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 (A) PPTs: Deep Learning Fundamentals PPTs

                                                                                    UNIT I (A)

Deep Learning: Fundamentals

1.      Introduction

2.      Building Block of Neural Networks

3.      Layers

4.      MLPs

5.      Forward pass

6.      backward pass

7.      class

8.      trainer and optimizer

9.      The Vanishing and Exploding Gradient Problems

10.  Difficulties in Convergence

11.  Local and Spurious Optima

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

                                                                             UNIT I (A)

Deep Learning: Fundamentals

1.      Introduction

2.      Building Block of Neural Networks

3.      Layers

4.      MLPs

5.      Forward pass

6.      backward pass

7.      class

8.      trainer and optimizer

9.      The Vanishing and Exploding Gradient Problems

10.  Difficulties in Convergence

11.  Local and Spurious Optima