[GigaCourse.com] Udemy - Neural Networks (ANN) using Keras and TensorFlow in Python
大小
3 GB
文件数
129
Info Hash:
023489E261F71D8D732DF009E55D6FF2895BF056
收录时间
2025-12-22 18:48:05
更新时间
2026-01-04 21:12:31
文件列表 (129)
1. Introduction/1. Welcome to the course.mp4
21.42 MB
1. Introduction/1. Welcome to the course.srt
3.15 KB
1. Introduction/2. Introduction to Neural Networks and Course flow.mp4
29.07 MB
1. Introduction/2. Introduction to Neural Networks and Course flow.srt
4.6 KB
1. Introduction/3. Course resources.html
117 B
1. Introduction/3.1 Files_ANN_Py.zip
10.51 MB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4
10.8 MB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt
1.87 KB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4
79.14 MB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt
11.96 KB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4
81.71 MB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt
9.59 KB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4
69.93 MB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt
9.02 KB
11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4
155.88 MB
11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt
21.71 KB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4
92.12 MB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt
11.5 KB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4
151.57 MB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt
18.79 KB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4
60.63 MB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt
9.43 KB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4
22.29 MB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt
3.9 KB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp4
23.42 MB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.srt
4.06 KB
15. Add-on 1 Data Preprocessing/11. Seasonality in Data.mp4
17.03 MB
15. Add-on 1 Data Preprocessing/11. Seasonality in Data.srt
3.78 KB
15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4
100.42 MB
15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.srt
18.29 KB
15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp4
44.08 MB
15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.srt
7.54 KB
15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp4
20.24 MB
15. Add-on 1 Data Preprocessing/14. Non-usable variables.srt
5.39 KB
15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp4
36.83 MB
15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.srt
4.86 KB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp4
26.54 MB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.srt
5.51 KB
15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp4
71.6 MB
15. Add-on 1 Data Preprocessing/17. Correlation Analysis.srt
11.04 KB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.mp4
55.31 MB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.srt
6.55 KB
15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4
20.51 MB
15. Add-on 1 Data Preprocessing/2. Data Exploration.srt
3.6 KB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4
69.38 MB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt
7.82 KB
15. Add-on 1 Data Preprocessing/4. Importing Data in Python.mp4
27.83 MB
15. Add-on 1 Data Preprocessing/4. Importing Data in Python.srt
5.58 KB
15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp4
24.2 MB
15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.srt
3.44 KB
15. Add-on 1 Data Preprocessing/6. EDD in Python.mp4
61.78 MB
15. Add-on 1 Data Preprocessing/6. EDD in Python.srt
10.36 KB
15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp4
24.48 MB
15. Add-on 1 Data Preprocessing/7. Outlier Treatment.srt
4.46 KB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp4
70.23 MB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.srt
13 KB
15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp4
25.01 MB
15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.srt
4.08 KB
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4
9.38 MB
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt
1.61 KB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4
41.87 MB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt
10.05 KB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4
25.11 MB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt
6.37 KB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4
44.87 MB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt
8.05 KB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
43.35 MB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt
9.89 KB
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4
92.14 MB
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt
15.85 KB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4
43.63 MB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt
8.02 KB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4
63.43 MB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt
11.36 KB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4
34.32 MB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt
5.73 KB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4
56.01 MB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt
9.02 KB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4
22.51 MB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt
5.29 KB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4
69.74 MB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt
12.34 KB
17. Practice Assignment/1. Neural Networks Classification Assignment.html
173 B
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4
16.26 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt
2.58 KB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4
65.18 MB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt
9.14 KB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4
40.91 MB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt
12.31 KB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4
12.75 MB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt
3.99 KB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4
64.43 MB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt
16.43 KB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4
60.33 MB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt
17.01 KB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4
43.87 MB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt
10.47 KB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4
46.89 MB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt
8.15 KB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4
40.36 MB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt
7.53 KB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4
44.76 MB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt
9.69 KB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4
34.62 MB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt
7.85 KB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4
86.6 MB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt
14.53 KB
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4
40.42 MB
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt
9.52 KB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4
60.33 MB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt
11.93 KB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4
122.2 MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt
22.78 KB
5. Important concepts Common Interview questions/1. Some Important Concepts.mp4
62.17 MB
5. Important concepts Common Interview questions/1. Some Important Concepts.srt
13.1 KB
5. Important concepts Common Interview questions/2. Quiz.html
169 B
6. Standard Model Parameters/1. Hyperparameters.mp4
45.35 MB
6. Standard Model Parameters/1. Hyperparameters.srt
8.95 KB
7. Practice Test/1. Test your conceptual understanding.html
169 B
8. Tensorflow and Keras/1. Keras and Tensorflow.mp4
14.92 MB
8. Tensorflow and Keras/1. Keras and Tensorflow.srt
3.56 KB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4
20.07 MB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt
3.79 KB
9. Python - Dataset for classification problem/1. Dataset for classification.mp4
56.13 MB
9. Python - Dataset for classification problem/1. Dataset for classification.srt
7.16 KB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp4
44.2 MB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt
5.73 KB
[GigaCourse.com].url
49 B