GigaCourse Com Udemy - Neural Networks in Python Deep Learning for Beginners

1. 1. Introduction/1. Welcome to the course.mp421.42MB
2. 1. Introduction/2. Introduction to Neural Networks and Course flow.mp429.07MB
3. 1. Introduction/4. This is a milestone!.mp420.68MB
4. 10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp410.8MB
5. 10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp479.14MB
6. 10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp481.71MB
7. 10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp469.92MB
8. 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4155.87MB
9. 12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp492.12MB
10. 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4151.58MB
11. 14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.63MB
12. 15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp422.28MB
13. 15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.mp425.01MB
14. 15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.mp423.42MB
15. 15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp417.02MB
16. 15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.mp4100.42MB
17. 15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.mp444.08MB
18. 15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp420.24MB
19. 15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.mp436.83MB
20. 15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.mp426.53MB
21. 15. Add-on 1 Data Preprocessing/18. Correlation Analysis.mp471.59MB
22. 15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.mp455.31MB
23. 15. Add-on 1 Data Preprocessing/2. Data Exploration.mp420.51MB
24. 15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.38MB
25. 15. Add-on 1 Data Preprocessing/5. Importing Data in Python.mp427.83MB
26. 15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.mp424.19MB
27. 15. Add-on 1 Data Preprocessing/7. EDD in Python.mp461.78MB
28. 15. Add-on 1 Data Preprocessing/8. Outlier Treatment.mp424.48MB
29. 15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.mp470.23MB
30. 16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp49.38MB
31. 16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp441.87MB
32. 16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp425.1MB
33. 16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp444.86MB
34. 16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.35MB
35. 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.14MB
36. 16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.63MB
37. 16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp463.43MB
38. 16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp434.32MB
39. 16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp456.01MB
40. 16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp422.5MB
41. 16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp469.74MB
42. 18. Bonus Section/1. The final milestone!.mp411.83MB
43. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.26MB
44. 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp465.17MB
45. 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp440.92MB
46. 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp412.75MB
47. 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp464.43MB
48. 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp460.32MB
49. 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp443.87MB
50. 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp446.89MB
51. 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp440.35MB
52. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76MB
53. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.61MB
54. 3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp486.59MB
55. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42MB
56. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.32MB
57. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB
58. 5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.17MB
59. 6. Standard Model Parameters/1. Hyperparameters.mp445.35MB
60. 8. Tensorflow and Keras/1. Keras and Tensorflow.mp414.93MB
61. 8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp420.07MB
62. 9. Python - Dataset for classification problem/1. Dataset for classification.mp456.12MB
63. 9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp444.19MB