About Author

 

Dr. Rambabu Pemula

Dr. Rambabu Pemula received his B.Tech. Degree in Computer Science and Engineering and M.Tech. Degree in Software Engineering from J.N.T.U, Hyderabad and awarded Ph.D. in the area of Digital Image Processing in the Department of Computer Science and Engineering from J.N.T. University Kakinada. He is currently working as Associate Professor in the Department of Artificial Intelligence, Vidya Jyothi Institute of Technology, Hyderabad, Telangana. He qualified GATE, UGC NET and AP SET in Computer Science and Applications. He published twenty research articles in UGC referred journals, SCI and Scopus indexed journals. He participated and presented research papers at different International and National conferences.  He has done different certificate courses from NPTEL and Coursera on AI, Machine Learning, Deep Learning and Computer Vision. His research area includes Machine Learning and Computer Vision. He completed MS in Computer Science in Artificial Intelligence and Machine Learning from Scaler Neovarsity a full constituent college of Woolf University.

 

Dr. Rambabu Pemula

Associate Professor

Department of Artificial Intelligence

Vidya Jyothi Institute of Technology

Hyderabad

rpemula@gmail.com

Deep Learning - UNIT 5 : Transfer Learning PPTs

UNIT V 

Transfer Learning 

1. Types 
2. Methodologies 
3. Diving into Transfer Learning 
4. Challenges

 

Deep Learning : UNIT 5 : Transfer Learning

 

UNIT V 

Transfer Learning 

1. Types 

2. Methodologies 

3. Diving into Transfer Learning 

4. Challenges

👉Transfer learning Notes

👉Transfer Learning PPTs

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 5: Transfer Learning Notes

UNIT V 

Transfer Learning 

1. Types 

2. Methodologies 

3. Diving into Transfer Learning 

4. Challenges

  Reference: Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021

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