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/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/34. Pre-Notebook Analyzing Student Data.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/35. Notebook Analyzing Student Data.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/25. Pre-Notebook Gradient Descent.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/26. Notebook Gradient Descent.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/29. Non-linear Data.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/10. Perceptron Algorithm.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/16. One-Hot Encoding.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/27. Perceptron vs Gradient Descent.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/31. Neural Network Architecture.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/48. Learning Rate Decay.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/38. Overfitting and Underfitting.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/45. Vanishing Gradient.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/07. Why Neural Networks.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/08. Perceptrons as Logical Operators.html

/Part 01-Module 01-Lesson 02_Introduction to Neural Networks/14. Discrete vs Continuous.html

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