Deep Learning: UNIT 4 : Autoencoders

UNIT IV

Autoencoders

1.     Denoising Autoencoders

2.     Sparse Autoencoders

3.     Deep Autoencoders

4.     Variational Autoencoders

5.     GANS 

👉Autoencoders Notes

👉Autoencoders PPTs

Deep Learning: UNIT-4 : Autoencoders Notes

UNIT IV 

Autoencoders 

1. Denoising Autoencoders 

2. Sparse Autoencoders 

3. Deep Autoencoders 

4. Variational Autoencoders 

5. GANS

  Reference: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, SECOND EDITION, O’Reilly

Deep Learning UNIT- 4 Autoencoders PPTs

UNIT IV 

Autoencoders 

1. Denoising Autoencoders 

2. Sparse Autoencoders 

3. Deep Autoencoders 

4. Variational Autoencoders 

5. GANS

 

Reference: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, SECOND EDITION, O’Reilly

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.

Deep Learning

Deep Learning  

👉Deep Learning Syllabus

• 👉Deep Learning: Fundamentals
• 👉CNN
• 👉RNN
• 👉Autoencoders
• 👉Transfer 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.

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

UNIT III

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

UNIT IV

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

UNIT V

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

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.

MCQs👇

1. Neural Network 1
   
2. Neural Network 2
3. Neural Network 3
4. Neural network 4
   
5. Neural Network 5
   
6. TensorFlow and Keras -1
   
7. TensorFlow and Keras-2
   
8. TensorFlow & keras 3
   
9. Convolutional Neural Network 1
   
10. Convolutional Neural Network 2