LeetCode Learning Journal

Reference

• Coding Interview University
• Huahua’s Tech Road

Algorithmic complexity / Big-O / Asymptotic analysis

• Harvard CS50 - Asymptotic Notation (video)
• Big O Notations (general quick tutorial) (video)
• UC Berkeley Big O (video)
• Amortized Analysis (video)
• TopCoder (includes recurrence relations and master theorem):
  • Computational Complexity: Section 1
  • Computational Complexity: Section 2
• Cheat sheet

Data Structures

• Arrays

  • About Arrays:
    • Arrays (video)
    • UC Berkeley CS61B - Linear and Multi-Dim Arrays (video) (Start watching from 15m 32s)
    • Dynamic Arrays (video)
    • Jagged Arrays (video)

• LinkedList

  • Description:
    • Singly Linked Lists (video)
    • CS 61B - Linked Lists 1 (video)
    • CS 61B - Linked Lists 2 (video)
  • C Code (video) - not the whole video, just portions about Node struct and memory allocation
  • Linked List vs Arrays:
    • Core Linked Lists Vs Arrays (video)
    • In The Real World Linked Lists Vs Arrays (video)
  • why you should avoid linked lists (video)
  • Gotcha: you need pointer to pointer knowledge: (for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
    • Pointers to Pointers
  • LeetCode:
    • Q2
    • Q24
    • Q206
    • Q141
    • Q23

• Stack

  • Stacks (video)
  • Will not implement. Implementing with array is trivial
  • LeetCode:
    • Q1003
    • Q496
    • Q1019
    • Q1063
    • Q907
    • Q2104

• Queue

  • Queue (video)
  • Circular buffer/FIFO
  • Implement using linked-list, with tail pointer:
    • enqueue(value) - adds value at position at tail
    • dequeue() - returns value and removes least recently added element (front)
    • empty()
    • LeetCode:
      • Q1424
      • Q1438
      • Q239
      • Q1499
      • Q862
      • Q1425

• Hash table

  • Videos:

    • Hashing with Chaining (video)
    • Table Doubling, Karp-Rabin (video)
    • Open Addressing, Cryptographic Hashing (video)
    • PyCon 2010: The Mighty Dictionary (video)
    • PyCon 2017: The Dictionary Even Mightier (video)
    • (Advanced) Randomization: Universal & Perfect Hashing (video)
    • (Advanced) Perfect hashing (video)

  • Online Courses:

    • Core Hash Tables (video)
    • Data Structures (video)
    • Phone Book Problem (video)
    • distributed hash tables:
      • Instant Uploads And Storage Optimization In Dropbox (video)
      • Distributed Hash Tables (video)

  • Implement with array using linear probing

    • hash(k, m) - m is size of hash table
    • add(key, value) - if key already exists, update value
    • exists(key)
    • get(key)
    • remove(key)

  • LeetCode:

    • Q482
    • Q904
    • Q929
    • Q3
    • Q11
    • Q15
    • Q31
    • Q43
    • Q48

More Knowledge

• Binary search

  • Binary Search (video)
  • Binary Search (video)
  • detail
  • blueprint
  • Implement:
    • binary search (on sorted array of integers)
    • binary search using recursion
  • LeetCode
    • "As for the question "When can we use binary search?", my answer is that, If we can discover some kind of monotonicity, for example, if condition(k) is True then condition(k + 1) is True, then we can consider binary search."
    • Q278
    • Q69
    • Q35
    • Q1011
    • Q875
    • Q1482
    • Q1201
    • Q1283

• Bitwise operations

  • Bits cheat sheet - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
  • Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
    • words
    • Good intro: Bit Manipulation (video)
    • C Programming Tutorial 2-10: Bitwise Operators (video)
    • Bit Manipulation
    • Bitwise Operation
    • Bithacks
    • The Bit Twiddler
    • The Bit Twiddler Interactive
    • Bit Hacks (video)
    • Practice Operations
  • 2s and 1s complement
    • Binary: Plusses & Minuses (Why We Use Two's Complement) (video)
    • 1s Complement
    • 2s Complement
  • Count set bits
    • 4 ways to count bits in a byte (video)
    • Count Bits
    • How To Count The Number Of Set Bits In a 32 Bit Integer
  • Swap values:
    • Swap
  • Absolute value:
    • Absolute Integer

