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GitHub - yukina1418/javascript-algorithms: 📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings

coding-interview-university/README-ko.md at main · jwasham/coding-interview-university · GitHub

알고리즘 복잡도 / Big-O / 점근적 분석¶

• Harvard CS50 - Asymptotic Notation (video)
• Big O Notations (general quick tutorial) (video)
• Big O Notation (and Omega and Theta) - best mathematical explanation (video)
• Skiena:
• 동영상
• 슬라이드 자료
• A Gentle Introduction to Algorithm Complexity Analysis
• Orders of Growth (video)
• Asymptotics (video)
• UC Berkeley Big O (video)
• UC Berkeley Big Omega (video)
• Amortized Analysis (video)
• Illustrating “Big O” (video)
• TopCoder (includes recurrence relations and master theorem):
• Computational Complexity: Section 1
• Computational Complexity: Section 2
• Cheat sheet

자료구조¶

배열¶

• 자동 리사이징 벡터 구현하기
• 설명:
  • 배열 (영상)
  • UCBerkley CS61B - 선형과 다차원 배열 (영상)
  • 동적 배열 (영상)
  • 가변 배열 (영상)
• 벡터 구현하기 (자동 리사이징을 포함한 동적 배열):
  • 배열, 포인터 및 인덱싱 대신하여 특정 인덱스에 접근하는 포인터 연산을 통한 코딩 연습
  • 메모리 할당을 포함한 새 배열
  • 배열 메소드 등의 기능을 활용하지 않으면서 정수 배열에 메모리를 할당할 수 있어야 함
  • 16으로 시작하거나 시작하는 숫자가 크다면 2의 제곱수(16, 32, 64, 128)로 시작
  • size() - 항목의 개수
  • capacity() - 들어갈 수 있는 항목의 최대 개수
  • is_empty()
  • at(index) - 인덱스에 있는 항목을 돌려주고, 인덱스가 범위 밖이면 에러를 냄
  • push(item)
  • insert(index, item) - index에 item을 삽입하고 기존 인덱스의 값부터 쭉 오른쪽으로 쉬프트
  • prepend(item) - 맨 앞에 원소를 삽입
  • pop() - 마지막 원소를 삭제하고 값을 돌려준다
  • delete(index) - delete item at index, shifting all trailing elements left
  • remove(item) - looks for value and removes index holding it (even if in multiple places)
  • find(item) - looks for value and returns first index with that value, -1 if not found
  • resize(new_capacity) // private 함수
  • 용량이 꽉 차면, 그 두배로 크기를 조정한다.
  • item을 하나 꺼낼 때, 용량이 1/4이라면, 용량을 절반으로 줄인다.
• 시간 복잡도
  • 접근, 수정, 끝에 추가/삭제하는 데 O(1)
  • 다른 곳에 추가/삭제하는 데 O(n)
• 공간 복잡도
  • 메모리에 연속적으로 있어서, 근접성이 성능을 향상시킨다.
  • 필요한 공간 = (n 이상인 배열의 용량) * item의 크기, 하지만 2n 크기에서는 여전히 O(n)

링크드 리스트¶

• 설명:
  • 단일 연결 리스트 (영상)
  • CS 61B - 연결 리스트 1 (영상)
  • CS 61B - 연결 리스트 2 (영상)
• C Code (video)
  - 전체 영상은 아니고, 노드 구조와 메모리 할당에 대한 부분입니다.
• 연결 리스트 vs 배열:
  • Core Linked Lists Vs Arrays (video)
  • 실세계에서의 연결 리스트 Vs 배열 (영상)
• 왜 연결 리스트를 기피해야 하는지 (영상)
• 짚고가기: 이중 포인터에 대한 지식이 필요하다면:
  (for when you pass a pointer to a function that may change the address where that pointer points)
  이 페이지는 포인터가 포인터를 가리키는 것을 파악하는 정도입니다. 저는 아래 목록을 순서대로 읽지 않기를 권장합니다. 가독성과 유지 보수성이 더 좋기 때문입니다.
  • Pointers to Pointers
• 구현 (저는 tail 포인터가 있는 것과 없는 것 모두 구현했었습니다.):
  • size() - 리스트 안의 데이터 개수를 반환한다.
  • empty() - 리스트가 비어있다면 true를 반환한다.
  • value_at(index) - index번째 위치의 value을 반환한다. (가장 앞은 0부터 시작한다.)
  • push_front(value) - 가장 앞에 value를 추가한다.
  • pop_front() - 가장 앞에 있는 것을 제거하고, 그 value를 반환한다.
  • push_back(value) - 가장 끝에 value을 추가한다.
  • pop_back() - 가장 끝에 있는 것을 제거하고, 그 value를 반환한다.
  • front() - 가장 앞에 있는 것의 value를 가져온다.
  • back() - 가장 끝에 있는 것의 value를 가져온다.
  • insert(index, value) - index번째 위치에 value를 추가한다. 즉, index번째에 새로 추가된 것이 기존의 index번째에 있던 것을 가리킨다.
  • erase(index) - index번째에 있는 노드를 삭제한다.
  • value_n_from_end(n) - 뒤에서부터 n번째에 있는 노드의 value를 반환한다.
  • reverse() - 리스트를 뒤집는다.
  • remove_value(value) - value와 같은 값을 가지는 첫 번째 노드를 제거한다.
• 이중 연결 리스트
  • 설명 (영상)
  • 구현할 필요는 없습니다.

스택¶

• Stacks (video)
• Will not implement. Implementing with array is trivial.

