यह मेरी एक वेब डेवलपर ( खुद से सीखा हुआ , बिना किसी कंप्यूटर साइंस डिग्री के ) से एक बड़ी कंपनी के लिए सॉफ्टवेयर इंजीनियर बनने की कई महीनो की योजना है ।
यह लम्बी सूची गूगल कोचिंग नोट्स से उद्धरण एव विस्तारित की गयी हैं, ताकि इन बातो को आपको पता चल सके. मैंने आपके इंटरव्यू में मदद कर सकने वाले कुछ अतिरिक्त विषय सूचि के, आखिर में डाले है.
अनेक विषय, स्टीव येग्गे की "Get that job at Google" से हैं.
- यह क्या है?
- इसका उपयोग क्यों करे?
- इसका कैसे उपयोग करे?
- गूगल की मुद्रा में आ जाएँ
- क्या मुझे नौकरी मिली?
- मेरे साथ चले
- अपने आप को कमजोर मत समझो
- गूगल के बारे में
- विडियो संसाधनों के बारे में
- इंटरव्यू प्रकिया और साधारण इंटरव्यू तयारी
- इंटरव्यू के लिए एक भाषा चुने
- प्रारंभ करने से पहले
- इसमे क्या समाविष्ट नहीं हे
- पूर्व प्रयोजनीय ज्ञान
- दैनिक योजना
- अल्गोरिथम जटिलता / बिग-O / Asymptotic analysis
- डेटा संरचनाएं
- अधिक जानकारी
- ट्रीज
- सॉर्टिंग
- ग्राफ
- और अधिक जानकारी
- रिकर्शन
- डायनामिक प्रोग्रामिंग
- Combinatorics (n choose k) & Probability
- NP, NP-Complete and Approximation Algorithms
- गार्बेज कलेक्शन
- काशेस
- Processes and Threads
- System Design, Scalability, Data Handling
- Papers
- Unicode
- Emacs और vi(m)
- Unix command line उपकरण
- परिक्षण
- Design patterns
- Scheduling
- Implement system routines
- String searching & manipulations
- आखरी समीक्षा
- पुस्तकें
- कोडिंग अभ्यास/चुनौतियों
- एक बार जब आप इंटरव्यू के करीब हो
- आपका रिज्यूमे
- इंटरव्यू की सोंच
- इन्तेर्विएवर के लिए प्रश्न रखे
- अतिरिक्त पढाई (जरुरत नहीं)
- इनफार्मेशन थ्योरी
- पारिटी और हैमिंग कोड
- एन्थ्रोपी
- क्रिप्टोग्राफी
- संक्षिप्तीकरण
- नेटवर्किंग
- संगणक सुरक्षा
- परैल्लेल प्रोग्रामिंग
- Messaging, Serialization, and Queueing Systems
- Fast Fourier Transform
- ब्लूम फ़िल्टर
- van Emde Boas Trees
- Augmented Data Structures
- स्किप लिस्ट
- Network Flows
- Disjoint Sets & Union Find
- Math for Fast Processing
- Treap
- Linear Programming - ज्यामिति, कॉन्वेक्स हल
- Discrete math
- मशीन लर्निंग
- गो
- कुछ विषयोकी अधिक जानकारी
- विडियो शृखला
- जब आपको नौकरी मिल जाये
मैं यह योजना का अनुपालन गूगल इनेर्विएव के तयारी के लिए कर रहा हूँ. मैं १९९७ से वेब, सर्विसेज और स्टार्टअप का निर्माण कर रहा हूँ. मेरे पास संगणक शात्र की पदवी ना होक अर्थशात्र की पदवी हैं. मैं अपने कैरियर में बहुत सफल रहा हूँ, पर मुजे गूगल में काम करने की इच्छा हें. मैं एक बड़े सिस्टम में प्रगति और कंप्यूटर प्रणालियों की एक असली समझ प्राप्त करना चाहते हु, अल्गोरिथम की निपुणता, डाटा स्ट्रक्चर का निष्पादन, लो-लेवल भाषाए, और वो कैसे काम करती हें. और अगर आपको एनमेंसे किसी की जानकारी नहीं हे तो गूगल आपको नियुन्क्त नहीं करेगा. मैंने जब ये परियोजना शुरू की, तब मैं स्टैक और हीप में फरक नहीं जनता था, मुजे नहीं पता था की Big-O क्या हे, ट्रीज क्या हे, या ग्राफ को पार कैसे करते हैं. अगर मुजे छाटने का अल्गोरिथम लिखना पड़ता तो मैं आपको ये बता सकता हु के वो इतना ख़ास नहीं होगा. जो भी डाटा स्ट्रक्चर का मैंने उपयोग किया वो भाषा में समाविष्ट था, और वो कैसे काम करता हे उसकी कोई जानकारी मुजे नहीं थी. मुजे कभी मेमोरी का संचालन नहीं करता पड़ा, जबतक मेरी चलाई कोई प्रोसेस "out of memory" का एरर न दे, और तब मुजे कोई वैकल्पिक हल धुन्दाना पड़ता था. मैंने मेरी जिन्दगी में बहोत कम मुल्ती-डायमेंशनल ऐरे और बहोत सारे अस्सोसिअतिव् ऐरे का उपयोग किया हे, पर मैंने कोई भी डाटा स्ट्रक्चर शुरू से नहीं लिखा था. पर इस अध्ययन योजना का उपयोग करने बाद मेरा नौकरी लगाने का आत्मविश्वास बहोत बढ़ा हें. यह एक लम्बी योजना हें. यह मेरे लिए बहोत महीनोतक चलेगी. अगर आपको ईंमैसे कुछ पता हैं तो आपको कम वक्त लगेगा.