Trees

• Trees - Notes & Background

  • Series: Trees (video)
  • basic tree construction
  • traversal
  • manipulation algorithms
  • BFS(breadth-first search) and DFS(depth-first search) (video)
    • BFS notes:
      • level order (BFS, using queue)
      • time complexity: O(n)
      • space complexity: best: O(1), worst: O(n/2)=O(n)
    • DFS notes:
      • time complexity: O(n)
      • space complexity: best: O(log n) - avg. height of tree worst: O(n)
      • inorder (DFS: left, self, right)
      • postorder (DFS: left, right, self)
      • preorder (DFS: self, left, right)
  • LeetCode
    • Q94
    • Q100
    • Q101
    • Q102
    • Q144
    • Q145
    • Q226
    • Q572
    • Q589
    • Q617
    • Q669

• Binary search trees: BSTs

  • Binary Search Tree Review (video)
  • Introduction (video)
  • MIT (video)
  • C/C++:
    • Binary search tree - Implementation in C/C++ (video)
    • BST implementation - memory allocation in stack and heap (video)
    • Find min and max element in a binary search tree (video)
    • Find height of a binary tree (video)
    • Binary tree traversal - breadth-first and depth-first strategies (video)
    • Binary tree: Level Order Traversal (video)
    • Binary tree traversal: Preorder, Inorder, Postorder (video)
    • Check if a binary tree is binary search tree or not (video)
    • Delete a node from Binary Search Tree (video)
    • Inorder Successor in a binary search tree (video)
  • Implement:
    • insert // insert value into tree
    • get_node_count // get count of values stored
    • print_values // prints the values in the tree, from min to max
    • delete_tree
    • is_in_tree // returns true if given value exists in the tree
    • get_height // returns the height in nodes (single node's height is 1)
    • get_min // returns the minimum value stored in the tree
    • get_max // returns the maximum value stored in the tree
    • is_binary_search_tree
    • delete_value
    • get_successor // returns next-highest value in tree after given value, -1 if none
  • LeetCode
    • Q98
    • Q700
    • Q230
    • Q99
    • Q108

• Heap / Priority Queue / Binary Heap

  • visualized as a tree, but is usually linear in storage (array, linked list)

  • Heap

  • Introduction (video)

  • Naive Implementations (video)

  • Binary Trees (video)

  • Tree Height Remark (video)

  • Basic Operations (video)

  • Complete Binary Trees (video)

  • Pseudocode (video)

  • Heap Sort - jumps to start (video)

  • Heap Sort (video)

  • Building a heap (video)

  • MIT: Heaps and Heap Sort (video)

  • CS 61B Lecture 24: Priority Queues (video)

  • Linear Time BuildHeap (max-heap)

  • Implement a max-heap:

    • insert
    • sift_up - needed for insert
    • get_max - returns the max item, without removing it
    • get_size() - return number of elements stored
    • is_empty() - returns true if heap contains no elements
    • extract_max - returns the max item, removing it
    • sift_down - needed for extract_max
    • remove(x) - removes item at index x
    • heapify - create a heap from an array of elements, needed for heap_sort
    • heap_sort() - take an unsorted array and turn it into a sorted array in-place using a max heap or min heap

  • LeetCode

    heapq  
    ------  
    - heapq.heapify(x:list) -> min-heap  
    - heapq.heappush(heap, item)  
    - heapq.heappop(heap)  
    

    • Q1046
    • Q1057
    • Q1642
    • Q1792
    • Q1882

Sorting

• Notes:

  • Implement sorts & know best case/worst case, average complexity of each:
    • no bubble sort - it's terrible - O(n^2), except when n <= 16
  • Stability in sorting algorithms ("Is Quicksort stable?")
    • Sorting Algorithm Stability
    • Stability In Sorting Algorithms
    • Stability In Sorting Algorithms
    • Sorting Algorithms - Stability
  • Which algorithms can be used on linked lists? Which on arrays? Which on both?
    • I wouldn't recommend sorting a linked list, but merge sort is doable.
    • Merge Sort For Linked List

