Record 1 learning
2, in your own words sum up "gradient" "gradient descent" and "Bayesian"
Gradient: changes the specified direction value per unit distance
Gradient descent: solve a problem is simply time to find his optimal solution, it could be a local optimum
Bayes' theorem: p (A | B) = P (A) x [p (B | A) / p (B)]