नीचे सब कुछ एक रूपरेखा है, और आप ऊपर से नीचे के क्रम में पढ़े.
मैं गितहब के विशेष मार्कडाउन का उपयोग कर रहा हूँ, प्रगति की जाँच करने के लिए कार्य सूचियों का प्रयोग करे.
- एक नई शाखा बनाएँ ताकि आप इस तरह की वस्तुओं की जांच कर सकते हैं, बस कोष्ठक में एक एक्स डाले: [x]
Github-flavored markdown की अधिक जानकारी
"फ्यूचर गूगलर" साइन की एक (या दो) प्रिंट निकाले और अपने पुरस्कार को आपने नजरो के सामने रखे.
मैंने अभीतक प्रयुक्त नहीं किया हें.
मुजे अभीभी कुछ दींन हे ये सूचि समाप्त करने के लिए, और आगे पुरे हफ्ते से में पूरा दिनप्रोग्रामिंग प्रश्न करने वाला हु. ये कुछ हफ्ते तक चलेगा और फिर मैं मेरे रेफेरेल जो की मैं फेब्रुअरी से रखा हे उससे नौकरी का अर्ज दूंगा.
Thanks for the referral, JP.
मैं एक सफ़र पर हु, मेरे साथ चलिए मेरे ब्लॉग से साथ GoogleyAsHeck.com
- Twitter: @googleyasheck
- Twitter: @StartupNextDoor
- Google+: +Googleyasheck
- LinkedIn: johnawasham
- गोगल के अभियंता चालक होते हें, पर बहोत लोगो असुरक्षा होती हे की वो नहीं चालक नहीं हें, जबकि वो गूगल में काम करते हें!
- The myth of the Genius Programmer
- छात्रों के लिए - Google Careers: Technical Development Guide
- सर्च कैसे काम करता हे:
- शृंखला:
- पुष्तक: गूगल कैसे काम करता हैं
- Made by Google घोषणा - ओक्टोबर २०१६ (विडियो)
कुछ विडियो सिर्फ Coursera, EdX, or Lynda.com के वर्ग में दाखिला लेने का बाद ही उपलब्ध हैं. उन्हें MOOC कहा जाता हैं. It is free to do so, but sometimes the classes are not in session so you have to wait a couple of months, so you have no access.
I'd appreciate your help converting the MOOC video links to public sources to replace the online course विडियो over time. I like using university lectures.
-
विडियो:
-
लेख:
- तिन कदमोमे गूगलर बने
- गूगल में वो नौकरी लो
- all the things he mentions that you need to know are listed below
- (बहोत पुराना) गूगल में नौकरी कैसे ले, इन्तेविएव प्रश्न, Hiring Process
- फोन स्क्रीन वाले सवाल
-
अतिरिक्त (not suggested by Google but I added):
- ABC: Always Be Coding
- Four Steps To Google Without A Degree
- Whiteboarding
- How Google Thinks About Hiring, Management And Culture
- Effective Whiteboarding during Programming Interviews
- Cracking The Coding Interview Set 1:
- बड़े ४ मैं नौकरी कैसे ले:
- गूगल इंटरव्यू में असफलता
मैं इसके बारे में इस छोटे से लेख लिखा था: महत्वपूर्ण: गूगल इंटरव्यू के लिए एक भाषा चुनें
इंटरव्यू मैं आप कोंसिभी एक भाषा जिसमे आप आरामदायक हो वो चुन सकते हैं, पर गूगल के लिए निम्नलिखित भाषाएँ अच्छी रहेगी:
- C++
- Java
- Python
आप निम्न्लिहित भाषाएँ भी चुन सकते हैं, पर उन्हें सावधानीसे चुने
- JavaScript
- Ruby
आप भाषा में बहुत सहज हो, और उसकी जानकार होने की जरूरत है.