• For heapsort, see Heap data structure above. Heap sort is great, but not stable

• Sedgewick - Mergesort (5 videos)

  • 1. Mergesort
  • 2. Bottom up Mergesort
  • 3. Sorting Complexity
  • 4. Comparators
  • 5. Stability

• Sedgewick - Quicksort (4 videos)

  • 1. Quicksort
  • 2. Selection
  • 3. Duplicate Keys
  • 4. System Sorts

• UC Berkeley:

  • CS 61B Lecture 29: Sorting I (video)
  • CS 61B Lecture 30: Sorting II (video)
  • CS 61B Lecture 32: Sorting III (video)
  • CS 61B Lecture 33: Sorting V (video)

• Bubble Sort (video)

• Analyzing Bubble Sort (video)

• Insertion Sort, Merge Sort (video)

• Insertion Sort (video)

• Merge Sort (video)

• Quicksort (video)

• Selection Sort (video)

• Merge sort code:

  • Using output array (C)
  • Using output array (Python)
  • In-place (C++)

• Quick sort code:

  • Implementation (C)
  • Implementation (C)
  • Implementation (Python)

• Implement:

  • Mergesort: O(n log n) average and worst case
  • Quicksort O(n log n) average case
  • Selection sort and insertion sort are both O(n^2) average and worst case
  • For heapsort, see Heap data structure above

• Not required, but I recommended them:

  • Sedgewick - Radix Sorts (6 videos)
    • 1. Strings in Java
    • 2. Key Indexed Counting
    • 3. Least Significant Digit First String Radix Sort
    • 4. Most Significant Digit First String Radix Sort
    • 5. 3 Way Radix Quicksort
    • 6. Suffix Arrays
  • Radix Sort
  • Radix Sort (video)
  • Radix Sort, Counting Sort (linear time given constraints) (video)
  • Randomization: Matrix Multiply, Quicksort, Freivalds' algorithm (video)
  • Sorting in Linear Time (video)

As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects

Graphs

Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.

• Notes:

  • There are 4 basic ways to represent a graph in memory:
    • objects and pointers
    • adjacency matrix
    • adjacency list
    • adjacency map
  • Familiarize yourself with each representation and its pros & cons
  • BFS and DFS - know their computational complexity, their trade offs, and how to implement them in real code
  • When asked a question, look for a graph-based solution first, then move on if none

• MIT(videos):

  • Breadth-First Search
  • Depth-First Search

• Skiena Lectures - great intro:

  • CSE373 2020 - Lecture 10 - Graph Data Structures (video)
  • CSE373 2020 - Lecture 11 - Graph Traversal (video)
  • CSE373 2020 - Lecture 12 - Depth First Search (video)
  • CSE373 2020 - Lecture 13 - Minimum Spanning Trees (video)
  • CSE373 2020 - Lecture 14 - Minimum Spanning Trees (con't) (video)
  • CSE373 2020 - Lecture 15 - Graph Algorithms (con't 2) (video)

• Graphs (review and more):

  • 6.006 Single-Source Shortest Paths Problem (video)
  • 6.006 Dijkstra (video)
  • 6.006 Bellman-Ford (video)
  • 6.006 Speeding Up Dijkstra (video)
  • Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (video)
  • Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)
  • Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (video)
  • Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (video)
  • CS 61B 2014: Weighted graphs (video)
  • Greedy Algorithms: Minimum Spanning Tree (video)
  • Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)

• Full Coursera Course:

  • Algorithms on Graphs (video)

• I'll implement:

  • DFS with adjacency list (recursive)
  • DFS with adjacency list (iterative with stack)
  • DFS with adjacency matrix (recursive)
  • DFS with adjacency matrix (iterative with stack)
  • BFS with adjacency list
  • BFS with adjacency matrix
  • single-source shortest path (Dijkstra)
  • minimum spanning tree
  • DFS-based algorithms (see Aduni videos above):
    • check for cycle (needed for topological sort, since we'll check for cycle before starting)
    • topological sort
    • count connected components in a graph
    • list strongly connected components
    • check for bipartite graph

