Machine Learning - Deep Learning

  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 Intell...

UNIT V  Transfer Learning  Types  Methodologies  Diving into Transfer Learning  Challenges  

  UNIT V  Transfer Learning  1. Types  2. Methodologies  3. Diving into Transfer Learning  4. Challenges 👉 Transfer learning Notes 👉 Transfer Learning PPTs

UNIT IV Autoencoders 1.      Denoising Autoencoders 2.      Sparse Autoencoders 3.      Deep Autoencoders 4.      Variational Autoencoders 5.      GANS   👉 Autoencoders Notes 👉 Autoencoders PPTs

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

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

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