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

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

                                                                             UNIT-1 B

Deep Learning: Fundamentals

1.     The Softmax Function

2.     Cross-Entropy Loss Function

3.     Activation Functions

4.     Preprocessing

5.     Momentum

6.     Learning Rate Decay

7.     Weight Initialization

8.     Regularization

9.     Dropout

Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part2 Notes

 UNIT-1 B

Deep Learning: Fundamentals

1.     The Softmax Function

2.     Cross-Entropy Loss Function

3.     Activation Functions

4.     Preprocessing

5.     Momentum

6.     Learning Rate Decay

7.     Weight Initialization

8.     Regularization

9.     Dropout