• LeetCode

  • []
  • []
  • []
  • []
  • []

Even More Knowledge

• Recursion

  • Stanford lectures on recursion & backtracking:
    • Lecture 8 | Programming Abstractions (video)
    • Lecture 9 | Programming Abstractions (video)
    • Lecture 10 | Programming Abstractions (video)
    • Lecture 11 | Programming Abstractions (video)
  • When it is appropriate to use it?
  • How is tail recursion better than not?
    • What Is Tail Recursion Why Is It So Bad?
    • Tail Recursion (video)
  • 5 Simple Steps for Solving Any Recursive Problem(video)

  Backtracking Blueprint: Java Python

• Dynamic Programming

  • You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a problem as being a candidate for dynamic programming.
  • This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
  • I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
  • Videos:
    • Skiena: CSE373 2020 - Lecture 19 - Introduction to Dynamic Programming (video)
    • Skiena: CSE373 2020 - Lecture 20 - Edit Distance (video)
    • Skiena: CSE373 2020 - Lecture 20 - Edit Distance (continued) (video)
    • Skiena: CSE373 2020 - Lecture 21 - Dynamic Programming (video)
    • Skiena: CSE373 2020 - Lecture 21 - Dynamic Programming and Review (video)
    • Simonson: Dynamic Programming 0 (starts at 59:18) (video)
    • Simonson: Dynamic Programming I - Lecture 11 (video)
    • Simonson: Dynamic programming II - Lecture 12 (video)
    • List of individual DP problems (each is short): Dynamic Programming (video)
  • Yale Lecture notes:
    • Dynamic Programming
  • Coursera:
    • The RNA secondary structure problem (video)
    • A dynamic programming algorithm (video)
    • Illustrating the DP algorithm (video)
    • Running time of the DP algorithm (video)
    • DP vs. recursive implementation (video)
    • Global pairwise sequence alignment (video)
    • Local pairwise sequence alignment (video)

• Design patterns

  • Quick UML review (video)
  • Learn these patterns:
    • strategy
    • singleton
    • adapter
    • prototype
    • decorator
    • visitor
    • factory, abstract factory
    • facade
    • observer
    • proxy
    • delegate
    • command
    • state
    • memento
    • iterator
    • composite
    • flyweight
  • Series of videos (27 videos)
  • Book: Head First Design Patterns
    • I know the canonical book is "Design Patterns: Elements of Reusable Object-Oriented Software", but Head First is great for beginners to OO.
  • Handy reference: 101 Design Patterns & Tips for Developers

• Combinatorics (n choose k) & Probability

  • Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)
  • Make School: Probability (video)
  • Make School: More Probability and Markov Chains (video)
  • Khan Academy:
    • Course layout:
      • Basic Theoretical Probability
    • Just the videos - 41 (each are simple and each are short):
      • Probability Explained (video)

• NP, NP-Complete and Approximation Algorithms

  • Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
  • Know what NP-complete means.
  • Computational Complexity (video)
  • Simonson:
    • Greedy Algs. II & Intro to NP Completeness (video)
    • NP Completeness II & Reductions (video)
    • NP Completeness III (Video)
    • NP Completeness IV (video)
  • Skiena:
    • CSE373 2020 - Lecture 23 - NP-Completeness (video)
    • CSE373 2020 - Lecture 24 - Satisfiability (video)
    • CSE373 2020 - Lecture 25 - More NP-Completeness (video)
    • CSE373 2020 - Lecture 26 - NP-Completeness Challenge (video)
  • Complexity: P, NP, NP-completeness, Reductions (video)
  • Complexity: Approximation Algorithms (video)
  • Complexity: Fixed-Parameter Algorithms (video)
  • Peter Norvig discusses near-optimal solutions to traveling salesman problem:
    • Jupyter Notebook
  • Pages 1048 - 1140 in CLRS if you have it.

