Welcome to our next exciting lesson, where we introduce the basics of Dynamic Programming — a powerful method for solving optimization, combinatorics, and other complex problems. Dynamic Programming provides an approach to solve problems by breaking them down into subproblems, storing the results of certain calculations that are likely to be used again. This method helps to avoid repetitive calculations, thus enhancing the efficiency of algorithms.

One of the famous introductory problems solved using the Dynamic Programming method is the Fibonacci Series computation. Traditionally, if you have to compute the N-th Fibonacci number using recursion, you would solve smaller, overlapping subproblems multiple times, leading to exponential time complexity. However, by employing Dynamic Programming (as in the task mentioned above), you can store the result of each solved subproblem in a memoization table. The next time you require the result, you check the table first, thus avoiding re-computation and dramatically reducing the time complexity.

Here is what the solution may look like:

Python
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1def fibonacci(n, memo={}):
2    if n in memo:
3        return memo[n]
4    if n <= 1:
5        return n
6    memo[n] = fibonacci(n - 1, memo) + fibonacci(n - 2, memo)
7    return memo[n]
8
9# Test
10print(fibonacci(10))  # Output: 55

Remember, understanding Dynamic Programming requires a different mindset; it is all about recognizing and solving interrelated subproblems. Take a pause and let this approach sink in. Next up, we dive deep into practice problems to shape up your Dynamic Programming skills. We aim to provide you with the ability to simplify complex problems into smaller, manageable tasks to achieve efficient and effective solutions. Get ready to jump in!