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

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

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收录时间：2021-04-29
最近下载：2025-08-11

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文件列表

  • 15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 226.6 MB
  • 12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.mp4 163.4 MB
  • 14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 159.0 MB
  • 13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.mp4 137.5 MB
  • 11. R - Building and training the Model/1. Building,Compiling and Training.mp4 137.1 MB
  • 5. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
  • 9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.mp4 117.2 MB
  • 18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4 105.4 MB
  • 11. R - Building and training the Model/2. Evaluating and Predicting.mp4 104.1 MB
  • 18. Add on Data Preprocessing/8. EDD in R.mp4 101.7 MB
  • 3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.mp4 101.5 MB
  • 12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.mp4 96.6 MB
  • 4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 90.8 MB
  • 3. Setting up R Studio and R Crash Course/3. Packages in R.mp4 87.0 MB
  • 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 85.6 MB
  • 13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.mp4 83.4 MB
  • 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 83.0 MB
  • 19. Test Train Split/4. Test train split in R.mp4 79.3 MB
  • 18. Add on Data Preprocessing/10. Outlier Treatment in Python.mp4 73.7 MB
  • 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 73.3 MB
  • 18. Add on Data Preprocessing/3. The Data and the Data Dictionary.mp4 72.7 MB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 68.4 MB
  • 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 67.6 MB
  • 6. Important concepts Common Interview questions/1. Some Important Concepts.mp4 65.2 MB
  • 18. Add on Data Preprocessing/7. EDD in Python.mp4 64.8 MB
  • 16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 63.6 MB
  • 17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 63.6 MB
  • 5. 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 63.2 MB
  • 3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.mp4 63.0 MB
  • 9. Dataset for classification problem/1. Python - Dataset for classification problem.mp4 58.9 MB
  • 18. Add on Data Preprocessing/18. Variable transformation in R.mp4 58.1 MB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 49.2 MB
  • 7. Standard Model Parameters/1. Hyperparameters.mp4 47.6 MB
  • 19. Test Train Split/3. Test train split in Python.mp4 47.1 MB
  • 4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 46.9 MB
  • 9. Dataset for classification problem/2. Python - Normalization and Test-Train split.mp4 46.4 MB
  • 18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.mp4 46.3 MB
  • 18. Add on Data Preprocessing/22. Dummy variable creation in R.mp4 46.1 MB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 46.0 MB
  • 3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.mp4 44.1 MB
  • 19. Test Train Split/1. Test-train split.mp4 43.9 MB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 42.9 MB
  • 3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.mp4 42.7 MB
  • 5. 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
  • 3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.mp4 40.7 MB
  • 18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 38.6 MB
  • 3. Setting up R Studio and R Crash Course/1. Installing R and R studio.mp4 37.4 MB
  • 4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 36.3 MB
  • 18. Add on Data Preprocessing/11. Outlier Treatment in R.mp4 32.2 MB
  • 1. Introduction/1. Introduction.mp4 30.5 MB
  • 18. Add on Data Preprocessing/4. Importing Data in Python.mp4 29.2 MB
  • 18. Add on Data Preprocessing/21. Dummy variable creation in Python.mp4 27.8 MB
  • 18. Add on Data Preprocessing/14. Missing Value imputation in R.mp4 27.3 MB
  • 3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.mp4 26.8 MB
  • 19. Test Train Split/2. Bias Variance trade-off.mp4 26.3 MB
  • 18. Add on Data Preprocessing/12. Missing Value imputation.mp4 26.2 MB
  • 18. Add on Data Preprocessing/9. Outlier Treatment.mp4 25.7 MB
  • 18. Add on Data Preprocessing/6. Univariate Analysis and EDD.mp4 25.4 MB
  • 18. Add on Data Preprocessing/13. Missing Value Imputation in Python.mp4 24.6 MB
  • 8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.mp4 23.9 MB
  • 18. Add on Data Preprocessing/1. Gathering Business Knowledge.mp4 23.4 MB
  • 18. Add on Data Preprocessing/2. Data Exploration.mp4 21.5 MB
  • 18. Add on Data Preprocessing/19. Non Usable Variables.mp4 21.2 MB
  • 8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.mp4 21.0 MB
