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An overview of various topics in Python programming, including matrix calculus, recurrence relations, and probability theory. It covers concepts such as list comprehension, list slicing, passing functions, and reading and writing files. The document also includes useful properties of matrices and calculus, as well as examples of coin payment problems and probability theory. It is intended for individuals interested in learning or expanding their knowledge of these topics.
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Roadmap Python Matrix Calculus Recurrence Relation Probability Theory
Gotchas
References
Useful Properties
”v − squared” = ‖v‖^22 = v · v = vT^ v (A + B)T^ = AT^ + BT (AB)T^ = BT^ AT
Matrix Calculus f (w) = (a · w + 1)^2
Compute ∇wf (w)
A Useful Quantity ∇ww>Cw = (C + C>)w
Matrix Calculus f (w) = ‖w‖^22
Compute ∇wf (w)
Roadmap Python Matrix Calculus Recurrence Relation Probability Theory
Coin Payment Problem
Suppose you have an unlimited supply of coins with values 2 and 3 cents How many ways can you pay for an item costing 8 cents?
Roadmap Python Matrix Calculus Recurrence Relation Probability Theory
Probability Law of total probability:
P(A) = ∑ n P(A ∩ Bn) = ∑ n P(Bn|A)P(Bn)
Bayes’ rule:
P(A|B) = P(B P|A(B)P)(A)
Random Variables Discrete: P(A = a) or pA(a) Continuous: P(A = a) fA(a) F (c) = P(A ≤ c) =^ ∫^ −∞c fA(a) da