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@ -215,7 +215,7 @@ Używam specjalnej odmiany Markdown GitHub, w tym list zadań do sprawdzania pos
[Więcej na temat Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Nie uważaj, że nie jesteś wystarczająco mądry
## Nie uważaj, że jesteś niewystarczająco mądry
- Odnoszący sukcesy inżynierowie oprogramowania są mądrzy, ale wielu nie ma pewności siebie odnośnie tego, że nie są wystarczająco mądrzy.
- [The myth of the Genius Programmer](https://www.youtube.com/watch?v=0SARbwvhupQ)
@ -392,99 +392,98 @@ aby czuć się z tym komfortowo, np. listy powiązane - otwórz jedną z książ
ale do tego jak zastosować wiedzę. Polecam kilka książek i stron.
Zobacz tutaj, aby uzyskać więcej informacji: [Praktyczne pytania programistyczne](#coding-question-practice)
### 4. Review, review, review
### 4. Przeglądaj, przeglądaj, przeglądaj
I keep a set of cheat sheets on ASCII, OSI stack, Big-O notations, and more. I study them when I have some spare time.
Trzymam zestaw ściąg na ASCII, stos OSI, notacje Big-O i inne. Przeglądam je, kiedy mam trochę wolnego czasu.
Take a break from programming problems for a half hour and go through your flashcards.
Zrób sobie przerwę od problemów programistycznych na pół godziny i przejrzyj swoje fiszki.
### 5. Focus
### 5. Skupienie
There are a lot of distractions that can take up valuable time. Focus and concentration are hard. Turn on some music
without lyrics and you'll be able to focus pretty well.
Istnieje wiele czynników, które mogą zająć cenny czas. Skupienie i koncentracja są trudne. Włącz muzykę bez słów, a będziesz w stanie całkiem dobrze się skupić.
## What you won't see covered
## Czego tutaj nie zobaczysz
These are prevalent technologies but not part of this study plan:
Są to dominujące technologie, ale nie są częścią tego planu nauki:
- SQL
- Javascript
- HTML, CSS, and other front-end technologies
- HTML, CSS, oraz inne technologie frontend
## The Daily Plan
## Plan dzienny
Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement.
Niektóre przedmioty zajmą jeden dzień, a inne kilka dni. Niektórzy dopiero się uczą nie mając nic do zaimplementowania.
Each day I take one subject from the list below, watch videos about that subject, and write an implementation in:
- C - using structs and functions that take a struct * and something else as args.
- C++ - without using built-in types
- C++ - using built-in types, like STL's std::list for a linked list
- Python - using built-in types (to keep practicing Python)
- 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.
Każdego dnia biorę jeden temat z poniższej listy, oglądam filmy na ten temat i piszę implementację w:
- C - używając struktur i funkcji, które mają * i coś jeszcze jako args.
- C++ - bez używania wbudowanych typów
- C++ - używając wbudowanych typów, takich jak z STL np. std::list dla linked list
- Python - używając wbudowanych typów (aby ćwiczyć Python)
- i piszę testy, aby upewnić się, że robię to dobrze, czasem używając prostych instrukcji assert()
- Możesz tak robić z Java lub czymś innym, to po prostu moje podejście.
You don't need all these. You need only [one language for the interview](#pick-one-language-for-the-interview).
Nie potrzebujesz tych wszystkich. Do rozmowy potrzebny jest tylko [jeden język](#pick-one-language-for-the-interview).
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 or Java))
- 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)
Po co kodować w tych wszystkich?
- Ćwiczenia, ćwiczenia, ćwiczenia, dopóki nie mam tego dość, i mogę to zrobić bez problemu (niektórzy mają wiele skrajnych przypadków i szczegółów księżek do zapamiętania)
- Praca w ramach surowych ograniczeń (przydzielanie / zwalnianie pamięci bez pomocy odśmiecania (z wyjątkiem Pythona lub Java))
- Korzystam z wbudowanych typów, więc mam doświadczenie w korzystaniu z wbudowanych narzędzi do użytku w świecie rzeczywistym (nie zamierzam pisać własnej implementacji list powiązanych na produkcji)
I may not have time to do all of these for every subject, but I'll try.
