Udemy - Recursion: the full course
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Files
| 0 | 0 B | ||
| 1. Conclusion.mp4 | 3.4 MB | ||
| 1. Conclusion.srt | 1 KB | ||
| 1. How to think recursively.mp4 | 14.5 MB | ||
| 1. How to think recursively.srt | 8 KB | ||
| 1. Process explanation.mp4 | 47.2 MB | ||
| 1. Process explanation.srt | 16.8 KB | ||
| 1. Recursion and linked lists.mp4 | 10 MB | ||
| 1. Recursion and linked lists.srt | 3.2 KB | ||
| 1. Recursion and timespace complexity.mp4 | 15.5 MB | ||
| 1. Recursion and timespace complexity.srt | 6.9 KB | ||
| 1. Solve the problem.html | 102 B | ||
| 1. The comparison.mp4 | 18.9 MB | ||
| 1. The comparison.srt | 6.4 KB | ||
| 1. Visualize call stack.mp4 | 22.5 MB | ||
| 1. Visualize call stack.srt | 5 KB | ||
| 1. What is divide-and-conquer.mp4 | 21.1 MB | ||
| 1. What is divide-and-conquer.srt | 7.8 KB | ||
| 1. What is double recursion.mp4 | 14.5 MB | ||
| 1. What is double recursion.srt | 5.7 KB | ||
| 1. What is memoization.mp4 | 9.6 MB | ||
| 1. What is memoization.srt | 2.8 KB | ||
| 1. What is recursion.mp4 | 37.1 MB | ||
| 1. What is recursion.srt | 15.9 KB | ||
| 1. What is tail recursion.mp4 | 20.2 MB | ||
| 1. What is tail recursion.srt | 8.9 KB | ||
| 1 | 483.5 KB | ||
| 1.1 ackermann.py | 102 B | ||
| 1.1 factorial.py | 102 B | ||
| 1.1 factorial_tail.py | 102 B | ||
| 1.1 linked_lists.py | 716 B | ||
| 1.1 merge.py | 614 B | ||
| 1.1 merge_callstack.py | 819 B | ||
| 1.1 ways.py | 204 B | ||
| 1.2 factorial.py | 102 B | ||
| 1.2 fibonacci_iter.py | 204 B | ||
| 1.2 karatsuba.py | 512 B | ||
| 1.3 binsearch.py | 307 B | ||
| 1.3 fibonacci.py | 102 B | ||
| 1.3 tow_rec_cases_calls.py | 204 B | ||
| 1.4 dfs_preorder.py | 204 B | ||
| 1.4 factorial_iter.py | 102 B | ||
| 1.5 merge.py | 614 B | ||
| 1.6 binsearch.py | 307 B | ||
| 1.7 bin_tree_sum.py | 307 B | ||
| 2. Code and execution.mp4 | 84.9 MB | ||
| 2. Code and execution.srt | 37.3 KB | ||
| 2. Examples.mp4 | 38.9 MB | ||
| 2. Examples.srt | 13.6 KB | ||
| 2 | 92.4 KB | ||
| 2. From recursion to iteration.mp4 | 37.4 MB | ||
| 2. From recursion to iteration.srt | 12.5 KB | ||
| 2. Optimize ways to climb stairs solution with memoization.mp4 | 23.6 MB | ||
| 2. Optimize ways to climb stairs solution with memoization.srt | 9.4 KB | ||
| 2. Recursion and trees.mp4 | 6.8 MB | ||
| 2. Recursion and trees.srt | 2.1 KB | ||
| 2. Recursion tree method.mp4 | 25.6 MB | ||
| 2. Recursion tree method.srt | 10.2 KB | ||
| 2. Recursion tree.mp4 | 30.2 MB | ||
| 2. Recursion tree.srt | 12.8 KB | ||
| 2. Solution + code.mp4 | 14.5 MB | ||
| 2. Solution + code.srt | 5 KB | ||
| 2. Visualize recursion tree.mp4 | 19.9 MB | ||
| 2. Visualize recursion tree.srt | 7.8 KB | ||
| 2. What is backtracking.mp4 | 35.1 MB | ||
| 2. What is backtracking.srt | 15.9 KB | ||
| 2.1 all_possible_phrases.py | 614 B | ||
| 2.1 array_permutations.py | 1 KB | ||
| 2.1 bin_tree_sum_iter.py | 614 B | ||
| 2.1 count_occurrences.py | 409 B | ||
| 2.1 factorial.py | 102 B | ||
| 2.1 has_adjacent_duplicates.py | 204 B | ||
| 2.1 keypad_combs.py | 819 B | ||
| 2.1 minimum_cost_path.py | 921 B | ||
| 2.1 reverse_string.py | 512 B | ||
| 2.1 string_subseq.py | 307 B | ||
| 2.1 sum_of_digits.py | 307 B | ||
| 2.1 sum_to_n.py | 204 B | ||
| 2.1 trees.py | 921 B | ||
| 2.1 valid_weight_combs.py | 819 B | ||
| 2.1 ways_memoiz.py | 307 B | ||
| 2.1 ways_rec_viz.py | 921 B | ||
| 2.1 word_search.py | 921 B | ||
| 2.2 dfs_postorder_iter.py | 512 B | ||
| 2.2 func_2.py | 102 B | ||
| 2.2 pow.py | 204 B | ||
| 2.3 fibonacci_tail.py | 307 B | ||
| 2.3 file_system.py | 512 B | ||
| 2.4 func_1.py | 102 B | ||
| 2.4 get_min_tail.py | 307 B | ||
| 2.5 fibonacci_iter.py | 204 B | ||
| 2.5 merge.py | 614 B | ||
| 2.6 hanoi.py | 307 B | ||
