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Deep Learning

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Supervised Learning and Neural Networks

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0 of 64 lessons
  • Introduction1:56
  • Neural Network8:13
  • Supervised Learning and Neural Networks7:52
  • Binary Classification13:55
  • Load Symbol List
  • Logistic Regression Algorithm8:51
  • Cost Function11:43
  • Calculate Variable Values14:00
  • Calculus13:34
  • Computational Graph6:23
  • Computational Graph for Derivative Calculation15:24
  • Derivative Calculation for Algorithm Application12:51
  • Derivative Calculation for Several Examples10:34
  • Understanding Directions15:59
  • Load Practical Example
  • Using Directions in Linear Regression Algorithm14:45
  • Distribution in Python14:29
  • Load Practical Example
  • Introduction to Hollow Neural Network11:08
  • Hollow Neural Network Representation and Output Calculation17:30
  • Output Calculation for Several Examples10:25
  • Activation Function11:23
  • Calculus for Activation Functions15:58
  • Loading Function Format
  • Implementing Gradient Descent for the Hollow Neural Network18:27
  • Introduction to Deep Neural Networks11:19
  • Forward Propagation in Deep Neural Networks13:52
  • How to Calculate Matrix Dimensions22:52
  • Core Components for Implementing Deep Neural Networks14:14
  • Implementing Core Components for Gradient Descent Calculation11:22
  • Hyperparameters4:44