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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

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

About Deep Learning

 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

 UNIT II

CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of                  CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected.

👉Deep Learning: UNIT-2: CNN PPTs

👉Deep Learning: UNIT-2: CNN Notes

👉Deep Learning: UNIT 2: CNN : Long Answer Questions

👉Deep Learning: UNIT 2: CNN: Short Answer Questions

 UNIT III

RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM 

👉Deep Learning: UNIT 3 (A): RNN: Notes

👉Deep Learning: UNIT 3 (A) : RNN : PPTs

👉Deep Learning: UNIT- 3: RNN: Long Answer Questions

👉Deep Learning: UNIT 3 : RNN : Short Answer Questions

UNIT IV

Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational                                  Autoencoders, GANS 

👉Deep Learning: UNIT 4: Autoencoders PPTs

👉Deep Learning: UNIT 4: Autoencoders NOTEs

UNIT V

Transfer Learning- Types, Methodologies, Diving into Transfer Learning, Challenges 

👉Deep Learning : UNIT 5: Transfer Learning Notes

👉Deep Learning : UNIT 5: Transfer Learning PPTs

Text Books:

1.  Seth Weidman, “Deep Learning from Scratch”, O'Reilly Media, Inc., 2019

2.  Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” , MIT Press, 2015

3.  Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021 

Reference Books:

1.  Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy "Deep Learning with TensorFlow: Explore neural networks with Python", Packt Publisher, 2017.

2.  Antonio Gulli, Sujit Pal, "Deep Learning with Keras", Packt Publishers, 2017.

3.  Francois Chollet, "Deep Learning with Python", Manning Publications, 2017.