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种子名称： [GigaCourse.Com] Udemy - Neural Networks in Python Deep Learning for Beginners
文件类型： 视频
文件数目： 63个文件
文件大小： 3.02 GB
收录时间： 2023-1-31 23:27
已经下载： 3次
资源热度： 171
最近下载： 2024-11-13 06:59

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[GigaCourse.Com] Udemy - Neural Networks in Python Deep Learning for Beginners.torrent

1. Introduction/1. Welcome to the course.mp421.42MB

1. Introduction/2. Introduction to Neural Networks and Course flow.mp429.07MB

1. Introduction/4. This is a milestone!.mp420.68MB

10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp410.8MB

10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp479.14MB

10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp481.71MB

10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp469.92MB

11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4155.87MB

12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp492.12MB

13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4151.58MB

14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.63MB

15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp422.28MB

15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.mp425.01MB

15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.mp423.42MB

15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp417.02MB

15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.mp4100.42MB

15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.mp444.08MB

15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp420.24MB

15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.mp436.83MB

15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.mp426.53MB

15. Add-on 1 Data Preprocessing/18. Correlation Analysis.mp471.59MB

15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.mp455.31MB

15. Add-on 1 Data Preprocessing/2. Data Exploration.mp420.51MB

15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.38MB

15. Add-on 1 Data Preprocessing/5. Importing Data in Python.mp427.83MB

15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.mp424.19MB

15. Add-on 1 Data Preprocessing/7. EDD in Python.mp461.78MB

15. Add-on 1 Data Preprocessing/8. Outlier Treatment.mp424.48MB

15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.mp470.23MB

16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp49.38MB

16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp441.87MB

16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp425.1MB

16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp444.86MB

16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.35MB

16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.14MB

16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.63MB

16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp463.43MB

16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp434.32MB

16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp456.01MB

16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp422.5MB

16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp469.74MB

18. Bonus Section/1. The final milestone!.mp411.83MB

2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.26MB

2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp465.17MB

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.mp464.43MB

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.mp443.87MB

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. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76MB

3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.61MB

3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.59MB

4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42MB

4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.32MB

4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB

5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.17MB

6. Standard Model Parameters/1. Hyperparameters.mp445.35MB

8. Tensorflow and Keras/1. Keras and Tensorflow.mp414.93MB

8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp420.07MB

9. Python - Dataset for classification problem/1. Dataset for classification.mp456.12MB

9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp444.19MB