vinod desai

Vinod Desai : Computer Graphics

OBJECT ORIENTED CONCEPTS OOC (18CS45) - Introduction and SYLLABUS
OBJECT ORIENTED CONCEPTS OOC (18CS45) - Introduction and SYLLABUS
COMPUTER GRAPHICS AND VISUALIZATION (18CS62) - Introduction and SYLLABUS
COMPUTER GRAPHICS AND VISUALIZATION (18CS62) - Introduction and SYLLABUS
Machine Learning LAB (17CSL76) video
Machine Learning LAB (17CSL76) video
Computer Organization - 18CS34 (module 5) Concepts: pipelining, Basic Concepts(4-stage pipeline)
Computer Organization - 18CS34 (module 5) Concepts: pipelining, Basic Concepts(4-stage pipeline)
Machine Learning (Module 5) : Basics of sampling theorem, Binomial Distribution
Machine Learning (Module 5) : Basics of sampling theorem, Binomial Distribution
Machine Learning (Module 5) : EVALUATING HYPOTHESES - MOTIVATION and ESTIMATING HYPOTHESIS ACCURACY
Machine Learning (Module 5) : EVALUATING HYPOTHESES - MOTIVATION and ESTIMATING HYPOTHESIS ACCURACY
Computer Organization - 18CS34 (module 5) Concepts: Micro Instructions
Computer Organization - 18CS34 (module 5) Concepts: Micro Instructions
Computer Organization - 18CS34 (module 5) Concepts: hardwired Control
Computer Organization - 18CS34 (module 5) Concepts: hardwired Control
Machine Learning (Module 4) BAYESIAN LEARNING: Bayesian Belief Networks
Machine Learning (Module 4) BAYESIAN LEARNING: Bayesian Belief Networks
Computer Organization - 18CS34 (module 5) Concepts: microprogrammed control
Computer Organization - 18CS34 (module 5) Concepts: microprogrammed control
Computer Organization - 18CS34 (module 5) Concepts: Hardwired control
Computer Organization - 18CS34 (module 5) Concepts: Hardwired control
Machine Learning (Module 4) BAYEIAN LEARNING: EM Algorithm
Machine Learning (Module 4) BAYEIAN LEARNING: EM Algorithm
Machine Learning (Module 4) BAYEIAN LEARNING: Naïve Bayes Classifier
Machine Learning (Module 4) BAYEIAN LEARNING: Naïve Bayes Classifier
ML - 17CS73 (Module 4) BAYEIAN LEARNING: Minimum Description Length Principal
ML - 17CS73 (Module 4) BAYEIAN LEARNING: Minimum Description Length Principal
CO - 18CS34 (module 3) Concepts: Hit Rate & Miss Penalty and Caches on the Processor Chip
CO - 18CS34 (module 3) Concepts: Hit Rate & Miss Penalty and Caches on the Processor Chip
ML(Module 4) BAYEIAN LEARNING: MAXIMUM LIKELIHOOD HYPOTHESES FOR PREDICTING PROBABILITIES
ML(Module 4) BAYEIAN LEARNING: MAXIMUM LIKELIHOOD HYPOTHESES FOR PREDICTING PROBABILITIES
CO - 18CS34 (module 3) Concepts: Replacement Algorithms and Performance Considerations
CO - 18CS34 (module 3) Concepts: Replacement Algorithms and Performance Considerations
Machine Learning (Module 4) BAYEIAN LEARNING: Normal Probability Distribution(Gaussian Distribution)
Machine Learning (Module 4) BAYEIAN LEARNING: Normal Probability Distribution(Gaussian Distribution)
Machine Learning (Module 4) BAYEIAN LEARNING: MAP Hypotheses and Consistent Learners
Machine Learning (Module 4) BAYEIAN LEARNING: MAP Hypotheses and Consistent Learners
Machine Learning (Module 4) BAYEIAN LEARNING - BAYES THEOREM & CONCEPT LEARNING
Machine Learning (Module 4) BAYEIAN LEARNING - BAYES THEOREM & CONCEPT LEARNING
CO - 18CS34 (module 3) Concepts: Cache Memories
CO - 18CS34 (module 3) Concepts: Cache Memories
Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - BAYES THEOREM and Example
Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - BAYES THEOREM and Example
Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - Introduction & Features of Bayesian Learning
Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - Introduction & Features of Bayesian Learning
CO - 18CS34 (module 3) Concepts: Asynchronous Memories (DRAM) and Synchronous DRAMs(SDRAM)
CO - 18CS34 (module 3) Concepts: Asynchronous Memories (DRAM) and Synchronous DRAMs(SDRAM)
CO (module 3) Concept:Semiconductor RAM Memories, Static Memories (SRAM)&Asynchronous Memories(DRAM)
CO (module 3) Concept:Semiconductor RAM Memories, Static Memories (SRAM)&Asynchronous Memories(DRAM)
Machine Learning - 17CS73 (Module 3) Concepts: REMARKS ON THE BACKPROPAGATION ALGORITHM
Machine Learning - 17CS73 (Module 3) Concepts: REMARKS ON THE BACKPROPAGATION ALGORITHM
Machine Learning 17CS73 (Module 3) Concepts: Derivation of the Back Propagation Rule
Machine Learning 17CS73 (Module 3) Concepts: Derivation of the Back Propagation Rule
Machine Learning (Module 3) Concepts: Multilayer Networks and Back-Propagation Algorithm
Machine Learning (Module 3) Concepts: Multilayer Networks and Back-Propagation Algorithm
Machine Learning (Module 3): Stochastic Approximation to Gradient Descent
Machine Learning (Module 3): Stochastic Approximation to Gradient Descent
Machine Learning (Module 3): Gradient Descent Rule and Gradient Descent Algorithm
Machine Learning (Module 3): Gradient Descent Rule and Gradient Descent Algorithm