Artificial Intelligence

Course Content

Total learning: 10 lessons

Part – 4

 Various Hyper Parameters In Neural Networks

  • Understand about challenges in Gradient
  • Introduction to various Error, Cost, Loss functions
  • ME, MAD, MSE, RMSE, MPE, MAPE, Entropy, Cross Entropy
  • Vanishing / Exploding Gradient
  • Learning Rate (Eta), Decay Parameter, Iteration, Epoch
  • Variants of Gradient Descent
  • Batch Gradient Descent (BGD)
  • Stochastic Gradient Descent (SGD)
  • Mini-batch Stochastic Gradient Descent (Mini-batch SGD)
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