| 2.7 binsearch.py | 307 B | ||
| 2.8 fibonacci.py | 102 B | ||
| 3. Base cases and recursive cases.mp4 | 16.1 MB | ||
| 3. Base cases and recursive cases.srt | 6.9 KB | ||
| 3. From iteration to recursion.mp4 | 17.1 MB | ||
| 3. From iteration to recursion.srt | 6.2 KB | ||
| 3. N-queens problem.mp4 | 43.6 MB | ||
| 3. N-queens problem.srt | 17.4 KB | ||
| 3. Recurrence relation method.mp4 | 39.6 MB | ||
| 3. Recurrence relation method.srt | 12.9 KB | ||
| 3. Recursion and graphs.mp4 | 6.1 MB | ||
| 3. Recursion and graphs.srt | 1.9 KB | ||
| 3. What is dynamic programming.mp4 | 18.8 MB | ||
| 3. What is dynamic programming.srt | 6.3 KB | ||
| 3.1 fibonacci_dp.py | 102 B | ||
| 3.1 get_min.py | 307 B | ||
| 3.1 graphs.py | 409 B | ||
| 3.1 nqueens.py | 716 B | ||
| 3.2 contains.py | 307 B | ||
| 3.3 nb_divisors.py | 716 B | ||
| 4. Master theorem method.mp4 | 58.6 MB | ||
| 4. Master theorem method.srt | 13.3 KB | ||
| 4. Optimize ways to climb stairs solution with dynamic programming.mp4 | 17.2 MB | ||
| 4. Optimize ways to climb stairs solution with dynamic programming.srt | 5.1 KB | ||
| 4.1 ways_dp.py | 204 B | ||
| 5. Space complexity of a recursive algorithm.mp4 | 24.4 MB | ||
| 5. Space complexity of a recursive algorithm.srt | 9.7 KB | ||
| TutsNode.com.txt | 102 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614 B | ||
| 3 | 352.1 KB | ||
| 4 | 365.8 KB | ||
| 5 | 412.3 KB | ||
| 6 | 377.7 KB | ||
| 7 | 358.3 KB | ||
| 8 | 395.3 KB | ||
| 9 | 381.7 KB | ||
| 10 | 409.6 KB | ||
| 11 | 60.2 KB | ||
| 12 | 134.5 KB | ||
| 13 | 431.4 KB | ||
| 14 | 385.1 KB | ||
| 15 | 63.3 KB | ||
| 16 | 312.4 KB | ||
| 17 | 26.3 KB | ||
| 18 | 443.1 KB | ||
| 19 | 134.6 KB | ||
| 20 | 409.6 KB | ||
| 21 | 31.5 KB | ||
| 22 | 37.7 KB | ||
| 23 | 365.4 KB | ||
| 24 | 241.7 KB | ||
| 25 | 329.5 KB | ||
| 26 | 113 KB | ||
| 27 | 61.8 KB | ||
| 28 | 160.1 KB | ||
| 29 | 266.8 KB | ||
| 30 | 380.8 KB | ||
| 31 | 399.5 KB | ||
| 32 | 45.6 KB | ||
| 33 | 473.8 KB | ||
| 34 | 1.8 KB | ||
| 35 | 14.1 KB | ||
| 36 | 8.5 KB | ||
| 37 | 453 KB | ||
| 38 | 228.8 KB | ||
| 39 | 390.4 KB | ||
| ▲ 189 total files | |||
Description
Description
Even if the concept of recursion is simple, a lot of people struggle with it (not understanding the recursive process, not being able to figure out the base cases and recursive cases…), this is why I wanted to create a full course on recursion that covers all what you need to know about it, it also contains 11 solved and explained coding problems to practice.
And knowing recursion will also give you a new way of thinking, which is dividing the problem into subproblems of the same type, which is necessary to understand techniques like dynamic programming, backtracking…
See you in the first lecture!
The course covers:
What is recursion
Code and execution
Base cases and recursive cases
Multiple recursive calls process
Call stack
Recursion tree
How to visualize the process
Recursive functions complexity analysis (time and space comp)
Recursion vs Iteration
How to optimize a recursive function (memoization and dynamic programming)
Divide-and-conquer
Backtracking
Recursive data structures
Tail recursion
Double recursion
How to think recursively
Plus 11 solved and explained coding problems to practice:
Sum of digits
Count occurrences
Has adjacent duplicates
Reverse string
Minimum cost path in matrix
All possible phrases
Keypad combinations
String subsequences
Binary numbers with at most 2 zeros
Word search
Array permutations
Who this course is for:
Programmers
Computer science students
Engineering students
Competitive programmers
Self-learning people
Requirements
Basic programming knowledge
Last Updated 12/2020
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