큐¶

• Queue (video)
• Circular buffer/FIFO05_04-priorityQueuesAndDeques.mp4)
• tail 포인터가 있는 연결 리스트를 사용하여 구현하기:
  • enqueue(value) - tail이 가리키는 곳에 value를 추가한다
  • dequeue() - value를 반환하고 가장 최근에 추가된 원소(front)를 제거한다.
  • empty()
• 고정 길이 배열을 사용하여 구현하기:
  • enqueue(value) - 사용 가능한 저장 공간의 끝에 item을 추가한다.
  • dequeue() - value를 반환하고 가장 최근에 추가된 원소를 제거한다.
  • empty()
  • full()
• 비용:
  • a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n)
    because you’d need the next to last element, causing a full traversal each dequeue
  • enqueue: O(1) (amortized, linked list and array [probing])
  • dequeue: O(1) (linked list and array)
  • empty: O(1) (linked list and array)

해시 테이블¶

• 동영상들:

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

• 온라인 강의들:

  • 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)

• Linear probing을 사용하여 배열로 구현해보기

  • hash(k, m) - m은 해시 테이블의 크기
  • add(key, value) - 키가 이미 존재한다면, 값을 갱신한다.
  • exists(key)
  • get(key)
  • remove(key)

추가 지식¶

이진 탐색¶

• Binary Search (video)
• Binary Search (video)
• 자세한 내용
• 구현:
  • (정수가 정렬된 배열에서) 이진 탐색
  • 재귀를 사용한 이진 탐색

비트 연산¶

• Bits cheat sheet - you should know many of the powers of 2 from (\(2^1\) to \(2^{16}\) and \(2^{32}\))
• 비트 연산자(&, |, ^, ~, >>, <<) 제대로 이해하기
  • 워드
  • 시작하기 좋은 곳:
    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)
• 2의 보수와 1의 보수
  • Binary: Plusses & Minuses (Why We Use Two’s Complement) (video)
  • 1의 보수
  • 2의 보수
• 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

트리¶

트리 - 배경 지식¶

• Series: Trees (video)
• 트리 기초 형태 만들기
• 순회
• 알고리즘 다루기
• BFS(너비-우선 탐색;breadth-first search) and DFS(깊이-우선 탐색;depth-first search)
  • BFS 노트:
  • level order (BFS, 큐 사용)
  • 시간 복잡도: O(n)
  • 공간 복잡도:
    최고: O(1)
    최악: O(n/2)=O(n)
  • DFS 노트:
  • 시간 복잡도: O(n)
  • 공간 복잡도:
    최고: O(log n) - 평균적으로, 트리의 높이이다.
    최악: O(n)
  • 중위(inorder) (DFS: 왼쪽, 자신, 오른쪽)
  • 후위(postorder) (DFS: 왼쪽, 오른쪽, 자신)
  • 전위(preorder) (DFS: 자신, 왼쪽, 오른쪽)

이진 탐색 트리 (BST)¶

• Binary Search Tree Review (video)
• Introduction (video)
• MIT (video)
• C/C++:
  • 이진 탐색 트리 - C/C++로 구현하기 (영상)
  • BST 구현 - 스택과 힙에 메모리 할당 (영상)
  • 이진 탐색 트리에서 가장 작은 원소와 가장 큰 원소 찾기 (영상)
  • 이진 트리의 높이 구하기 (영상)
  • 이진 트리 순회 - 너비-우선과 깊이-우선 전략 (영상)
  • 이진 트리: Level Order Traversal (video)
  • 이진 트리 순회: 전위, 중위, 후위 (영상)
  • 이진 트리가 이진 탐색 트리인지 아닌 지 확인하기 (영상)
  • 이진 탐색 트리에서 노드 삭제하기 (영상)
  • Inorder Successor in a binary search tree (video)
• 구현:
  • insert // 트리에 어떤 값을 삽입
  • get_node_count // 저장된 값들의 개수 세기
  • print_values // 트리 안의 값들을 최소부터 최대까지 출력
  • delete_tree
  • is_in_tree // 주어진 값이 트리 안에 있는 지를 반환
  • get_height // 어떤 노드의 높이를 반환 (노드 하나의 높이는 1이다.)
  • get_min // 트리에 저장된 값 중 가장 작은 값을 반환
  • get_max // 트리에 저장된 값 중 가장 큰 값을 반환
  • is_binary_search_tree
  • delete_value
  • get_successor // 값이 주어지면, 다음으로 가장 큰 값을, 없으면 -1을 반환

힙 / 우선순위 큐 / 이진 힙¶

• 트리처럼 보여지지만, 보통은 선형으로 저장됩니다. (배열, 링크드리스트처럼)
• 힙(Heap)
• 소개 (영상)
• Naive한 구현들 (영상)
• 이진 트리 (영상)
• Tree Height Remark (video)
• 기본 연산들 (영상)
• 완전 이진 트리 (영상)
• 의사 코드(Pseudocode) (영상)
• 힙 정렬 - 시작하기 (영상)
• 힙 정렬 (영상)
• 힙 만들기 (영상)
• MIT: 힙과 힙 정렬 (영상)
• CS 61B Lecture 24: 우선순위 큐 (영상)
• 선형 시간에 힙 만들기 (max-heap)
• max-heap 구현하기:
  • insert
  • sift_up - insert 하려면 필요
  • get_max - 최대 원소를 반환하되, 삭제는 하지 않는다.
  • get_size() - 저장된 원소들의 개수를 반환
  • is_empty() - 힙에 원소를 하나도 없는 지 반환
  • extract_max - 최대 원소를 반환하고, 그걸 삭제한다.
  • sift_down - extract_max 하려면 필요하다
  • remove(x) - x번째 원소를 삭제
  • heapify - 배열에 있는 원소들로 힙을 만든다. heap_sort 하려면 필요
  • heap_sort() - 정렬되지 않은 배열을 받아서 정렬된 배열로 만든다. 추가 메모리 없이 제자리에서 max-heap을 사용한다.
  • 노트: min-heap을 사용하면 연산을 줄일 수 있지만, 공간이 두 배로 필요합니다. (제자리에서 못 하기 때문에)

정렬¶

• Notes:
• 정렬들 구현 & 각 정렬의 최적의 경우/최악의 경우, 평균적인 복잡도를 알기:
  • 버블 소트 쓰지 마세요 - 끔찍하니까요 - n이 16이하 제외하고 O(n^2)
• 정렬 알고리즘들의 안정성 (“퀵소트는 안정적인가?”)
  • 정렬 알고리즘의 안정성
  • 정렬 알고리즘들의 안정성
  • 정렬 알고리즘들의 안정성
  • 정렬 알고리즘들 - 안정성

• 어떤 알고리즘들에 연결 리스트를 쓸 수 있는가? 배열은? 둘 다는?

