种子简介

种子名称： [GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python
文件类型： 视频
文件数目： 61个文件
文件大小： 2.99 GB
收录时间： 2021-1-8 15:09
已经下载： 3次
资源热度： 300
最近下载： 2024-6-26 07:54

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:023489e261f71d8d732df009e55d6ff2895bf056&dn=[GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python.torrent

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

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

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.mp469.93MB

11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4155.88MB

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.mp4151.57MB

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

15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp422.29MB

15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp423.42MB

15. Add-on 1 Data Preprocessing/11. Seasonality in Data.mp417.03MB

15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4100.42MB

15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp444.08MB

15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp420.24MB

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

15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp426.54MB

15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp471.6MB

15. Add-on 1 Data Preprocessing/18. 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/4. Importing Data in Python.mp427.83MB

15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp424.2MB

15. Add-on 1 Data Preprocessing/6. EDD in Python.mp461.78MB

15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp424.48MB

15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp470.23MB

15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp425.01MB

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.mp425.11MB

16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp444.87MB

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.mp422.51MB

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

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.mp465.18MB

2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp440.91MB

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.mp460.33MB

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.mp440.36MB

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

3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.62MB

3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.6MB

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

4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.33MB

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.mp414.92MB

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

9. Python - Dataset for classification problem/1. Dataset for classification.mp456.13MB

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