Single neuron still has a lot to say In the post of the first neural network tutorial, we studied a perceptron as a simple supervised learning machine. The perceptron is an amazing structure to understanding inference. In the post of the first neural network tutorial, I said I would leave you to find the objective function and and draw the plot of it. I just introduce here.

Continue reading

I assume that the readers know the Bayes’ rule already. If you are not familiar to it, read any kind of textbook about probability, data science, and machine learning. I recommend the book, which I learned Bayes’ rule. Bayesians say that you cannot do inference without making assumptions. Thus, Bayesians also use probabilities to describe inferences. The author in the chapter 2 introduces some rules of probability theory and introduces more about assumptions in inference in the chapter 3.

Continue reading

Author's picture

Namshik Kim

physicist, data scientist

Data Scientist

Vancouver, BC, Canada.