  • 연결 리스트를 정렬하는 것은 추천하지 않지만, 병합 정렬은 가능합니다.
  • 링크드 리스트로 병합 정렬

• 힙소트의 경우, 위의 힙 데이터 구조를 보세요. 힙 정렬은 훌륭하지만 안정적이지 못합니다.

• 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 (영상)

• Analyzing Bubble Sort (영상)
• 삽입 정렬과 병합 정렬 (영상)
• 삽입 (영상)
• 병합 정렬 (영상)
• 퀵 정렬 (영상)

• 선택 정렬 (영상)

• 병합 정렬 코드:

• Using output array (C)
• Using output array (Python)
• In-place (C++)
• 퀵 정렬 코드:
• 구현 (C언어)
• 구현 (C언어)

• 구현 (Python)

• 구현:

• 병합 정렬: 평균과 최악의 경우 O(n log n)
• 퀵 정렬: 평균적인 경우 O(n log n)
• 선택 정렬과 삽입 정렬은 둘 다 평균과 최악의 경우에 O(n^2)

• 힙 정렬의 경우, 위의 힙 데이터 구조를 보세요.

• 필요한 건 아니지만, 아래도 추천합니다:

• Sedgewick - 기수 정렬(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)

개략적으로 보자면, 여기에 시각적으로 나타낸 15가지 정렬 알고리즘들을 보세요.
이 주제에 대해서 더 자세히 알고 싶다면, 몇몇 주제에 대한 세부사항에서 “정렬” 섹션를 보세요.

그래프¶

그래프는 컴퓨터 과학의 여러 문제들을 표현하는 데 사용할 수 있다. 때문에 이 섹션은 트리나 정렬 섹션처럼 길다.

• 노트:
• 메모리에 그래프를 표시하는 네 가지 기본 방법이 있다:
  • 오브젝트와 포인터
  • 행렬
  • 인접 리스트
  • 인접 맵
• 각각의 표현과 장단점을 숙지하라.
• 넓이 우선 탐색(BFS)와 깊이 우선 탐색(DFS) - 계산상의 복잡성, 장단점, 실제 코드로 구현하는 방법을 알아야 한다.

• 질문을 받을 시 먼저 그래프 기반 솔루션을 찾고, 없을 경우에 다른 솔루션으로 넘어가라.

• MIT(영상):

• 너비 우선 탐색(BFS;Breadth-First Search)

• 깊이 우선 탐색(DFS;Depth-First Search)

• Skiena의 강좌 - 시작하기 아주 좋습니다:

• CSE373 2012 - Lecture 11 - Graph Data Structures (video)
• CSE373 2012 - Lecture 12 - Breadth-First Search (video)
• CSE373 2012 - Lecture 13 - Graph Algorithms (video)
• CSE373 2012 - Lecture 14 - Graph Algorithms (con’t) (video)
• CSE373 2012 - Lecture 15 - Graph Algorithms (con’t 2) (video)

• CSE373 2012 - Lecture 16 - Graph Algorithms (con’t 3) (video)

• 그래프 (검토, 그 외 여러가지):

• 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 (starting at 58:09) (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)

• 내가 구현할 것:

• DFS with 인접 리스트 (재귀)
• DFS with 인접 리스트 (스택을 쓴 비재귀)
• DFS with 인접 행렬 (재귀)
• DFS with 인접 행렬 (스택을 쓴 비재귀)
• BFS with 인접 리스트
• BFS with 인접 행렬
• 단일 출발지 최단 경로 (다익스트라)
• 최소 신장 트리 (MST;minimum spanning tree)
• DFS-기반 알고리즘들 (위의 Aduni 영상들을 보세요):
  • 사이클 검사/확인 (위상 정렬할 때 필요합니다. 시작하기 전에 검사해야 하거든요.)
  • 위상 정렬
  • 그래프 내의 연결 요소(Connected Component)들 개수
  • 강연결요소(SCC;Strongly Connected Component)들 나열하기
  • 이분 그래프 확인하기

Skiena의 책(아래의 책 섹션 참조)과 인터뷰 책에서 더 많은 그래프 실습을 할 수 있습니다.

더 많은 지식¶

재귀 (recursion)¶

• 재귀와 백트래킹에 대한 스탠포드 대학 강의:
  • Lecture 8 | Programming Abstractions (video)
  • Lecture 9 | Programming Abstractions (video)
  • Lecture 10 | Programming Abstractions (video)
  • Lecture 11 | Programming Abstractions (video)
• 재귀는 언제 사용해야 하는 지
• 꼬리 재귀를 사용하는 게 그렇지 않은 것보다 얼마나 나은가요?
  • 꼬리 재귀가 무엇이고 왜 그게 좋지 않은지에 대하여
  • 꼬리 재귀 (영상)

동적 프로그래밍 (Dynamic Programming)¶

• 인터뷰에서 DP 문제를 접하지 않을 수도 있습니다. 하지만 알고 있는게 미뤄두는 것 보다 낫습니다.
• 이 주제는 아주 어렵습니다. DP로 풀리는 각 문제마다 어떤 점화식을 정의해야 하는데 그게 까다롭습니다.
• 얽혀있는 패턴들을 확실히 이해할 때까지, 많은 DP 예시 문제들을 찾아보기를 권합니다.
• Videos:
  • Skiena씨의 영상들은 따라가기 힘듭니다. 가끔 화이트보드를 사용하시는 데 너무 작아서 보기가 힘들거든요.
  • Skiena: CSE373 2012 - Lecture 19 - 동적 프로그래밍 소개 (영상)
  • Skiena: CSE373 2012 - Lecture 20 - Edit Distance (video)
  • Skiena: CSE373 2012 - Lecture 21 - 동적 프로그래밍 예제들 (영상)
  • Skiena: CSE373 2012 - Lecture 22 - 동적 프로그래밍의 활용 (영상)
  • Simonson: Dynamic Programming 0 (59:18부터 시작) (영상)
  • Simonson: Dynamic Programming I - Lecture 11 (영상)
  • Simonson: Dynamic programming II - Lecture 12 (영상)
  • 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)