विकल्पों के बारे में अधिक पढ़ें:
- http://www.byte-by-byte.com/choose-the-right-language-for-your-coding-interview/
- http://blog.codingforinterviews.com/best-programming-language-jobs/
- https://www.quora.com/What-is-the-best-language-to-program-in-for-an-in-person-Google-interview
क्युकी में मैं पढ़ रहा हूँ, आपको कुछ C, C++, और Python शामिल दिखेगा. वहाँ कुछ शामिल किताबें, नीचे आखिर में देख ले.
इस सूची में कई महीनों से वृद्धि हुई है, और हाँ, यह एक तरह से हाथ से बाहर हो गयी हैं
निचे कुछ गलतिया हैं जो मैंने की हैं तो आपका अनुभव बेहतर होगा
मैंने घंटो वीडिय के विडियो देखे और टिप्पणिया लिखी, और महीनो बाद मुजे कुछ याद नहीं रहा. सबकी समीक्षा करने के लिए मैंने 3 दिन मेरी तिप्पनिओयो और flashcards बनाने में बितायें (नीचे देखें).
To solve the problem, I made a little flashcards site where I could add flashcards of 2 types: general and code. Each card has different formatting.
I made a mobile-first website so I could review on my phone and tablet, wherever I am.
Make your own for free:
- Flashcards site repo
- My flash cards database: Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It's way too much for what's required by Google.
Note on flashcards: The first time you recognize you know the answer, don't mark it as known. You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain.
I keep a set of cheatsheets on ASCII, OSI stack, Big-O notations, and more. I study them when I have some spare time.
Take a break from programming problems for a half hour and go through your flashcards.
There are a lot of distractions that can take up valuable time. Focus and concentration is hard.
यह बड़ी सूचि गूगल इंटरव्यू टिप्पणियों से व्यक्तिगत कार्य सूचि से बनायीं गयी थी. निचे कुछ प्रचलित टेक्नोलॉजी हैं पर उन्हें टिप्पणियो में समविस्ट नहीं किया गया हैं.
- SQL
- Javascript
- HTML, CSS, and other front-end technologies
कुछ विषय एक दिन ले सकते हैं और कुछ ज्यादा.कुचो का सिर्फ पढाई हो सकती हैं पर अमल नहीं हो सकता.
हर दिन मैं निचली सूचि से एक विषय लेता हु, उसका विडियो देखता हु, और उसका अमल निचे दिए तरह करता हूँ: C - struct और function का उपयोग करके जो struct * या args का उपयोग करते हैं. C++ - built-in types का उपयोग न करके C++ - built-in types का उपयोग करके, जैसे STL की std::list, linked list के लिए Python - built-in types का उपयोग करके (Python का अभ्यास रखने के लिए) and write tests to ensure I'm doing it right, sometimes just using simple assert() statements You may do Java or something else, this is just my thing.
Why code in all of these? Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember) Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python)) Make use of built-in types so I have experience using the built-in tools for real-world use (not going to write my own linked list implementation in production)
I may not have time to do all of these for every subject, but I'll try.
You can see my code here:
- [C] (https://github.com/jwasham/practice-c)
- [C++] (https://github.com/jwasham/practice-cpp)
- [Python] (https://github.com/jwasham/practice-python)
You don't need to memorize the guts of every algorithm.
Write code on a whiteboard, not a computer. Test with some sample inputs. Then test it out on a computer.
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How computers process a program:
-
How floating point numbers are stored:
- simple 8-bit: Fractions in binary? (विडियो)
- 32 bit: Representation of Floating Point Numbers - 1 (विडियो)
- 64 bit: IEEE754 32-bit floating point binary (विडियो)
-
Computer Arch Intro: (first video only - interesting but not required) Introduction and Basics - Carnegie Mellon - Computer Architecture
-
Compilers
-
nothing to implement
-
Big O Notation (and Omega and Theta) - best mathematical explanation (विडियो)
-
Skiena:
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TopCoder (includes recurrence relations and master theorem):
-
If some of the lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics विडियो to get the background knowledge.
-
- Implement an automatically resizing vector.