Może nie mam czasu na zrobienie wszystkich tych rzeczy dla każdego przedmiotu, ale próbuję.
You can see my code here:
Możesz zobaczyć moje kody tutaj:
- [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.
Nie musisz zapamiętywać wnętrzności każdego algorytmu.
Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then test it out on a computer.
Napisz kod na tablicy lub papierze, a nie na komputerze. Testuj z niektórymi przykładowymi danymi wejściowymi. Następnie przetestuj na komputerze.
## Prerequisite Knowledge
## Wymagana wiedza
- [ ] **Learn C**
- C is everywhere. You'll see examples in books, lectures, videos, *everywhere* while you're studying.
- [ ] **Nauka C**
- C jest wszędzie. Przykłady znajdziesz w książkach, wykładach, filmach, *wszędzie* podczas nauki.
- [ ] [C Programming Language, Vol 2](https://www.amazon.com/Programming-Language-Brian-W-Kernighan/dp/0131103628)
- This is a short book, but it will give you a great handle on the C language and if you practice it a little
you'll quickly get proficient. Understanding C helps you understand how programs and memory work.
- [answers to questions](https://github.com/lekkas/c-algorithms)
- Jest to krótka książka, ale zapewni doskonałą znajomość języka C i jeśli trochę go przećwiczysz
szybko osiągniesz biegłość. Zrozumienie C pomaga zrozumieć, jak działają programy i pamięć.
- [odpowiedzi na pytania](https://github.com/lekkas/c-algorithms)
- [ ] **How computers process a program:**
- [ ] [How CPU executes a program (video)](https://www.youtube.com/watch?v=XM4lGflQFvA)
- [ ] [How computers calculate - ALU (video)](https://youtu.be/1I5ZMmrOfnA)
- [ ] [Registers and RAM (video)](https://youtu.be/fpnE6UAfbtU)
- [ ] [The Central Processing Unit (CPU) (video)](https://youtu.be/FZGugFqdr60)
- [ ] [Instructions and Programs (video)](https://youtu.be/zltgXvg6r3k)
- [ ] **Jak komputery przetwarzają program:**
- [ ] [Jak procesor wykonuje program (wideo)](https://www.youtube.com/watch?v=XM4lGflQFvA)
- [ ] [Jak komputery liczą - ALU (wideo)](https://youtu.be/1I5ZMmrOfnA)
- [ ] [Rejestry i pamięć RAM (wideo)](https://youtu.be/fpnE6UAfbtU)
- [ ] [Central Processing Unit (CPU) - procesor (wideo)](https://youtu.be/FZGugFqdr60)
- [ ] [Instrukcje i programy (wideo)](https://youtu.be/zltgXvg6r3k)
## Algorithmic complexity / Big-O / Asymptotic analysis
## Złożoność algorytmiczna / Big-O / Analiza asymptotyczna
- Nothing to implement
- There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.
- If some of the lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics videos to get the background knowledge.
- [ ] [Harvard CS50 - Asymptotic Notation (video)](https://www.youtube.com/watch?v=iOq5kSKqeR4)
- [ ] [Big O Notations (general quick tutorial) (video)](https://www.youtube.com/watch?v=V6mKVRU1evU)
- [ ] [Big O Notation (and Omega and Theta) - best mathematical explanation (video)](https://www.youtube.com/watch?v=ei-A_wy5Yxw&index=2&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)
- Nic do implementacji
- Tutaj jest wiele filmów. Po prostu oglądaj wystarczająco długo, aż zrozumiesz. Zawsze możesz wrócić i przejrzeć ponownie.
- Jeśli niektóre wykłady są zbyt matematyczne, możesz zeskoczyć na dół i obejrzeć filmy z matematyki dyskretnej, aby uzyskać podstawową wiedzę.