디자인 패턴¶

• Quick UML review (video)
• 아래 패턴들을 배워봅시다:
  • strategy
  • singleton
  • adapter
  • prototype
  • decorator
  • visitor
  • factory, abstract factory
  • facade
  • observer
  • proxy
  • delegate
  • command
  • state
  • memento
  • iterator
  • composite
  • flyweight
• Chapter 6 (Part 1) - Patterns (video)
• Chapter 6 (Part 2) - Abstraction-Occurrence, General Hierarchy, Player-Role, Singleton, Observer, Delegation (video)
• Chapter 6 (Part 3) - Adapter, Facade, Immutable, Read-Only Interface, Proxy (video)
• Series of videos (27 videos)
• 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
• Design patterns for humans

조합과 확률¶

• 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-완전, 근사 알고리즘¶

• 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 2012 - Lecture 23 - Introduction to NP-Completeness (video)
  • CSE373 2012 - Lecture 24 - NP-Completeness Proofs (video)
  • CSE373 2012 - Lecture 25 - 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.

컴퓨터가 프로그램을 처리하는 방식¶

• 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)

캐시¶

• 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)

프로세스와 쓰레드¶

• 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?
• 알아 두어야 할 것:
  • 프로세스, 쓰레드, 동시성 문제들
  • 프로세스와 쓰레드의 차이점
  • 프로세스
  • 쓰레드
  • 락(Locks)
  • 뮤텍스(Mutexes)
  • 세마포어(Semaphores)
  • Monitors
  • 각각이 어떻게 동작하는지?
  • 데드락(Deadlock)
  • 라이브락(Livelock)
  • CPU activity, 인터럽트(interrupts), 문맥 교환(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

테스트¶

• 알아 두어야 할 것:
  • 유닛 테스트는 어떻게 작동하는지
  • mock object 는 무엇인지
  • 통합 테스트는 무엇인지
  • 의존성 주입은 무엇인지
• James Bach과 함께하는 애자일 소프트웨어 테스트 (비디오)
• 소프트웨어 테스트에 대한 James Bach의 무료 강의 (비디오)
• Steve Freeman - Test-Driven 개발 (이것은 우리가 의미하는 것은 아니다) (비디오)
  • 참고자료
• 의존성 주입:
  • 비디오
  • Tao Of Testing
• 테스트 어떻게 작성하는지

문자열 검색 & 조작¶

• 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)

  이 주제를 더 자세히 알고 싶으시다면, 몇몇 주제에 대한 세부사항에서 “문자열 매칭” 섹션을 읽어보세요.

트라이¶

• 트라이에는 여러 종류가 있다는 것을 유의하라. 어떤 건 접두사가 있는 데, 어떤 건 그렇지 않고 또 어떤 것은 경로 추적을 위해 비트 대신에 문자열을 사용한다.
• 나는 코드만 읽었고, 구현은 안 했다.
• 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)

부동 소수점¶

• simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)
• 32 bit: IEEE754 32-bit floating point binary (video)

유니코드¶

• 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.

네트워크¶

• 만약 당신이 네트워크에 대한 경험이 있거나 operations engineer 또는 믿음직한 엔지니어가 되고 싶다면 받을 수 있는 질문들
• 즉, 알면 좋은 것들이다.
• Khan Academy
• UDP and TCP: Comparison of Transport Protocols
• TCP/IP and the OSI Model Explained!
• Packet Transmission across the Internet. Networking & TCP/IP tutorial.
• HTTP
• SSL and HTTPS
• SSL/TLS
• HTTP 2.0
• Video Series (21 videos)
• Subnetting Demystified - Part 5 CIDR Notation
• 소켓:
  • Java - Sockets - Introduction (video)
  • Socket Programming (video)