- Description:
- Implement a vector (mutable array with automatic resizing):
- Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
- new raw data array with allocated memory
- can allocate int array under the hood, just not use its features
- start with 16, or if starting number is greater, use power of 2 - 16, 32, 64, 128
- size() - number of items
- capacity() - number of items it can hold
- is_empty()
- at(index) - returns item at given index, blows up if index out of bounds
- push(item)
- insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
- prepend(item) - can use insert above at index 0
- pop() - remove from end, return value
- 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 function
- when you reach capacity, resize to double the size
- when popping an item, if size is 1/4 of capacity, resize to half
- Time
- O(1) to add/remove at end (amortized for allocations for more space), index, or update
- O(n) to insert/remove elsewhere
- Space
- contiguous in memory, so proximity helps performance
- space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)
-
- Description:
- C Code (विडियो) - not the whole video, just portions about Node struct and memory allocation.
- Linked List vs Arrays:
- why you should avoid linked lists (विडियो)
- 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.
- implement (I did with tail pointer & without):
- size() - returns number of data elements in list
- empty() - bool returns true if empty
- value_at(index) - returns the value of the nth item (starting at 0 for first)
- push_front(value) - adds an item to the front of the list
- pop_front() - remove front item and return its value
- push_back(value) - adds an item at the end
- pop_back() - removes end item and returns its value
- front() - get value of front item
- back() - get value of end item
- insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
- erase(index) - removes node at given index
- value_n_from_end(n) - returns the value of the node at nth position from the end of the list
- reverse() - reverses the list
- remove_value(value) - removes the first item in the list with this value
- Doubly-linked List
- Description (विडियो)
- No need to implement
-
- Stacks (विडियो)
- Using Stacks Last-In First-Out (विडियो)
- Will not implement. Implementing with array is trivial.
-
- Using Queues First-In First-Out(विडियो)
- Queue (विडियो)
- Circular buffer/FIFO
- Priority Queues (विडियो)
- 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()
- Implement using fixed-sized array:
- enqueue(value) - adds item at end of available storage
- dequeue() - returns value and removes least recently added element
- empty()
- full()
- Cost:
- 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)
-
-
विडियो:
-
Online Courses:
-
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)
-
-
- Big And Little Endian
- Big Endian Vs Little Endian (विडियो)
- Big And Little Endian Inside/Out (विडियो)
- Very technical talk for kernel devs. Don't worry if most is over your head.
- The first half is enough.
-
- Binary Search (विडियो)
- Binary Search (विडियो)
- detail
- Implement:
- binary search (on sorted array of integers)
- binary search using recursion
-
- 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: &, |, ^, ~, >>, <<
- 2s and 1s complement
- count set bits
- round to next power of 2:
- swap values:
- absolute value:
-
- Series: Core Trees (विडियो)
- Series: Trees (विडियो)
- basic tree construction
- traversal
- manipulation algorithms
- BFS (breadth-first search)
- MIT (विडियो)
- level order (BFS, using queue) time complexity: O(n) space complexity: best: O(1), worst: O(n/2)=O(n)
- DFS (depth-first search)
- MIT (विडियो)
- 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)
-
- Binary Search Tree Review (विडियो)
- Series (विडियो)
- starts with symbol table and goes through BST applications
- Introduction (विडियो)
- MIT (विडियो)
- C/C++:
- Binary search tree - Implementation in C/C++ (विडियो)
- BST implementation - memory allocation in stack and heap (विडियो)
- Find min and max element in a binary search tree (विडियो)
- Find height of a binary tree (विडियो)
- Binary tree traversal - breadth-first and depth-first strategies (विडियो)
- Binary tree: Level Order Traversal (विडियो)
- Binary tree traversal: Preorder, Inorder, Postorder (विडियो)
- Check if a binary tree is binary search tree or not (विडियो)
- Delete a node from Binary Search Tree (विडियो)
- Inorder Successor in a binary search tree (विडियो)
- 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
-
- visualized as a tree, but is usually linear in storage (array, linked list)
- Heap
- Introduction (विडियो)
- Naive Implementations (विडियो)
- Binary Trees (विडियो)
- Tree Height Remark (विडियो)
- Basic Operations (विडियो)
- Complete Binary Trees (विडियो)
- Pseudocode (विडियो)
- Heap Sort - jumps to start (विडियो)
- Heap Sort (विडियो)
- Building a heap (विडियो)
- MIT: Heaps and Heap Sort (विडियो)
- CS 61B Lecture 24: Priority Queues (विडियो)
- 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(i) - 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
- note: using a min heap instead would save operations, but double the space needed (cannot do in-place).
-
- 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.
- Notes on Data Structures and Programming Techniques
- Short course विडियो:
- The Trie: A Neglected Data Structure
- TopCoder - Using Tries
- Stanford Lecture (real world use case) (विडियो)
- MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through)
-
-
Know least one type of balanced binary tree (and know how it's implemented):
-
"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.
