Все публикации

ML - 17CS730:11:35

ML - 17CS73 (Module 4) BAYEIAN LEARNING: Minimum Description Length Principal

CO - 18CS340:10:45

CO - 18CS34 (module 3) Concepts: Hit Rate & Miss Penalty and Caches on the Processor Chip

ML(Module 4) BAYEIAN0:19:15

ML(Module 4) BAYEIAN LEARNING: MAXIMUM LIKELIHOOD HYPOTHESES FOR PREDICTING PROBABILITIES

CO - 18CS340:14:16

CO - 18CS34 (module 3) Concepts: Replacement Algorithms and Performance Considerations

Machine Learning (Module0:15:07

Machine Learning (Module 4) BAYEIAN LEARNING: Normal Probability Distribution(Gaussian Distribution)

Machine Learning (Module0:16:25

Machine Learning (Module 4) BAYEIAN LEARNING: MAP Hypotheses and Consistent Learners

CO - 18CS340:11:59

CO - 18CS34 (module 3) Concepts: Cache Memories

Machine Learning -0:17:37

Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - BAYES THEOREM and Example

Machine Learning -0:11:47

Machine Learning - 17CS73 (Module 4) BAYEIAN LEARNING - Introduction & Features of Bayesian Learning

CO - 18CS340:15:27

CO - 18CS34 (module 3) Concepts: Asynchronous Memories (DRAM) and Synchronous DRAMs(SDRAM)

CO (module 3)0:23:22

CO (module 3) Concept:Semiconductor RAM Memories, Static Memories (SRAM)&Asynchronous Memories(DRAM)

Machine Learning -0:14:14

Machine Learning - 17CS73 (Module 3) Concepts: REMARKS ON THE BACKPROPAGATION ALGORITHM

Machine Learning 17CS730:15:52

Machine Learning 17CS73 (Module 3) Concepts: Derivation of the Back Propagation Rule

Machine Learning (Module0:19:06

Machine Learning (Module 3) Concepts: Multilayer Networks and Back-Propagation Algorithm

Machine Learning (Module0:14:19

Machine Learning (Module 3): Stochastic Approximation to Gradient Descent

Machine Learning (Module0:12:19

Machine Learning (Module 3): Gradient Descent Rule and Gradient Descent Algorithm

CO - 18CS340:16:14

CO - 18CS34 ( module 2) Concepts: USB Addressing and USB Protocols

CO ( module0:29:53

CO ( module 2) Concepts: USB and USB Architecture

Machine Learning (Module0:12:17

Machine Learning (Module 3): Gradient Descent and Delta Rule, Visualizing the Hypothesis Space

Machine Learning (Mod3):PERCEPTRONS,0:16:48

Machine Learning (Mod3):PERCEPTRONS, Representational Power of Perceptrons, Perceptron Training Rule

CO ( module0:18:02

CO ( module 2) Concepts: Output Interface Circuit, Combined I/O interface circuit.

Machine Learning (Module0:16:02

Machine Learning (Module 3)- Concepts: Architectures of Artificial Neural Networks, PERCEPTRONS

Machine Learning (Module0:09:08

Machine Learning (Module 3)- Concepts: 'APPROPRIATE PROBLEMS FOR NEURAL NETWORK LEARNING'

Machine Learning (Module0:11:43

Machine Learning (Module 3)- Concepts: NEURAL NETWORK REPRESENTATIONS