시스템 디자인, 확장성, 데이터 핸들링¶

• 4년 이상의 경력자라면 이런 시스템 디자인 질문들을 받을 수 있다.
• Scalability and System Design are very large topics with many topics and resources, since
  there is a lot to consider when designing a software/hardware system that can scale.
  Expect to spend quite a bit of time on this.
• 고려사항:
• scalability
  • Distill large data sets to single values
  • Transform one data set to another
  • Handling obscenely large amounts of data
• system design
  • features sets
  • interfaces
  • class hierarchies
  • designing a system under certain constraints
  • simplicity and robustness
  • tradeoffs
  • performance analysis and optimization
• 여기서 시작하세요: The System Design Primer
• System Design from HiredInTech
• How Do I Prepare To Answer Design Questions In A Technical Inverview?
• 8 Things You Need to Know Before a System Design Interview
• Algorithm design
• Database Normalization - 1NF, 2NF, 3NF and 4NF (video)
• System Design Interview - 여기에 리소스가 정말 많이 있습니다. 글과 예제들을 살펴보세요. 일부는 아래에도 적어놓았습니다.
• How to ace a systems design interview
• Numbers Everyone Should Know
• How long does it take to make a context switch?
• Transactions Across Datacenters (video)
• A plain English introduction to CAP Theorem
• Consensus Algorithms:
• Paxos - Paxos Agreement - Computerphile (video)
• Raft - An Introduction to the Raft Distributed Consensus Algorithm (video)
  • Easy-to-read paper
  • Infographic
• Consistent Hashing
• NoSQL Patterns
• Scalability:
• Great overview (video)
• Short series:
  • Clones
  • Database
  • Cache
  • Asynchronism
• Scalable Web Architecture and Distributed Systems
• Fallacies of Distributed Computing Explained
• Pragmatic Programming Techniques
  • extra: Google Pregel Graph Processing
• Jeff Dean - Building Software Systems At Google and Lessons Learned (video)
• Introduction to Architecting Systems for Scale
• Scaling mobile games to a global audience using App Engine and Cloud Datastore (video)
• How Google Does Planet-Scale Engineering for Planet-Scale Infra (video)
• The Importance of Algorithms
• Sharding
• Scale at Facebook (2009)
• Scale at Facebook (2012), “Building for a Billion Users” (video)
• Engineering for the Long Game - Astrid Atkinson Keynote(video)
• 7 Years Of YouTube Scalability Lessons In 30 Minutes
  • video
• How PayPal Scaled To Billions Of Transactions Daily Using Just 8VMs
• How to Remove Duplicates in Large Datasets
• A look inside Etsy’s scale and engineering culture with Jon Cowie (video)
• What Led Amazon to its Own Microservices Architecture
• To Compress Or Not To Compress, That Was Uber’s Question
• Asyncio Tarantool Queue, Get In The Queue
• When Should Approximate Query Processing Be Used?
• Google’s Transition From Single Datacenter, To Failover, To A Native Multihomed Architecture
• Spanner
• Machine Learning Driven Programming: A New Programming For A New World
• The Image Optimization Technology That Serves Millions Of Requests Per Day
• A Patreon Architecture Short
• Tinder: How Does One Of The Largest Recommendation Engines Decide Who You’ll See Next?
• Design Of A Modern Cache
• Live Video Streaming At Facebook Scale
• A Beginner’s Guide To Scaling To 11 Million+ Users On Amazon’s AWS
• How Does The Use Of Docker Effect Latency?
• A 360 Degree View Of The Entire Netflix Stack
• Latency Is Everywhere And It Costs You Sales - How To Crush It
• Serverless (very long, just need the gist)
• What Powers Instagram: Hundreds of Instances, Dozens of Technologies
• Cinchcast Architecture - Producing 1,500 Hours Of Audio Every Day
• Justin.Tv’s Live Video Broadcasting Architecture
• Playfish’s Social Gaming Architecture - 50 Million Monthly Users And Growing
• TripAdvisor Architecture - 40M Visitors, 200M Dynamic Page Views, 30TB Data
• PlentyOfFish Architecture
• Salesforce Architecture - How They Handle 1.3 Billion Transactions A Day
• ESPN’s Architecture At Scale - Operating At 100,000 Duh Nuh Nuhs Per Second
• See “Messaging, Serialization, and Queueing Systems” way below for info on some of the technologies that can glue services together
• Twitter:
  • O’Reilly MySQL CE 2011: Jeremy Cole, “Big and Small Data at @Twitter” (video)
  • Timelines at Scale
• For even more, see “Mining Massive Datasets” video series in the Video Series section.
• Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
• review: The System Design Primer
• System Design from HiredInTech
• cheat sheet
• flow:
  1. Understand the problem and scope:
  - define the use cases, with interviewer’s help
  - suggest additional features
  - remove items that interviewer deems out of scope
  - assume high availability is required, add as a use case
  2. Think about constraints:
  - ask how many requests per month
  - ask how many requests per second (they may volunteer it or make you do the math)
  - estimate reads vs. writes percentage
  - keep 80/20 rule in mind when estimating
  - how much data written per second
  - total storage required over 5 years
  - how much data read per second
  3. Abstract design:
  - layers (service, data, caching)
  - infrastructure: load balancing, messaging
  - rough overview of any key algorithm that drives the service
  - consider bottlenecks and determine solutions
• Exercises:
  • Design a CDN network: old article
  • Design a random unique ID generation system
  • Design an online multiplayer card game
  • Design a key-value database
  • Design a picture sharing system
  • Design a recommendation system
  • Design a URL-shortener system: copied from above
  • Design a cache system

최종 검토¶

이 섹션에는 중요한 개념들을 빠르게 검토할 수 있는 짧은 영상들이 포함되어 있다.  
복습을 하고자 한다면, 이 영상들이 도움이 될 것이다.

• 2-3분 분량의 주제별 짧은 영상 시리즈 (23 videos)
• Videos
• 2-5분 분량의 주제별 짧은 영상 시리즈 - Michael Sambol (38 videos):
• Videos
• Sedgewick Videos - Algorithms I
• Sedgewick Videos - Algorithms II

Additional Books¶

아래는 당신이 흥미로워하는 주제에 대해 공부할 수 있는 자료들입니다.

• The Unix Programming Environment
• an oldie but a goodie
• The Linux Command Line: A Complete Introduction
• a modern option
• TCP/IP Illustrated Series
• Head First Design Patterns
• a gentle introduction to design patterns
• Design Patterns: Elements of Reusable Object-Oriente d Software
• aka the “Gang Of Four” book, or GOF
• the canonical design patterns book
• UNIX and Linux System Administration Handbook, 5th Edition
• Algorithm Design Manual (Skiena)
• As a review and problem recognition
• The algorithm catalog portion is well beyond the scope of difficulty you’ll get in an interview.
• This book has 2 parts:
  • class textbook on data structures and algorithms
  • pros:
    • is a good review as any algorithms textbook would be
    • nice stories from his experiences solving problems in industry and academia
    • code examples in C
  • cons:
    • can be as dense or impenetrable as CLRS, and in some cases, CLRS may be a better alternative for some subjects
    • chapters 7, 8, 9 can be painful to try to follow, as some items are not explained well or require more brain than I have
    • don’t get me wrong: I like Skiena, his teaching style, and mannerisms, but I may not be Stony Brook material.
  • algorithm catalog:
  • this is the real reason you buy this book.
  • about to get to this part. Will update here once I’ve made my way through it.
• Can rent it on kindle
• Answers:
  • Solutions
  • Solutions
• Errata
• Write Great Code: Volume 1: Understanding the Machine
• The book was published in 2004, and is somewhat outdated, but it’s a terrific resource for understanding a computer in brief.
• The author invented HLA, so take mentions and examples in HLA with a grain of salt. Not widely used, but decent examples of what assembly looks like.
• These chapters are worth the read to give you a nice foundation:
  • Chapter 2 - Numeric Representation
  • Chapter 3 - Binary Arithmetic and Bit Operations
  • Chapter 4 - Floating-Point Representation
  • Chapter 5 - Character Representation
  • Chapter 6 - Memory Organization and Access
  • Chapter 7 - Composite Data Types and Memory Objects
  • Chapter 9 - CPU Architecture
  • Chapter 10 - Instruction Set Architecture
  • Chapter 11 - Memory Architecture and Organization
• Introduction to Algorithms
• Important: Reading this book will only have limited value. This book is a great review of algorithms and data structures, but won’t teach you how to write good code. You have to be able to code a decent solution efficiently.