-
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 (विडियो)
- AVL Trees (विडियो)
- AVL Tree Implementation (विडियो)
- 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 (विडियो)
- 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 (विडियो)
- Binary View of 23-Tree
- 2-3 Trees (student recitation) (विडियो)
-
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 (विडियो)
- Bottom Up 234-Trees (विडियो)
- Top Down 234-Trees (विडियो)
-
B-Trees
- fun fact: it's a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor)
- In Practice: B-Trees are widely used in databases. Most modern filesystems use B-trees (or Variants). In addition to its use in databases, the B-tree is also used in filesystems to allow quick random access to an arbitrary block in a particular file. The basic problem is turning the file block i address into a disk block (or perhaps to a cylinder-head-sector) address.
- B-Tree
- Introduction to B-Trees (विडियो)
- B-Tree Definition and Insertion (विडियो)
- B-Tree Deletion (विडियो)
- MIT 6.851 - Memory Hierarchy Models (विडियो) - 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)
-
Red/black trees
- In practice: Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time. Not only does this make them valuable in time-sensitive applications such as real-time applications, but it makes them valuable building blocks in other data structures which provide worst-case guarantees; for example, many data structures used in computational geometry can be based on red–black trees, and the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In the version 8 of Java, the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor hashcodes, a Red-Black tree is used.
- Aduni - Algorithms - Lecture 4 (link jumps to starting point) (विडियो)
- Aduni - Algorithms - Lecture 5 (विडियो)
- Black Tree
- An Introduction To Binary Search And Red Black Tree
-
-
- 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
- note: the N or K is the branching factor (max branches)
-
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?")
- 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
- Implement sorts & know best case/worst case, average complexity of each:
-
For heapsort, see Heap data structure above. Heap sort is great, but not stable.
-
Stanford lectures on sorting:
-
Shai Simonson, Aduni.org:
-
Steven Skiena lectures on sorting:
-
UC बर्कले:
-
- Merge sort code:
-
- Quick sort code:
-
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.
-
For curiosity - not required:
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
-
Notes from Yegge:
- There are three basic ways to represent a graph in memory:
- objects and pointers
- matrix
- adjacency list
- Familiarize yourself with each representation and its pros & cons
- BFS and DFS - know their computational complexity, their tradeoffs, and how to implement them in real code
- When asked a question, look for a graph-based solution first, then move on if none.
- There are three basic ways to represent a graph in memory:
-
Skiena Lectures - great intro:
- CSE373 2012 - Lecture 11 - Graph Data Structures (विडियो)
- CSE373 2012 - Lecture 12 - Breadth-First Search (विडियो)
- CSE373 2012 - Lecture 13 - Graph Algorithms (विडियो)
- CSE373 2012 - Lecture 14 - Graph Algorithms (con't) (विडियो)
- CSE373 2012 - Lecture 15 - Graph Algorithms (con't 2) (विडियो)
- CSE373 2012 - Lecture 16 - Graph Algorithms (con't 3) (विडियो)
-
Graphs (review and more):
- 6.006 Single-Source Shortest Paths Problem (विडियो)
- 6.006 Dijkstra (विडियो)
- 6.006 Bellman-Ford (विडियो)
- 6.006 Speeding Up Dijkstra (विडियो)
- Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (विडियो)
- Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (विडियो)
- Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (विडियो)
- Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (विडियो)
- CS 61B 2014 (starting at 58:09) (विडियो)
- CS 61B 2014: Weighted graphs (विडियो)
- Greedy Algorithms: Minimum Spanning Tree (विडियो)
- Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (विडियो)
-
Full Coursera Course:
-
Yegge: If you get a chance, try to study up on fancier algorithms:
- Dijkstra's algorithm - see above - 6.006
- A*
-
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 विडियो 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
You'll get more graph practice in Skiena's book (see Books section below) and the interview books
-
- Stanford lectures on recursion & backtracking:
- when it is appropriate to use it
- how is tail recursion better than not?
-
- 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.
