Archives for bayesian inference
21
Dec
7 Must-Know Algorithms in ML


Algorithms for training machine learning models, including neural networks, Bayesian inference, and probabilistic inference
The post 7 Must-Know Algorithms in ML appeared first on Analytics India Magazine.
21
Dec
7 Must-Know Algorithms in ML


Algorithms for training machine learning models, including neural networks, Bayesian inference, and probabilistic inference
The post 7 Must-Know Algorithms in ML appeared first on Analytics India Magazine.
Markov Chain Monte Carlo (MCMC) refers to a class of methods for sampling from a probability distribution to construct the most likely distribution. Logistic distribution cannot be directly calculated, so instead generates thousands of values preferred as samples for the parameters of the function to create an approximation of the distribution.


The Bayesian approach is used to analyze the data and update the beliefs based on data. Monte Carlo Markov Chain is a method that stimulates high dimensional probability distribution for Bayesian inference.


According to Frequentists, probability is related to the frequency of repeated events, whereas Bayesian thinks of probability as a measure of uncertainty.


Applying bayesian on neural networks is a method of controlling overfitting. We can also apply bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN.


Applying bayesian on neural networks is a method of controlling overfitting. We can also apply bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN.
22
Mar
If you want an AI job in India, don’t focus on computer vision: Avisek Lahiri, Google researcher


I would recommend people to focus on graph neural networks.


traditional MMS are not much eligible to equip the hard data with prior knowledge. The simple models are defined with the parameters which are independent of each other. Bayesian MMMs can be eligible to deal with such hard data.


traditional MMS are not much eligible to equip the hard data with prior knowledge. The simple models are defined with the parameters which are independent of each other. Bayesian MMMs can be eligible to deal with such hard data.