• aka CLR, sometimes CLRS, because Stein was late to the game

• Computer Architecture, Sixth Edition: A Quantitative Approach

• For a richer, more up-to-date (2017), but longer treatment

• Programming Pearls

• The first couple of chapters present clever solutions to programming problems (some very old using data tape) but
  that is just an intro. This a guidebook on program design and architecture.

Additional Learning¶

두루 갖춘 소프트웨어 엔지니어가 되는데 도움이 될만한 것들을 추가했습니다. 이를 통해 더 큰 도구들을 다루실 수 있게 되실 겁니다.

• Compilers¶

• How a Compiler Works in ~1 minute (video)
• Harvard CS50 - Compilers (video)
• C++ (video)

• Understanding Compiler Optimization (C++) (video)

• Emacs and vi(m)¶

• Familiarize yourself with a unix-based code editor
• vi(m):
  • Editing With vim 01 - Installation, Setup, and The Modes (video)
  • VIM Adventures
  • set of 4 videos:
  • The vi/vim editor - Lesson 1
  • The vi/vim editor - Lesson 2
  • The vi/vim editor - Lesson 3
  • The vi/vim editor - Lesson 4
  • Using Vi Instead of Emacs

• emacs:

  • Basics Emacs Tutorial (video)
  • set of 3 (videos):
  • Emacs Tutorial (Beginners) -Part 1- File commands, cut/copy/paste, cursor commands
  • Emacs Tutorial (Beginners) -Part 2- Buffer management, search, M-x grep and rgrep modes
  • Emacs Tutorial (Beginners) -Part 3- Expressions, Statements, ~/.emacs file and packages
  • Evil Mode: Or, How I Learned to Stop Worrying and Love Emacs (video)
  • Writing C Programs With Emacs

• Unix command line tools¶

• I filled in the list below from good tools.
• bash
• cat
• grep
• sed
• awk
• curl or wget
• sort
• tr
• uniq
• strace

• tcpdump

• Information theory (videos)¶

• Khan Academy
• More about Markov processes:
  • Core Markov Text Generation
  • Core Implementing Markov Text Generation
  • Project = Markov Text Generation Walk Through

• See more in MIT 6.050J Information and Entropy series below.

• Parity & Hamming Code (videos)¶

• Intro
• Parity
• Hamming Code:
  • Error detection
  • Error correction

• Error Checking

• Entropy¶

• also see videos below
• make sure to watch information theory videos first

• Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)

• Cryptography¶

• also see videos below
• make sure to watch information theory videos first
• Khan Academy Series
• Cryptography: Hash Functions

• Cryptography: Encryption

• Compression¶

• make sure to watch information theory videos first
• Computerphile (videos):
  • Compression
  • Entropy in Compression
  • Upside Down Trees (Huffman Trees)
  • EXTRA BITS/TRITS - Huffman Trees
  • Elegant Compression in Text (The LZ 77 Method)
  • Text Compression Meets Probabilities
• Compressor Head videos

• (optional) Google Developers Live: GZIP is not enough!

• Computer Security¶

• MIT (23 videos)

  • Introduction, Threat Models
  • Control Hijacking Attacks
  • Buffer Overflow Exploits and Defenses
  • Privilege Separation
  • Capabilities
  • Sandboxing Native Code
  • Web Security Model
  • Securing Web Applications
  • Symbolic Execution
  • Network Security
  • Network Protocols
  • Side-Channel Attacks

• Garbage collection¶

• GC in Python (video)
• Deep Dive Java: Garbage Collection is Good!

• Deep Dive Python: Garbage Collection in CPython (video)

• Parallel Programming¶

• Coursera (Scala)

• Efficient Python for High Performance Parallel Computing (video)

• Messaging, Serialization, and Queueing Systems¶

• Thrift
  • Tutorial
• Protocol Buffers
  • Tutorials
• gRPC
  • gRPC 101 for Java Developers (video)
• Redis
  • Tutorial
• Amazon SQS (queue)
• Amazon SNS (pub-sub)
• RabbitMQ
  • Get Started
• Celery
  • First Steps With Celery
• ZeroMQ
  • Intro - Read The Manual
• ActiveMQ
• Kafka
• MessagePack

• Avro

• A*¶

• A Search Algorithm
• A* Pathfinding Tutorial (video)

• A* Pathfinding (E01: algorithm explanation) (video)

• Fast Fourier Transform¶

• An Interactive Guide To The Fourier Transform
• What is a Fourier transform? What is it used for?
• What is the Fourier Transform? (video)
• Divide & Conquer: FFT (video)

• Understanding The FFT

• Bloom Filter¶

• Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
• Bloom Filters
• Bloom Filters | Mining of Massive Datasets | Stanford University
• Tutorial

• How To Write A Bloom Filter App

• HyperLogLog¶

• How To Count A Billion Distinct Objects Using Only 1.5KB Of Memory

• Locality-Sensitive Hashing¶

• used to determine the similarity of documents
• the opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same.