- विडियो:
- the Skiena विडियो can be hard to follow since he sometimes uses the whiteboard, which is too small to see
- Skiena: CSE373 2012 - Lecture 19 - Introduction to Dynamic Programming (विडियो)
- Skiena: CSE373 2012 - Lecture 20 - Edit Distance (विडियो)
- Skiena: CSE373 2012 - Lecture 21 - Dynamic Programming Examples (विडियो)
- Skiena: CSE373 2012 - Lecture 22 - Applications of Dynamic Programming (विडियो)
- Simonson: Dynamic Programming 0 (starts at 59:18) (विडियो)
- Simonson: Dynamic Programming I - Lecture 11 (विडियो)
- Simonson: Dynamic programming II - Lecture 12 (विडियो)
- List of individual DP problems (each is short): Dynamic Programming (विडियो)
- Yale Lecture notes:
- Coursera:
-
- Math Skills: How to find Factorial, Permutation and Combination (Choose) (विडियो)
- Make School: Probability (विडियो)
- Make School: More Probability and Markov Chains (विडियो)
- खान अकादमी:
- Course layout:
- Just the विडियो - 41 (each are simple and each are short):
-
- 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 (विडियो)
- Simonson:
- Skiena:
- Complexity: P, NP, NP-completeness, Reductions (विडियो)
- Complexity: Approximation Algorithms (विडियो)
- Complexity: Fixed-Parameter Algorithms (विडियो)
- Peter Norvik discusses near-optimal solutions to traveling salesman problem:
- Pages 1048 - 1140 in CLRS if you have it.
-
- Computer Science 162 - Operating Systems (25 विडियो):
- for precesses and threads see विडियो 1-11
- Operating Systems and System Programming (विडियो)
- 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
- 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 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
- Processes, Threads, Concurrency issues
- threads in C++ (series - 10 विडियो)
- concurrency in Python (विडियो):
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.
- Computer Science 162 - Operating Systems (25 विडियो):
-
- Considerations from Yegge:
- 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
- scalability
- START HERE: 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 (विडियो)
- System Design Interview - There are a lot of resources in this one. Look through the articles and examples. I put some of them below.
- How to ace a systems design interview
- Numbers Everyone Should Know
- How long does it take to make a context switch?
- Transactions Across Datacenters (विडियो)
- A plain english introduction to CAP Theorem
- Paxos Consensus algorithm:
- Consistent Hashing
- NoSQL Patterns
- Optional: UML 2.0 Series (vido)
- OOSE: Software Dev Using UML and Java (21 विडियो):
- Can skip this if you have a great grasp of OO and OO design practices.
- OOSE: Software Dev Using UML and Java
- SOLID OOP Principles:
- Bob Martin SOLID Principles of Object Oriented and Agile Design (विडियो)
- SOLID Design Patterns in C# (विडियो)
- SOLID Principles (विडियो)
- S - Single Responsibility Principle | Single responsibility to each Object
- O - Open/Closed Principal | On production level Objects are ready for extension for not for modification
- L - Liskov Substitution Principal | Base Class and Derived class follow ‘IS A’ principal
- I - Interface segregation principle | clients should not be forced to implement interfaces they don't use
- D -Dependency Inversion principle | Reduce the dependency In composition of objects.
- Scalability:
- Great overview (विडियो)
- Short series:
- Scalable Web Architecture and Distributed Systems
- पतझड़acies of Distributed Computing Explained
- Pragmatic Programming Techniques
- Jeff Dean - Building Software Systems At Google and Lessons Learned (विडियो)
- Introduction to Architecting Systems for Scale
- Scaling mobile games to a global audience using App Engine and Cloud Datastore (विडियो)
- How Google Does Planet-Scale Engineering for Planet-Scale Infra (विडियो)
- The Importance of Algorithms
- Sharding
- Scale at Facebook (2009)
- Scale at Facebook (2012), "Building for a Billion Users" (विडियो)
- Engineering for the Long Game - Astrid Atkinson Keynote(विडियो)
- 7 Years Of YouTube Scalability Lessons In 30 Minutes
- 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 (विडियो)
- 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
- Egnyte Architecture: Lessons Learned In Building And Scaling A Multi Petabyte Distributed System
- 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?
- Does AMP Counter An Existential Threat To Google?
- 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:
- 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: System Design from HiredInTech
- cheat sheet
- flow:
- 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
- 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
- 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
- Understand the problem and scope:
- 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 function to return the top k requests during past time interval
- Design a picture sharing system
- Design a recommendation system
- Design a URL-shortener system: copied from above
- Design a cache system
- Considerations from Yegge:
-
- These are Google papers and well-known papers.
- Reading all from end to end with full comprehension will likely take more time than you have. I recommend being selective on papers and their sections.
- 1978: Communicating Sequential Processes
- 2003: The Google File System
- replaced by Colossus in 2012
- 2004: MapReduce: Simplified Data Processing on Large Clusters
- mostly replaced by Cloud Dataflow?