- [ ] [Harvard CS50 - Notacja asymptotyczna (wideo)](https://www.youtube.com/watch?v=iOq5kSKqeR4)
- [ ] [Big O Notations (ogólny szybki samouczek) (wideo)](https://www.youtube.com/watch?v=V6mKVRU1evU)
- [ ] [Big O Notation (oraz Omega i Theta) - najlepsze wyjaśnienia matematyczne (wideo)](https://www.youtube.com/watch?v=ei-A_wy5Yxw&index=2&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)
- [ ] Skiena:
- [video](https://www.youtube.com/watch?v=gSyDMtdPNpU&index=2&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b)
- [slides](http://www3.cs.stonybrook.edu/~algorith/video-lectures/2007/lecture2.pdf)
- [wideo](https://www.youtube.com/watch?v=gSyDMtdPNpU&index=2&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b)
- [prezentacja](http://www3.cs.stonybrook.edu/~algorith/video-lectures/2007/lecture2.pdf)
- [ ] [A Gentle Introduction to Algorithm Complexity Analysis](http://discrete.gr/complexity/)
- [ ] [Orders of Growth (video)](https://www.coursera.org/lecture/algorithmic-thinking-1/orders-of-growth-6PKkX)
- [ ] [Asymptotics (video)](https://www.coursera.org/lecture/algorithmic-thinking-1/asymptotics-bXAtM)
- [ ] [UC Berkeley Big O (video)](https://archive.org/details/ucberkeley_webcast_VIS4YDpuP98)
- [ ] [UC Berkeley Big Omega (video)](https://archive.org/details/ucberkeley_webcast_ca3e7UVmeUc)
- [ ] [Amortized Analysis (video)](https://www.youtube.com/watch?v=B3SpQZaAZP4&index=10&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)
- [ ] [Illustrating "Big O" (video)](https://www.coursera.org/lecture/algorithmic-thinking-1/illustrating-big-o-YVqzv)
- [ ] [Orders of Growth (wideo)](https://www.coursera.org/lecture/algorithmic-thinking-1/orders-of-growth-6PKkX)
- [ ] [Asymptotics (wideo)](https://www.coursera.org/lecture/algorithmic-thinking-1/asymptotics-bXAtM)
- [ ] [UC Berkeley Big O (wideo)](https://archive.org/details/ucberkeley_webcast_VIS4YDpuP98)
- [ ] [UC Berkeley Big Omega (wideo)](https://archive.org/details/ucberkeley_webcast_ca3e7UVmeUc)
- [ ] [Amortized Analysis (wideo)](https://www.youtube.com/watch?v=B3SpQZaAZP4&index=10&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)
- [ ] [Illustrating "Big O" (wideo)](https://www.coursera.org/lecture/algorithmic-thinking-1/illustrating-big-o-YVqzv)
- [ ] TopCoder (includes recurrence relations and master theorem):
- [Computational Complexity: Section 1](https://www.topcoder.com/community/competitive-programming/tutorials/computational-complexity-section-1/)
- [Computational Complexity: Section 2](https://www.topcoder.com/community/competitive-programming/tutorials/computational-complexity-section-2/)
- [ ] [Cheat sheet](http://bigocheatsheet.com/)
- [ ] [Ściągawka](http://bigocheatsheet.com/)
## Data Structures
## Struktury danych
- ### Arrays
- Implement an automatically resizing vector.
- [ ] Description:
- Zaimplementuj wektor automatycznie zmieniający rozmiar.
