Deep Learning: UNIT 1 : Deep Learning Fundamentals

  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