- 2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)
- 2012: Google's Colossus
- paper not available
- 2012: AddressSanitizer: A Fast Address Sanity Checker:
- 2013: Spanner: Google’s Globally-Distributed Database:
- 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
-
- suggested by Yegge, from an old Amazon recruiting post: Familiarize yourself with a unix-based code editor
- vi(m):
- emacs:
-
- To cover:
- how unit testing works
- what are mock objects
- what is integration testing
- what is dependency injection
- Agile Software Testing with James Bach (विडियो)
- Open Lecture by James Bach on Software Testing (विडियो)
- Steve Freeman - Test-Driven Development (that’s not what we meant) (विडियो)
- TDD is dead. Long live testing.
- Is TDD dead? (विडियो)
- Video series (152 विडियो) - not all are needed (विडियो)
- Test-Driven Web Development with Python
- Dependency injection:
- How to write tests
- To cover:
-
- Quick UML review (विडियो)
- Learn these patterns:
- strategy
- singleton
- adapter
- prototype
- decorator
- visitor
- factory, abstract factory
- facade
- observer
- proxy
- delegate
- command
- state
- memento
- iterator
- composite
- flyweight
- Chapter 6 (Part 1) - Patterns (विडियो)
- Chapter 6 (Part 2) - Abstraction-Occurrence, General Hierarchy, Player-Role, Singleton, Observer, Delegation (विडियो)
- Chapter 6 (Part 3) - Adapter, Facade, Immutable, Read-Only Interface, Proxy (विडियो)
- Series of विडियो (27 विडियो)
- 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
-
- in an OS, how it works
- can be gleaned from Operating System विडियो
-
- understand what lies beneath the programming APIs you use
- can you implement them?
-
- Search pattern in text (विडियो)
- Rabin-Karp (विडियो):
- Precomputing
- Optimization: Implementation and Analysis
- Knuth-Morris-Pratt (KMP):
- Boyer–Moore string search algorithm
- Coursera: Algorithms on Strings
This section will have shorter विडियो that can you watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.
(More items will be added here)
- Series of 2-3 minutes short subject विडियो (23 विडियो)
- Series of 2-5 minutes short subject विडियो - Michael Sambol (18 विडियो):
- Merge Sort: https://www.youtube.com/watch?v=GCae1WNvnZM
Read and do exercises:
-
The Algorithm Design Manual (Skiena)
- Book (can rent on kindle):
- Half.com is a great resource for textbooks at good prices.
- Answers:
- Errata
Once you've understood everything in the daily plan, and read and done exercises from the the books above, read and do exercises from the books below. Then move to coding challenges (further down below)
Read first:
Read second (recommended by many, but not in Google coaching docs):
- Cracking the Coding Interview, 6th Edition
- If you see people reference "The Google Resume", it was a book replaced by "Cracking the Coding Interview".
These were not suggested by Google but I added because I needed the background knowledge
-
C Programming Language, Vol 2
-
C++ Primer Plus, 6th Edition
-
- Half.com is a great resource for textbooks at good prices.
-
Elements of Programming Interviews
- all code is in C++, if you're looking to use C++ in your interview
- good book on problem solving in general.
Once you've learned your brains out, put those brains to work. Take coding challenges every day, as many as you can.
- Cracking The Coding Interview Set 2 (विडियो):
- एक (थोड़ा) कम भयंकर रिज्यूमे करने के लिए दस सुझाव
- Great stuff at the back of Cracking The Coding Interview
निचे दिए गए विषयो के साथ साथ, अपने २० इंटरव्यू प्रश्न तयार रखे. हर एक प्रश्न के २-३ जवाब तयार रखे. आपने जो हासिल किया हे उसकी कहानी रखे.
- आपको ये नौकरी क्यू चाहिए?
- आपने कौनसी एक कठिन समस्या हल की हैं?
- आपकी सबसे बढ़ी चुनोतिया कोनसी थी?
- आपने देखि हुए सर्वोतम और बुरी संरचनाये?
- किसी मौजूदा गूगल उत्पाद में सुधार के लिए विचार.
- आप अपना काम सर्वोत्तम कैसे कर सकते हो, टीम के साथ या एकेले?
- आपकी कोनसी कुशलता या अनुभव आपके भूमिका में मदतगार होंगे?
- आपने [जॉब क्ष / प्रोजेक्ट य] में सबसे ज्यादा किससे आनद मिला?
- आपकी सबसे बड़ी [जॉब क्ष/ प्रोजेक्ट य] की चुनोती जिसे आपको सामना करना पड़ा?
- [जॉब क्ष / प्रोजेक्ट य] में से सबसे बड़ा बग?
- आपने [जॉब क्ष / प्रोजेक्ट य] में क्या सिखा?
- [जॉब क्ष / प्रोजेक्ट य] में आप क्या सुधार कर सकते थे/ करना चाहते थे?