  • 18. Add on Data Preprocessing/15. 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
  • 18. Add on Data Preprocessing/5. Importing the dataset into R.mp4 13.7 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/2.1 keras.zip 5.8 MB
  • 2. Setting up Python and Jupyter Notebook/8.1 Product.txt 142.8 kB
  • 2. Setting up Python and Jupyter Notebook/8.2 Customer.csv 65.6 kB
  • 5. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23.3 kB
  • 12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.srt 22.2 kB
  • 15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 20.9 kB
  • 14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 19.2 kB
  • 18. Add on Data Preprocessing/16. 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
  • 11. R - Building and training the Model/1. Building,Compiling and Training.srt 15.8 kB
  • 4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 14.9 kB
  • 3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.srt 13.7 kB
  • 6. Important concepts Common Interview questions/1. Some Important Concepts.srt 13.4 kB
  • 13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.srt 13.4 kB
  • 18. Add on Data Preprocessing/10. Outlier Treatment in Python.srt 13.3 kB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12.6 kB
  • 9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.srt 12.4 kB
  • 10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12.2 kB
  • 5. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12.2 kB
  • 18. Add on Data Preprocessing/8. EDD in R.srt 11.8 kB
  • 12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.srt 11.8 kB
  • 3. Setting up R Studio and R Crash Course/3. Packages in R.srt 11.7 kB
  • 3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.srt 11.1 kB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10.7 kB
  • 18. Add on Data Preprocessing/7. EDD in Python.srt 10.6 kB
  • 19. Test Train Split/1. Test-train split.srt 10.3 kB
  • 4. 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
  • 5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 9.7 kB
  • 16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.7 kB
  • 17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.7 kB
  • 11. R - Building and training the Model/2. Evaluating and Predicting.srt 9.7 kB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9.4 kB
  • 18. Add on Data Preprocessing/18. Variable transformation in R.srt 9.3 kB
  • 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9.2 kB
  • 7. Standard Model Parameters/1. Hyperparameters.srt 9.2 kB
  • 19. Test Train Split/4. Test train split in R.srt 8.6 kB
  • 13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.srt 8.5 kB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8.3 kB
  • 19. Test Train Split/3. Test train split in Python.srt 8.2 kB
  • 4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8.0 kB
  • 18. Add on Data Preprocessing/3. The Data and the Data Dictionary.srt 8.0 kB
  • 18. Add on Data Preprocessing/17. 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. Dataset for classification problem/1. Python - Dataset for classification problem.srt 7.3 kB
  • 3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.srt 6.5 kB
  • 19. Test Train Split/2. Bias Variance trade-off.srt 6.5 kB
  • 3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.srt 6.0 kB
  • 9. Dataset for classification problem/2. Python - Normalization and Test-Train split.srt 5.9 kB
  • 3. Setting up R Studio and R Crash Course/1. Installing R and R studio.srt 5.8 kB
  • 18. Add on Data Preprocessing/4. Importing Data in Python.srt 5.7 kB
  • 18. Add on Data Preprocessing/21. Dummy variable creation in Python.srt 5.6 kB
  • 18. Add on Data Preprocessing/19. Non Usable Variables.srt 5.5 kB
  • 18. Add on Data Preprocessing/22. Dummy variable creation in R.srt 5.3 kB
  • 18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 5.0 kB
  • 1. Introduction/1. Introduction.srt 4.7 kB
  • 18. Add on Data Preprocessing/9. Outlier Treatment.srt 4.6 kB
  • 18. Add on Data Preprocessing/11. Outlier Treatment in R.srt 4.4 kB
  • 18. Add on Data Preprocessing/12. Missing Value imputation.srt 4.2 kB
  • 18. Add on Data Preprocessing/13. Missing Value Imputation in Python.srt 4.2 kB
  • 3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.srt 4.1 kB
  • 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4.1 kB
  • 18. Add on Data Preprocessing/1. Gathering Business Knowledge.srt 4.0 kB
  • 8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.srt 3.9 kB
  • 18. Add on Data Preprocessing/15. Seasonality in Data.srt 3.9 kB
  • 18. Add on Data Preprocessing/2. Data Exploration.srt 3.7 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 3.5 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 2.7 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

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