• How computers process a program

  • How CPU executes a program (video)
  • How computers calculate - ALU (video)
  • Registers and RAM (video)
  • The Central Processing Unit (CPU) (video)
  • Instructions and Programs (video)

• Caches

  • LRU cache:
    • The Magic of LRU Cache (100 Days of Google Dev) (video)
    • Implementing LRU (video)
    • LeetCode - 146 LRU Cache (C++) (video)
  • CPU cache:
    • MIT 6.004 L15: The Memory Hierarchy (video)
    • MIT 6.004 L16: Cache Issues (video)

• Processes and Threads

  • Computer Science 162 - Operating Systems (25 videos):
    • for processes and threads see videos 1-11
    • Operating Systems and System Programming (video)
  • What Is The Difference Between A Process And A Thread?
  • Covers:
    • Processes, Threads, Concurrency issues
      • Difference between processes and threads
      • Processes
      • Threads
      • Locks
      • Mutexes
      • Semaphores
      • Monitors
      • How they work?
      • Deadlock
      • Livelock
    • CPU activity, interrupts, context switching
    • Modern concurrency constructs with multicore processors
    • Paging, segmentation and virtual memory (video)
    • Interrupts (video)
    • Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
    • Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
    • Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
    • Context switching
      • How context switching is initiated by the operating system and underlying hardware?
  • threads in C++ (series - 10 videos)
  • CS 377 Spring '14: Operating Systems from University of Massachusetts
  • concurrency in Python (videos):
    • Short series on threads
    • Python Threads
    • Understanding the Python GIL (2010)
      • reference
    • David Beazley - Python Concurrency From the Ground Up: LIVE! - PyCon 2015
    • Keynote David Beazley - Topics of Interest (Python Asyncio)
    • Mutex in Python

• Testing

  • To cover:
    • how unit testing works
    • what are mock objects
    • what is integration testing
    • what is dependency injection
  • Agile Software Testing with James Bach (video)
  • Open Lecture by James Bach on Software Testing (video)
  • Steve Freeman - Test-Driven Development (that’s not what we meant) (video)
    • slides
  • Dependency injection:
    • video
    • Tao Of Testing
  • How to write tests

• String searching & manipulations

  • Sedgewick - Suffix Arrays (video)
  • Sedgewick - Substring Search (videos)
    • 1. Introduction to Substring Search
    • 2. Brute-Force Substring Search
    • 3. Knuth-Morris Pratt
    • 4. Boyer-Moore
    • 5. Rabin-Karp
  • Search pattern in text (video)

  If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects.

• Tries

  • Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits to track the path
  • I read through code, but will not implement
  • Sedgewick - Tries (3 videos)
    • 1. R Way Tries
    • 2. Ternary Search Tries
    • 3. Character Based Operations
  • Notes on Data Structures and Programming Techniques
  • Short course videos:
    • Introduction To Tries (video)
    • Performance Of Tries (video)
    • Implementing A Trie (video)
  • The Trie: A Neglected Data Structure
  • TopCoder - Using Tries
  • Stanford Lecture (real world use case) (video)
  • MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)

• Floating Point Numbers

  • simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)

• Unicode

  • The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
  • What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text

• Endianness

  • Big And Little Endian
  • Big Endian Vs Little Endian (video)
  • Big And Little Endian Inside/Out (video)
    • Very technical talk for kernel devs. Don't worry if most is over your head.
    • The first half is enough.

• Networking

  • if you have networking experience or want to be a reliability engineer or operations engineer, expect questions
  • Otherwise, this is just good to know
  • Khan Academy
  • UDP and TCP: Comparison of Transport Protocols (video)
  • TCP/IP and the OSI Model Explained! (video)
  • Packet Transmission across the Internet. Networking & TCP/IP tutorial. (video)
  • HTTP (video)
  • SSL and HTTPS (video)
  • SSL/TLS (video)
  • HTTP 2.0 (video)
  • Video Series (21 videos) (video)
  • Subnetting Demystified - Part 5 CIDR Notation (video)
  • Sockets:
    • Java - Sockets - Introduction (video)
    • Socket Programming (video)

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