- [ ] Opis:
- [Arrays (video)](https://www.coursera.org/learn/data-structures/lecture/OsBSF/arrays)
- [UC Berkeley CS61B - Linear and Multi-Dim Arrays (video)](https://archive.org/details/ucberkeley_webcast_Wp8oiO_CZZE) (Start watching from 15m 32s)
- [Basic Arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/02_04-basicArrays.mp4)
@ -493,11 +492,11 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [Jagged Arrays (video)](https://www.youtube.com/watch?v=1jtrQqYpt7g)
- [Jagged Arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/02_06-jaggedArrays.mp4)
- [Resizing arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/03_01-resizableArrays.mp4)
- [ ] Implement a vector (mutable array with automatic resizing):
- [ ] Zaimplementuj vector (mutable array z automatycznym zmienianiem rozmiaru):
- [ ] 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
- potraf zaalokować int array pod maską, bez używania gotowych funkcji
- zacznij z 16, lub jeśli liczba początkowa jest większa, użyj potęgi 2 - 16, 32, 64, 128
- [ ] size() - number of items
- [ ] capacity() - number of items it can hold
- [ ] is_empty()
@ -510,16 +509,16 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] 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
- po osiągnięciu pojemności zmień rozmiar, aby podwoić rozmiar
- podczas usuwania elementu, jeśli rozmiar wynosi 1/4 pojemności, przeskaluj do połowy
- [ ] Time
- O(1) to add/remove at end (amortized for allocations for more space), index, or update
- O(1) to add/remove na koniec (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)
- ### Linked Lists
- ### Listy łączone
- [ ] Description:
- [ ] [Singly Linked Lists (video)](https://www.coursera.org/learn/data-structures/lecture/kHhgK/singly-linked-lists)
- [ ] [CS 61B - Linked Lists 1 (video)](https://archive.org/details/ucberkeley_webcast_htzJdKoEmO0)
@ -549,16 +548,16 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] 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
- [ ] Lista podwójnie łączona
- [Description (video)](https://www.coursera.org/learn/data-structures/lecture/jpGKD/doubly-linked-lists)
- No need to implement
- ### Stack
- ### Stos
- [ ] [Stacks (video)](https://www.coursera.org/learn/data-structures/lecture/UdKzQ/stacks)
- [ ] [Using Stacks Last-In First-Out (video)](https://archive.org/details/0102WhatYouShouldKnow/05_01-usingStacksForLast-inFirst-out.mp4)
- [ ] Will not implement. Implementing with array is trivial.
- ### Queue
- ### Kolejka
- [ ] [Using Queues First-In First-Out(video)](https://archive.org/details/0102WhatYouShouldKnow/05_03-usingQueuesForFirst-inFirst-out.mp4)
- [ ] [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq)
- [ ] [Circular buffer/FIFO](https://en.wikipedia.org/wiki/Circular_buffer)
@ -607,7 +606,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- get(key)
- remove(key)
## More Knowledge
## Więcej wiedzy
- ### Binary search
- [ ] [Binary Search (video)](https://www.youtube.com/watch?v=D5SrAga1pno)
@ -617,7 +616,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- binary search (on sorted array of integers)
- binary search using recursion
- ### Bitwise operations
- ### Operacje bitowe
- [ ] [Bits cheat sheet](https://github.com/jwasham/coding-interview-university/blob/master/extras/cheat%20sheets/bits-cheat-cheet.pdf) - 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](https://en.wikipedia.org/wiki/Word_(computer_architecture))
@ -642,9 +641,9 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] absolute value:
- [Absolute Integer](https://bits.stephan-brumme.com/absInteger.html)
## Trees
## Drzewa
- ### Trees - Notes & Background
- ### Drzewa - uwagi & zarys
- [ ] [Series: Core Trees (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/ovovP/core-trees)
- [ ] [Series: Trees (video)](https://www.coursera.org/learn/data-structures/lecture/95qda/trees)
- basic tree construction
@ -694,7 +693,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] delete_value
- [ ] get_successor // returns next-highest value in tree after given value, -1 if none
- ### Heap / Priority Queue / Binary Heap
- ### Sterta / kolejka priorytetowa / sterta binarna
- visualized as a tree, but is usually linear in storage (array, linked list)
- [ ] [Heap](https://en.wikipedia.org/wiki/Heap_(data_structure))
- [ ] [Introduction (video)](https://www.coursera.org/learn/data-structures/lecture/2OpTs/introduction)
@ -723,9 +722,9 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] 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).
