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Showing posts from September, 2018

BAYESIAN NETWORKS in Machine Learning

So basically this topic is a mix of three theories namely: Graph Theory Probability Theory Bayes Theory 1. Graph Theory  Graph theory is a mathematical field for the study of Graphs. Graph = Nodes (or vertices) + Arcs (or lines, or edges!) So a node can be anything that you want, a company, profits or a band. The thing is- you can have many nodes- but they have to have some form of relation between them. Graphs can be directed and undirected as shown below. 2. Probability Theory Probability is a measure of the likelihood of something happening. And is always a value between 0 and 1. The more likely, the closer the value is to 1 and vs-versa. Probability = (Particular Event)➗(All Possible Events) An example is coin flip. The probability of getting heads is 0.50 - and this brings us to our next idea, conditional probability . Instead of one event at a time, what is the the probability of getting a heads twice? Which then becomes P(Heads|Heads) The ' | ' sim

ANDROID: support libraries must use the exact same version specification

Here's a way you can tackle this annoying issue. Click on the Gradle side pane, select Tasks->android->androidDependencies. Running it will generate a list of the dependencies and the versions and a quick scroll should help you! Pic below should help