Basics Of Neural Nets

Leila Etaati has a new series on neural nets in R:

in Neural Network, we have some hidden Nodes that do the main job ! they found the best value for the output, they are using some function that we call that functions as “Activation function” for instance in below picture, Node C is a hidden node that take the values from node A and B. as you can see the weight (the better path) related to Node B as shown in tick line that means Node B may lead to get better results so Node C get input values from Node B not Node A.

If you have time, also check out the linked YouTube videos.

Related Posts

Beware Multi-Assignment dplyr::mutate() Statements

John Mount hits on an issue when using dplyr backed by a database in R: Notice the above gives an incorrect result: all of the x_i columns are identical, and all of the y_i columns are identical. I am not saying the above code is in any way desirable (though something like it does arise naturally in certain test […]

Read More

Markov Chains In Python

Sandipan Dey shows off various uses of Markov chains as well as how to create one in Python: Perspective. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today’s Information Age. In this paper, Shannon proposed using a Markov chain to […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930