• Simhashing (hopefully) made simple

• van Emde Boas Trees¶

• Divide & Conquer: van Emde Boas Trees (video)

• MIT Lecture Notes

• Augmented Data Structures¶

• CS 61B Lecture 39: Augmenting Data Structures

• Balanced search trees¶

• 적어도 하나의 타입의 균형 이진 트리에 대하여 알고 계시는 게 좋습니다 (그리고 어떻게 적용되는지까지요):
• “Among balanced search trees, AVL and 2/3 trees are now passé, and red-black trees seem to be more popular.
  A particularly interesting self-organizing data structure is the splay tree, which uses rotations
  to move any accessed key to the root.” - Skiena
• Of these, I chose to implement a splay tree. From what I’ve read, you won’t implement a
  balanced search tree in your interview. But I wanted exposure to coding one up
  and let’s face it, splay trees are the bee’s knees. I did read a lot of red-black tree code.
  • splay tree: insert, search, delete functions
    If you end up implementing red/black tree try just these:
  • search and insertion functions, skipping delete
• I want to learn more about B-Tree since it’s used so widely with very large data sets.
• Self-balancing binary search tree

AVL trees¶

• In practice:
  From what I can tell, these aren’t used much in practice, but I could see where they would be:
  The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly
  balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it
  attractive for data structures that may be built once and loaded without reconstruction, such as language
  dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter).
• MIT AVL Trees / AVL Sort (video)
• AVL Trees (video)
• AVL Tree Implementation (video)
• Split And Merge

Splay trees¶

• In practice:
  Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors,
  data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory,
  networking and file system code) etc.
• CS 61B: Splay Trees (video)
• MIT Lecture: Splay Trees:
• Gets very mathy, but watch the last 10 minutes for sure.
• Video

2-3 search trees¶

• In practice:
  2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
• You would use 2-3 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
• 23-Tree Intuition and Definition (video)
• Binary View of 23-Tree

• 2-3 Trees (student recitation) (video)

• 2-3-4 Trees (aka 2-4 trees)

  • In practice:
    For every 2-4 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion
    operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an
    important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce
    2-4 trees just before red–black trees, even though 2-4 trees are not often used in practice.
  • CS 61B Lecture 26: Balanced Search Trees (video)
  • Bottom Up 234-Trees (video)
  • Top Down 234-Trees (video)

• N-ary (K-ary, M-ary) trees

  • note: the N or K is the branching factor (max branches)
  • binary trees are a 2-ary tree, with branching factor = 2
  • 2-3 trees are 3-ary
  • K-Ary Tree

• B-Trees

  • 재밌는 사실: it’s a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor)
  • In Practice:
    B-트리는 데이터베이스에 광범위하게 사용됩니다. 가장 현대적인 파일시스템은 B-트리를 씁니다 (or Variants).
    데이터베이스에 사용될 뿐만 아니라, B-트리는 특정한 파일의 임의의 블록에 ‘빠른 무작위 탐색’을 가능하게 합니다.
    기본적인 문제는 파일블록 주소 i를 하나의 디스크 블록(또는 아마도 실린더-헤드-섹터) 주소로 바꾸는 것입니다.
  • B-Tree
  • B-Tree Definition and Insertion (video)
  • Introduction to B-Trees (video)
  • B-Tree Deletion (video)
  • MIT 6.851 - Memory Hierarchy Models (video)
    - covers cache-oblivious B-Trees, very interesting data structures
    - the first 37 minutes are very technical, may be skipped (B is block size, cache line size)

• k-D Trees¶

• great for finding number of points in a rectangle or higher dimension object
• a good fit for k-nearest neighbors
• Kd Trees (video)

• kNN K-d tree algorithm (video)

• Skip lists¶

• “These are somewhat of a cult data structure” - Skiena
• Randomization: Skip Lists (video)

• For animations and a little more detail

• Network Flows¶

• Ford-Fulkerson in 5 minutes — Step by step example (video)
• Ford-Fulkerson Algorithm (video)

• Network Flows (video)

• Disjoint Sets & Union Find¶

• UCB 61B - Disjoint Sets; Sorting & selection (video)

• Sedgewick Algorithms - Union-Find (6 videos)

• Math for Fast Processing¶

• Integer Arithmetic, Karatsuba Multiplication (video)

• The Chinese Remainder Theorem (used in cryptography) (video)

• Treap¶

• Combination of a binary search tree and a heap
• Treap
• Data Structures: Treaps explained (video)

• Applications in set operations

• Linear Programming (videos)¶

• Linear Programming
• Finding minimum cost
• Finding maximum value

• Solve Linear Equations with Python - Simplex Algorithm

• Geometry, Convex hull (videos)¶

• Graph Alg. IV: Intro to geometric algorithms - Lecture 9
• Geometric Algorithms: Graham & Jarvis - Lecture 10

• Divide & Conquer: Convex Hull, Median Finding

• 이산수학¶

• 아래에 있는 영상을 확인하세요.

• 기계학습¶

• 왜 기계학습이 중요하죠?
  • How Google Is Remaking Itself As A Machine Learning First Company
  • Large-Scale Deep Learning for Intelligent Computer Systems (video)
  • Deep Learning and Understandability versus Software Engineering and Verification by Peter Norvig
• Google’s Cloud Machine learning tools (video)
• Google Developers’ Machine Learning Recipes (Scikit Learn & Tensorflow) (video)
• Tensorflow (video)
• Tensorflow Tutorials
• Practical Guide to implementing Neural Networks in Python (using Theano)
• 강의들:
  • Great starter course: Machine Learning
    - videos only
    - see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
  • Neural Networks for Machine Learning
  • Google’s Deep Learning Nanodegree
  • Google/Kaggle Machine Learning Engineer Nanodegree
  • Self-Driving Car Engineer Nanodegree
• 자료들:
  • Books:
  • Python Machine Learning
  • Data Science from Scratch: First Principles with Python
  • Introduction to Machine Learning with Python
  • Machine Learning for Software Engineers
  • Data School: http://www.dataschool.io/

몇몇 주제에 대한 세부사항¶

이미 언급한 몇몇의 개념에 대한 설명을 좀 더 보강하기 위해서 적었습니다.  
하지만 더하길 원하지 않았어요. 왜냐면 그 양이 너무나 방대하기 때문이지요.   
하나의 주제에 대하여 지나치게 깊게 파고드는 것은 쉬운 일입니다.  
이번 세기에 직장을 구하고 싶으시잖아요, 맞죠?

