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[GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python
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[GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python
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023489e261f71d8d732df009e55d6ff2895bf056
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收录时间:
2021-03-14
最近下载:
2025-02-24
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文件列表
11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4
163.5 MB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4
158.9 MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4
128.1 MB
15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4
105.3 MB
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4
96.6 MB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4
96.6 MB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4
90.8 MB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4
85.7 MB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4
83.0 MB
15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp4
75.1 MB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp4
73.6 MB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4
73.3 MB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4
73.1 MB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4
72.7 MB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4
68.3 MB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4
67.6 MB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4
66.5 MB
5. Important concepts Common Interview questions/1. Some Important Concepts.mp4
65.2 MB
15. Add-on 1 Data Preprocessing/6. EDD in Python.mp4
64.8 MB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4
63.6 MB
4. 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
63.3 MB
9. Python - Dataset for classification problem/1. Dataset for classification.mp4
58.9 MB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4
58.7 MB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.mp4
58.0 MB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4
49.2 MB
6. Standard Model Parameters/1. Hyperparameters.mp4
47.5 MB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4
47.0 MB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4
46.9 MB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp4
46.3 MB
15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp4
46.2 MB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4
46.0 MB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4
45.7 MB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
45.5 MB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4
43.9 MB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4
42.9 MB
4. 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
15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp4
38.6 MB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4
36.3 MB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4
36.0 MB
1. Introduction/2. Introduction to Neural Networks and Course flow.mp4
30.5 MB
15. Add-on 1 Data Preprocessing/4. Importing Data in Python.mp4
29.2 MB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp4
27.8 MB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4
26.3 MB
15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp4
26.2 MB
15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp4
25.7 MB
15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp4
25.4 MB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp4
24.6 MB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4
23.6 MB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4
23.4 MB
1. Introduction/1. Welcome to the course.mp4
22.5 MB
15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4
21.5 MB
15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp4
21.2 MB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4
21.0 MB
15. Add-on 1 Data Preprocessing/11. 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
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/3.1 Files_ANN_Py.zip
11.0 MB
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4
9.8 MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt
23.3 kB
11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt
22.2 kB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt
19.2 kB
15. Add-on 1 Data Preprocessing/12. 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
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt
16.2 kB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt
14.9 kB
5. Important concepts Common Interview questions/1. Some Important Concepts.srt
13.4 kB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.srt
13.3 kB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt
12.6 kB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt
12.6 kB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt
12.2 kB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt
12.2 kB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt
11.8 kB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt
11.6 kB
15. Add-on 1 Data Preprocessing/17. Correlation Analysis.srt
11.3 kB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt
10.7 kB
15. Add-on 1 Data Preprocessing/6. EDD in Python.srt
10.6 kB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt
10.3 kB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt
10.1 kB
3. 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
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt
9.7 kB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt
9.7 kB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt
9.4 kB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt
9.2 kB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt
9.2 kB
6. Standard Model Parameters/1. Hyperparameters.srt
9.2 kB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt
8.3 kB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt
8.2 kB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt
8.2 kB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt
8.0 kB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt
8.0 kB
15. Add-on 1 Data Preprocessing/13. 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. Python - Dataset for classification problem/1. Dataset for classification.srt
7.3 kB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.srt
6.7 kB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt
6.5 kB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt
5.9 kB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt
5.9 kB
15. Add-on 1 Data Preprocessing/4. Importing Data in Python.srt
5.7 kB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.srt
5.6 kB
15. Add-on 1 Data Preprocessing/14. Non-usable variables.srt
5.5 kB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt
5.4 kB
15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt
5.0 kB
1. Introduction/2. Introduction to Neural Networks and Course flow.srt
4.7 kB
15. Add-on 1 Data Preprocessing/7. Outlier Treatment.srt
4.6 kB
15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.srt
4.2 kB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.srt
4.2 kB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt
4.1 kB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt
4.0 kB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt
3.9 kB
15. Add-on 1 Data Preprocessing/11. Seasonality in Data.srt
3.9 kB
15. Add-on 1 Data Preprocessing/2. Data Exploration.srt
3.7 kB
8. Tensorflow and Keras/1. Keras and Tensorflow.srt
3.6 kB
15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.srt
3.5 kB
1. Introduction/1. Welcome to the course.srt
3.2 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
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt
1.7 kB
Readme.txt
962 Bytes
17. Practice Assignment/1. Neural Networks Classification Assignment.html
173 Bytes
5. Important concepts Common Interview questions/2. Quiz.html
169 Bytes
7. Practice Test/1. Test your conceptual understanding.html
169 Bytes
1. Introduction/3. Course resources.html
117 Bytes
[GigaCourse.com].url
49 Bytes
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