मेरे कुछ प्रश्न (मुजे पहिलेसेही कुछ जवाब बता हे पर मैं टीम की राय जानना चाहता हूँ):
- आपकी टीम कितनी बड़ी हैं?
- आपकी डेव साइकिल कैसी हैं? क्या आपको वॉटरफॉल/स्प्रिंट/एजाइल पता हैं?
- क्या काम के पीछे भागना पड़ता हैं? या लचीलापन हैं?
- आपकी टीम मैं निर्णय कैसे लिए जाते हैं?
- हर सप्ताह आपकी कितनी बैठके होती हैं?
- क्या आपका काम का मौहोल काम करने मैं मदत करता हैं?
- आप किसपे काम करते हो?
- आपको उसमे क्या पसंद हैं?
- आपका काम जीवन कैसा हैं?
Everything below is my recommendation, not Google's, and you may not have enough time to
learn, watch or read them all. That's ok. I may not either.
-
- खान अकादमी
- more about Markov processes:
- See more in MIT 6.050J Information and Entropy series below.
-
- Intro
- Parity
- Hamming Code:
- Error Checking
-
- also see विडियो below
- make sure to watch information theory विडियो first
- Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (विडियो)
-
- also see विडियो below
- make sure to watch information theory विडियो first
- खान अकादमी Series
- Cryptography: Hash Functions
- Cryptography: Encryption
-
- make sure to watch information theory विडियो first
- कोम्पुतेरफिल(विडियो):
- Compressor Head विडियो
- (optional) Google Developers Live: GZIP is not enough!
-
- 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
-
- "These are somewhat of a cult data structure" - Skiena
- Randomization: Skip Lists (विडियो)
- For animations and a little more detail
-
- Disjoint Set
- UCB 61B - Disjoint Sets; Sorting & selection (विडियो)
- Coursera (not needed since the above video explains it great):
-
- Combination of a binary search tree and a heap
- Treap
- Data Structures: Treaps explained (विडियो)
- Applications in set operations
- [ ] लेखाचित्र अल्गोरिथम IV: ज्यामितीय एल्गोरिदम का परिचय - व्याख्यान ९ - [ ] ज्यामितीय एल्गोरिदम: ग्रैहम और जारविस - व्याख्यान १० - [ ] डिवाइड और कॉन्कर: कॉन्वेक्स हल, माध्य ढूँढना
-
- see विडियो below
-
- Why ML?
- Google's Cloud Machine learning tools (विडियो)
- Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (विडियो)
- Tensorflow (विडियो)
- Tensorflow Tutorials
- [Practical Guide to implementing Neural Networks in Python](using Theano)])http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
- Courses:
- Great starter course: Machine Learning - विडियो only - see विडियो 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
- Metis Online Course ($99 for 2 months)
- Resources:
- Great book: Data Science from Scratch: First Principles with Python: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X
- Data School: http://www.dataschool.io/
- [ ] विडियो: - [ ] Why Learn Go? - [ ] Go Programming - [ ] A Tour of Go - [ ] पुस्तके: - [ ] गो प्रोग्रामिंग का परिचय (ऑनलाइन मुफ्त पढ़े) - [ ] गो प्रोग्रामिंग लैंग्वेज (दोनोवन & केर्निघन) - [ ] बूतकाम्प
--
I added these to reinforce some ideas already presented above, but didn't want to include them
above because it's just too much. It's easy to overdo it on a subject.
You want to get hired in this century, right?
-
और डायनामिक प्रोग्रामिंग (विडियो)
- 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 (विडियो)
-
MIT Probability (mathy, and go slowly, which is good for mathy things) (विडियो):
Sit back and enjoy. "netflix and skill" :P
-
List of individual Dynamic Programming problems (each is short)
-
Excellent - MIT Calculus Revisited: Single Variable Calculus
-
कंप्यूटर विज्ञान ७०, ००१ – वसंत २०१५ - Discrete Mathematics and Probability Theory
-
CSE373 - एल्गोरिदम का विश्लेषण (२५ विडियो)
-
UC बर्कले CS १५२: कंप्यूटर वास्तुकला और इंजीनियरिंग (२० विडियो )
-
MIT ६.०४२J: कंप्यूटर विज्ञान के लिए गणित, पतझड़ २०१० (२५ वीडियो)
-
स्टैनफोर्ड: प्रोग्रामिंग मानदंड (१७ विडियो)
http://www.gainlo.co/ - बड़ी कंपनी के मोक इंटरव्यू
बधाई हो!
सिखाते रहो.
वास्तव में आपकी पढाई कभी ख़तम नहीं होती.