.mp4 96.8 MB
12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.mp4 92.1 MB
4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 86.6 MB
3. Setting up R Studio and R Crash Course/3. Packages in R.mp4 82.9 MB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 81.7 MB
13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.mp4 79.6 MB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 79.1 MB
19. Test Train Split/4. Test train split in R.mp4 75.6 MB
18. Add on Data Preprocessing/10. Outlier Treatment in Python.mp4 70.2 MB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 69.9 MB
18. Add on Data Preprocessing/3. The Data and the Data Dictionary.mp4 69.3 MB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 65.2 MB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 64.4 MB
6. Important concepts Common Interview questions/1. Some Important Concepts.mp4 62.2 MB
18. Add on Data Preprocessing/7. EDD in Python.mp4 61.8 MB
16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 60.6 MB
17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 60.6 MB
5. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60.3 MB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 60.3 MB
3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.mp4 60.1 MB
9. Dataset for classification problem/1. Python - Dataset for classification problem.mp4 56.2 MB
18. Add on Data Preprocessing/18. Variable transformation in R.mp4 55.4 MB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 46.9 MB
7. Standard Model Parameters/1. Hyperparameters.mp4 45.4 MB
19. Test Train Split/3. Test train split in Python.mp4 44.9 MB
4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 44.8 MB
9. Dataset for classification problem/2. Python - Normalization and Test-Train split.mp4 44.2 MB
18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.mp4 44.1 MB
18. Add on Data Preprocessing/22. Dummy variable creation in R.mp4 44.0 MB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 43.9 MB
3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.mp4 42.0 MB
19. Test Train Split/1. Test-train split.mp4 41.9 MB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 40.9 MB
3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.mp4 40.7 MB
5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40.4 MB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 40.3 MB
3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.mp4 38.9 MB
18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 36.8 MB
3. Setting up R Studio and R Crash Course/1. Installing R and R studio.mp4 35.7 MB
4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 34.6 MB
18. Add on Data Preprocessing/11. Outlier Treatment in R.mp4 30.7 MB
1. Introduction/1. Introduction.mp4 29.1 MB
18. Add on Data Preprocessing/4. Importing Data in Python.mp4 27.8 MB
18. Add on Data Preprocessing/21. Dummy variable creation in Python.mp4 26.5 MB
18. Add on Data Preprocessing/14. Missing Value imputation in R.mp4 26.0 MB
3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.mp4 25.5 MB
19. Test Train Split/2. Bias Variance trade-off.mp4 25.1 MB
18. Add on Data Preprocessing/12. Missing Value imputation.mp4 25.0 MB
18. Add on Data Preprocessing/9. Outlier Treatment.mp4 24.5 MB
18. Add on Data Preprocessing/6. Univariate Analysis and EDD.mp4 24.2 MB
18. Add on Data Preprocessing/13. Missing Value Imputation in Python.mp4 23.4 MB
8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.mp4 22.8 MB
18. Add on Data Preprocessing/1. Gathering Business Knowledge.mp4 22.3 MB
18. Add on Data Preprocessing/2. Data Exploration.mp4 20.5 MB
18. Add on Data Preprocessing/19. Non Usable Variables.mp4 20.2 MB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.mp4 20.1 MB
18. Add on Data Preprocessing/15. Seasonality in Data.mp4 17.0 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16.3 MB
8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 14.9 MB
18. Add on Data Preprocessing/5. Importing the dataset into R.mp4 13.1 MB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 12.7 MB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 10.8 MB
1. Introduction/2.1 keras.zip 5.5 MB
2. Setting up Python and Jupyter Notebook/8.1 Product.txt 139.5 KB
2. Setting up Python and Jupyter Notebook/8.2 Customer.csv 64.0 KB
5. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 22.8 KB
12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.srt 21.7 KB
15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 20.4 KB
14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 18.8 KB
18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.srt 18.3 KB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17.0 KB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16.4 KB
