Deep Learning: UNIT 1 : Deep Learning Fundamentals

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  Deep Learning

UNIT I

Deep Learning: Fundamentals

  • Introduction
  • Building Block of Neural Networks
  • Layers
  • MLPs
  • Forward pass
  • backward pass
  • class
  • trainer and optimizer
  • The Vanishing and Exploding Gradient Problems
  • Difficulties in Convergence
  • Local and Spurious Optima
  • Preprocessing
  • Momentum
  • learning rate Decay
  • Weight Initialization
  • Regularization
  • Dropout
  • SoftMax
  • Cross Entropy loss function
  • Activation Functions


👉Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes

👉Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs

👉Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes

👉Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs

👉Deep Learning: UNIT 1: Deep Learning Fundamentals -Long Answer Questions

👉Deep Learning: UNIT 1: Deep Learning Fundamentals - Short Answer Questions

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