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exercises/practice/knapsack/.docs/hints.md

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 - A good starting point is a brute-force recursive solution.
 You can see it sketched out in the first half of the article ["Demystifying the 0-1 knapsack problem: top solutions explained"](demystifying-the-knapsack-problem).
-- For a more efficient solution, you can improve your recursive solution using *dynamic programming*, which is introduced in the second half of [the above-mentioned article](demystifying-the-knapsack-problem).
+- For a more efficient solution, you can improve your recursive solution using _dynamic programming_, which is introduced in the second half of [the above-mentioned article](demystifying-the-knapsack-problem).
 For a more general explainer, see the video ["5 Simple Steps for Solving Dynamic Programming Problems"](solving-dynamic-programming-problems)
 - If you need a more visual walkthrough of how to apply dynamic programming to the knapsack problem, see the video ["0/1 Knapsack problem | Dynamic Programming"](0-1-knapsack-problem).
-Also worth mentioning is [this answer](intuition-of-dp-for-knapsack-problem) to a question on Reddit, *"What is the intuition behind Knapsack problem solution using dynamic programming?"*.
+Also worth mentioning is [this answer](intuition-of-dp-for-knapsack-problem) to a question on Reddit, _"What is the intuition behind Knapsack problem solution using dynamic programming?"_.
 Below is the answer in full.
 
 > The intuition behind the solution is basically like any dynamic programming solution: split the task into many sub-tasks, and save solutions to these sub-tasks for later use.