• SOLID
• Bob Martin SOLID Principles of Object Oriented and Agile Design (video)
• S - Single Responsibility Principle | Single responsibility to each Object
  • more flavor
• O - Open/Closed Principal | On production level Objects are ready for extension but not for modification
  • more flavor
• L - Liskov Substitution Principal | Base Class and Derived class follow ‘IS A’ principal
  • more flavor
• I - Interface segregation principle | clients should not be forced to implement interfaces they don’t use
  • Interface Segregation Principle in 5 minutes (video)
  • more flavor

• D -Dependency Inversion principle | Reduce the dependency In composition of objects.

  • Why Is The Dependency Inversion Principle And Why Is It Important
  • more flavor

• Union-Find

• Overview
• Naive Implementation
• Trees
• Union By Rank
• Path Compression

• Analysis Options

• More Dynamic Programming (videos)

• 6.006: Dynamic Programming I: Fibonacci, Shortest Paths
• 6.006: Dynamic Programming II: Text Justification, Blackjack
• 6.006: DP III: Parenthesization, Edit Distance, Knapsack
• 6.006: DP IV: Guitar Fingering, Tetris, Super Mario Bros.
• 6.046: Dynamic Programming & Advanced DP
• 6.046: Dynamic Programming: All-Pairs Shortest Paths

• 6.046: Dynamic Programming (student recitation)

• Advanced Graph Processing (videos)

• Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees

• Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees

• MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):

• MIT 6.042J - Probability Introduction
• MIT 6.042J - Conditional Probability
• MIT 6.042J - Independence
• MIT 6.042J - Random Variables
• MIT 6.042J - Expectation I
• MIT 6.042J - Expectation II
• MIT 6.042J - Large Deviations

• MIT 6.042J - Random Walks

• Simonson: Approximation Algorithms (video)

• **문자열 매칭

• 라빈-카프(Rabin-Karp) (동영상):
  • 라빈 카프 알고리즘
  • Precomputing
  • 최적화: 구현과 분석
  • Table Doubling, Karp-Rabin
  • Rolling Hashes, Amortized Analysis
• Knuth-Morris-Pratt (KMP):
  • The Knuth-Morris-Pratt (KMP) String Matching Algorithm
• 보이어-무어(Boyer–Moore) 문자열 검색 알고리즘
  • Boyer-Moore String Search Algorithm
  • Advanced String Searching Boyer-Moore-Horspool Algorithms (video)

• Coursera: Algorithms on Strings

  • starts off great, but by the time it gets past KMP it gets more complicated than it needs to be
  • 트라이(tries)에 대해서 잘 설명하고 있다.
  • 이건 생략 가능

• 정렬

• 스탠포드 대학의 정렬 강의들:

  • Lecture 15 | Programming Abstractions (video)
  • Lecture 16 | Programming Abstractions (video)
• Shai Simonson, Aduni.org:
  • Algorithms - Sorting - Lecture 2 (video)
  • Algorithms - Sorting II - Lecture 3 (video)
• Steven Skiena lectures on sorting:
  • lecture begins at 26:46 (video)
  • lecture begins at 27:40 (video)
  • lecture begins at 35:00 (video)
  • lecture begins at 23:50 (video)

Video Series¶

편하게 보세요. “Netflix and skill”이라니까요 :P

• List of individual Dynamic Programming problems (each is short)

• x86 Architecture, Assembly, Applications (11 videos)

• MIT 18.06 Linear Algebra, Spring 2005 (35 videos)

• Excellent - MIT Calculus Revisited: Single Variable Calculus

• Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory

• Discrete Mathematics by Shai Simonson (19 videos)

• Discrete Mathematics Part 1 by Sarada Herke (5 videos)

• CSE373 - Analysis of Algorithms (25 videos)

• Skiena lectures from Algorithm Design Manual

• UC Berkeley 61B (Spring 2014): Data Structures (25 videos)

• UC Berkeley 61B (Fall 2006): Data Structures (39 videos)

• UC Berkeley 61C: Machine Structures (26 videos)

• OOSE: Software Dev Using UML and Java (21 videos)

• UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)

• MIT 6.004: Computation Structures (49 videos)

• Carnegie Mellon - Computer Architecture Lectures (39 videos)

• MIT 6.006: Intro to Algorithms (47 videos)

• MIT 6.033: Computer System Engineering (22 videos)

• MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)

• MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)

• MIT 6.046: Design and Analysis of Algorithms (34 videos)

• MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)

• MIT 6.851: Advanced Data Structures (22 videos)

• MIT 6.854: Advanced Algorithms, Spring 2016 (24 videos)

• Harvard COMPSCI 224: Advanced Algorithms (25 videos)

• MIT 6.858 Computer Systems Security, Fall 2014

• Stanford: Programming Paradigms (27 videos)

• Introduction to Cryptography by Christof Paar

• Course Website along with Slides and Problem Sets

• Mining Massive Datasets - Stanford University (94 videos)

• Graph Theory by Sarada Herke (67 videos)

컴퓨터 과학 강의들¶

• 컴퓨터 과학 온라인 강의들
• (많은 온라인 강의가 있는) 컴퓨터 과학 강의들

학술 자료들¶

• Love classic papers?
• 1978: Communicating Sequential Processes
• implemented in Go
• 2003: The Google File System
• replaced by Colossus in 2012
• 2004: MapReduce: Simplified Data Processing on Large Clusters
• mostly replaced by Cloud Dataflow?
• 2006: Bigtable: A Distributed Storage System for Structured Data
• An Inside Look at Google BigQuery
• 2006: The Chubby Lock Service for Loosely-Coupled Distributed Systems
• 2007: Dynamo: Amazon’s Highly Available Key-value Store
• The Dynamo paper kicked off the NoSQL revolution
• 2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)
• 2010: Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
• 2010: Dremel: Interactive Analysis of Web-Scale Datasets
• 2012: Google’s Colossus
• paper not available
• 2012: AddressSanitizer: A Fast Address Sanity Checker:
• paper
• video
• 2013: Spanner: Google’s Globally-Distributed Database:
• paper
• video
• 2014: Machine Learning: The High-Interest Credit Card of Technical Debt
• 2015: Continuous Pipelines at Google
• 2015: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
• 2015: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• 2015: How Developers Search for Code: A Case Study
• 2016: Borg, Omega, and Kubernetes