11. R - Building and training the Model/1. Building,Compiling and Training.srt 15.4 KB
4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 14.5 KB
3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.srt 13.4 KB
6. Important concepts Common Interview questions/1. Some Important Concepts.srt 13.1 KB
13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.srt 13.1 KB
18. Add on Data Preprocessing/10. Outlier Treatment in Python.srt 13.0 KB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12.3 KB
9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.srt 12.1 KB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12.0 KB
5. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 11.9 KB
18. Add on Data Preprocessing/8. EDD in R.srt 11.6 KB
12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.srt 11.5 KB
3. Setting up R Studio and R Crash Course/3. Packages in R.srt 11.5 KB
3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.srt 10.8 KB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10.5 KB
18. Add on Data Preprocessing/7. EDD in Python.srt 10.4 KB
19. Test Train Split/1. Test-train split.srt 10.1 KB
4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 9.7 KB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 9.6 KB
5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 9.5 KB
16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.4 KB
17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.4 KB
11. R - Building and training the Model/2. Evaluating and Predicting.srt 9.4 KB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9.1 KB
18. Add on Data Preprocessing/18. Variable transformation in R.srt 9.0 KB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9.0 KB
7. Standard Model Parameters/1. Hyperparameters.srt 8.9 KB
19. Test Train Split/4. Test train split in R.srt 8.4 KB
13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.srt 8.3 KB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8.2 KB
19. Test Train Split/3. Test train split in Python.srt 8.1 KB
4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 7.9 KB
18. Add on Data Preprocessing/3. The Data and the Data Dictionary.srt 7.8 KB
18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.srt 7.5 KB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 7.5 KB
9. Dataset for classification problem/1. Python - Dataset for classification problem.srt 7.2 KB
3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.srt 6.4 KB
19. Test Train Split/2. Bias Variance trade-off.srt 6.4 KB
3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.srt 5.9 KB
9. Dataset for classification problem/2. Python - Normalization and Test-Train split.srt 5.7 KB
3. Setting up R Studio and R Crash Course/1. Installing R and R studio.srt 5.6 KB
18. Add on Data Preprocessing/4. Importing Data in Python.srt 5.6 KB
18. Add on Data Preprocessing/21. Dummy variable creation in Python.srt 5.5 KB
18. Add on Data Preprocessing/19. Non Usable Variables.srt 5.4 KB
18. Add on Data Preprocessing/22. Dummy variable creation in R.srt 5.2 KB
18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 4.9 KB
1. Introduction/1. Introduction.srt 4.6 KB
18. Add on Data Preprocessing/9. Outlier Treatment.srt 4.5 KB
18. Add on Data Preprocessing/11. Outlier Treatment in R.srt 4.3 KB
18. Add on Data Preprocessing/12. Missing Value imputation.srt 4.1 KB
18. Add on Data Preprocessing/13. Missing Value Imputation in Python.srt 4.1 KB
3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.srt 4.0 KB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4.0 KB
18. Add on Data Preprocessing/1. Gathering Business Knowledge.srt 3.9 KB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.srt 3.8 KB
18. Add on Data Preprocessing/15. Seasonality in Data.srt 3.8 KB
18. Add on Data Preprocessing/2. Data Exploration.srt 3.6 KB
8. Tensorflow and Keras/1. Keras and Tensorflow.srt 3.6 KB
18. Add on Data Preprocessing/14. Missing Value imputation in R.srt 3.5 KB
18. Add on Data Preprocessing/6. Univariate Analysis and EDD.srt 3.4 KB
8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.srt 3.0 KB
3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.srt 3.0 KB
18. Add on Data Preprocessing/5. Importing the dataset into R.srt 2.6 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
Readme.txt 962 bytes
5. Neural Networks - Stacking cells to create network/4. Quiz.html 166 bytes
1. Introduction/2. Course Resources.html 117 bytes
[GigaCourse.com].url 49 bytes

[GigaCourse.com] Udemy - Deep Learning with Keras and Tensorflow in Python and R

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