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种子名称：
[GigaCourse.com] Udemy - Deep Learning with Keras and Tensorflow in Python and R
文件类型：
视频
文件数目：
72个文件
文件大小：
4 GB
收录时间：
2020-11-23 09:36
已经下载：
3次
资源热度：
247
最近下载：
2024-9-13 16:45
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magnet:?xt=urn:btih:a24dc0ed8c01e123276ab97f1f6716e974dd2995&dn=[GigaCourse.com] Udemy - Deep Learning with Keras and Tensorflow in Python and R
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种子包含的文件
[GigaCourse.com] Udemy - Deep Learning with Keras and Tensorflow in Python and R.torrent
1. Introduction/1. Introduction.mp429.1MB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp410.81MB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp479.15MB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp481.66MB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp469.87MB
11. R - Building and training the Model/1. Building,Compiling and Training.mp4130.73MB
11. R - Building and training the Model/2. Evaluating and Predicting.mp499.26MB
12. Python - Regression problems and Functional API/1. Building Neural Network for Regression Problem.mp4155.87MB
12. Python - Regression problems and Functional API/2. Using Functional API for complex architectures.mp492.14MB
13. R - Regression Problem and Functional API/1. Building Regression Model with Functional AP.mp4131.14MB
13. R - Regression Problem and Functional API/2. Complex Architectures using Functional API.mp479.58MB
14. Python - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4151.63MB
15. R - Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4216.1MB
16. Python - Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.64MB
17. R - Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.63MB
18. Add on Data Preprocessing/1. Gathering Business Knowledge.mp422.29MB
18. Add on Data Preprocessing/10. Outlier Treatment in Python.mp470.24MB
18. Add on Data Preprocessing/11. Outlier Treatment in R.mp430.75MB
18. Add on Data Preprocessing/12. Missing Value imputation.mp424.99MB
18. Add on Data Preprocessing/13. Missing Value Imputation in Python.mp423.42MB
18. Add on Data Preprocessing/14. Missing Value imputation in R.mp426MB
18. Add on Data Preprocessing/15. Seasonality in Data.mp417.04MB
18. Add on Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4100.47MB
18. Add on Data Preprocessing/17. Variable transformation and deletion in Python.mp444.12MB
18. Add on Data Preprocessing/18. Variable transformation in R.mp455.43MB
18. Add on Data Preprocessing/19. Non Usable Variables.mp420.25MB
18. Add on Data Preprocessing/2. Data Exploration.mp420.51MB
18. Add on Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp436.84MB
18. Add on Data Preprocessing/21. Dummy variable creation in Python.mp426.53MB
18. Add on Data Preprocessing/22. Dummy variable creation in R.mp443.97MB
18. Add on Data Preprocessing/3. The Data and the Data Dictionary.mp469.34MB
18. Add on Data Preprocessing/4. Importing Data in Python.mp427.84MB
18. Add on Data Preprocessing/5. Importing the dataset into R.mp413.1MB
18. Add on Data Preprocessing/6. Univariate Analysis and EDD.mp424.2MB
18. Add on Data Preprocessing/7. EDD in Python.mp461.78MB
18. Add on Data Preprocessing/8. EDD in R.mp496.98MB
18. Add on Data Preprocessing/9. Outlier Treatment.mp424.48MB
19. Test Train Split/1. Test-train split.mp441.87MB
19. Test Train Split/2. Bias Variance trade-off.mp425.1MB
19. Test Train Split/3. Test train split in Python.mp444.87MB
19. Test Train Split/4. Test train split in R.mp475.62MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.28MB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp465.2MB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp440.92MB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp412.75MB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp464.44MB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp460.32MB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp443.89MB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp446.89MB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp440.35MB
3. Setting up R Studio and R Crash Course/1. Installing R and R studio.mp435.7MB
3. Setting up R Studio and R Crash Course/2. Basics of R and R studio.mp438.85MB
3. Setting up R Studio and R Crash Course/3. Packages in R.mp482.95MB
3. Setting up R Studio and R Crash Course/4. Inputting data part 1 Inbuilt datasets of R.mp440.73MB
3. Setting up R Studio and R Crash Course/5. Inputting data part 2 Manual data entry.mp425.52MB
3. Setting up R Studio and R Crash Course/6. Inputting data part 3 Importing from CSV or Text files.mp460.07MB
3. Setting up R Studio and R Crash Course/7. Creating Barplots in R.mp496.76MB
3. Setting up R Studio and R Crash Course/8. Creating Histograms in R.mp442.01MB
4. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.75MB
4. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.63MB
4. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.59MB
5. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.43MB
5. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34MB
5. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB
6. Important concepts Common Interview questions/1. Some Important Concepts.mp462.18MB
7. Standard Model Parameters/1. Hyperparameters.mp445.35MB
8. Tensorflow and Keras/1. Keras and Tensorflow.mp414.92MB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras in Python.mp420.06MB
8. Tensorflow and Keras/3. Installing TensorFlow and Keras in R.mp422.83MB
9. Dataset for classification problem/1. Python - Dataset for classification problem.mp456.18MB
9. Dataset for classification problem/2. Python - Normalization and Test-Train split.mp444.21MB
9. Dataset for classification problem/3. R - Dataset, Normalization and Test-Train set.mp4111.81MB
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