GigaCourse com Udemy - Neural Networks ANN using Keras and TensorFlow in Python

1. 1. Introduction/1. Welcome to the course.mp421.42MB
2. 1. Introduction/2. Introduction to Neural Networks and Course flow.mp429.07MB
3. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp410.8MB
4. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp479.14MB
5. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp481.71MB
6. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp469.93MB
7. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4155.88MB
8. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp492.12MB
9. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4151.57MB
10. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.63MB
11. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp422.29MB
12. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation in Python.mp423.42MB
13. 15. Add-on 1 Data Preprocessing/11. Seasonality in Data.mp417.03MB
14. 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4100.42MB
15. 15. Add-on 1 Data Preprocessing/13. Variable transformation and deletion in Python.mp444.08MB
16. 15. Add-on 1 Data Preprocessing/14. Non-usable variables.mp420.24MB
17. 15. Add-on 1 Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp436.83MB
18. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation in Python.mp426.54MB
19. 15. Add-on 1 Data Preprocessing/17. Correlation Analysis.mp471.6MB
20. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis in Python.mp455.31MB
21. 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp420.51MB
22. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.38MB
23. 15. Add-on 1 Data Preprocessing/4. Importing Data in Python.mp427.83MB
24. 15. Add-on 1 Data Preprocessing/5. Univariate analysis and EDD.mp424.2MB
25. 15. Add-on 1 Data Preprocessing/6. EDD in Python.mp461.78MB
26. 15. Add-on 1 Data Preprocessing/7. Outlier Treatment.mp424.48MB
27. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment in Python.mp470.23MB
28. 15. Add-on 1 Data Preprocessing/9. Missing Value Imputation.mp425.01MB
29. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp49.38MB
30. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp441.87MB
31. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp425.11MB
32. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp444.87MB
33. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.35MB
34. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.14MB
35. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.63MB
36. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp463.43MB
37. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp434.32MB
38. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp456.01MB
39. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp422.51MB
40. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp469.74MB
41. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.26MB
42. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp465.18MB
43. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp440.91MB
44. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp412.75MB
45. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp464.43MB
46. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp460.33MB
47. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp443.87MB
48. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp446.89MB
49. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp440.36MB
50. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76MB
51. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.62MB
52. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.6MB
53. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42MB
54. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.33MB
55. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB
56. 5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.17MB
57. 6. Standard Model Parameters/1. Hyperparameters.mp445.35MB
58. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp414.92MB
59. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp420.07MB
60. 9. Python - Dataset for classification problem/1. Dataset for classification.mp456.13MB
61. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp444.2MB