## Sorting
## Sortowanie
- [ ] Notes:
- [ ] Uwagi:
- 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?")
@ -798,11 +797,11 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
As a summary, here is a visual representation of [15 sorting algorithms](https://www.youtube.com/watch?v=kPRA0W1kECg).
If you need more detail on this subject, see "Sorting" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects)
## Graphs
## Grafy
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
- Notes:
- Uwagi:
- There are 4 basic ways to represent a graph in memory:
- objects and pointers
- adjacency matrix
@ -812,11 +811,11 @@ Graphs can be used to represent many problems in computer science, so this secti
- 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.
- [ ] MIT(videos):
- [ ] MIT (filmy):
- [ ] [Breadth-First Search](https://www.youtube.com/watch?v=s-CYnVz-uh4&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=13)
- [ ] [Depth-First Search](https://www.youtube.com/watch?v=AfSk24UTFS8&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=14)
- [ ] Skiena Lectures - great intro:
- [ ] Wykłady Skiena - świetne wprowadzenie:
- [ ] [CSE373 2012 - Lecture 11 - Graph Data Structures (video)](https://www.youtube.com/watch?v=OiXxhDrFruw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=11)
- [ ] [CSE373 2012 - Lecture 12 - Breadth-First Search (video)](https://www.youtube.com/watch?v=g5vF8jscteo&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=12)
- [ ] [CSE373 2012 - Lecture 13 - Graph Algorithms (video)](https://www.youtube.com/watch?v=S23W6eTcqdY&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=13)
@ -860,7 +859,7 @@ Graphs can be used to represent many problems in computer science, so this secti
## Even More Knowledge
- ### Recursion
- ### Rekursja
- [ ] Stanford lectures on recursion & backtracking:
- [ ] [Lecture 8 | Programming Abstractions (video)](https://www.youtube.com/watch?v=gl3emqCuueQ&list=PLFE6E58F856038C69&index=8)
- [ ] [Lecture 9 | Programming Abstractions (video)](https://www.youtube.com/watch?v=uFJhEPrbycQ&list=PLFE6E58F856038C69&index=9)
@ -1016,7 +1015,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Agile Software Testing with James Bach (video)](https://www.youtube.com/watch?v=SAhJf36_u5U)
- [ ] [Open Lecture by James Bach on Software Testing (video)](https://www.youtube.com/watch?v=ILkT_HV9DVU)
- [ ] [Steve Freeman - Test-Driven Development (that’s not what we meant) (video)](https://vimeo.com/83960706)
- [slides](http://gotocon.com/dl/goto-berlin-2013/slides/SteveFreeman_TestDrivenDevelopmentThatsNotWhatWeMeant.pdf)
- [prezentacja](http://gotocon.com/dl/goto-berlin-2013/slides/SteveFreeman_TestDrivenDevelopmentThatsNotWhatWeMeant.pdf)
- [ ] Dependency injection:
- [ ] [video](https://www.youtube.com/watch?v=IKD2-MAkXyQ)
- [ ] [Tao Of Testing](http://jasonpolites.github.io/tao-of-testing/ch3-1.1.html)
@ -1108,7 +1107,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- simplicity and robustness
- tradeoffs
- performance analysis and optimization
- [ ] **START HERE**: [The System Design Primer](https://github.com/donnemartin/system-design-primer)
- [ ] **ZACZNIJ TUTAJ**: [The System Design Primer](https://github.com/donnemartin/system-design-primer)
- [ ] [System Design from HiredInTech](http://www.hiredintech.com/system-design/)
- [ ] [How Do I Prepare To Answer Design Questions In A Technical Inverview?](https://www.quora.com/How-do-I-prepare-to-answer-design-questions-in-a-technical-interview?redirected_qid=1500023)
- [ ] [8 Things You Need to Know Before a System Design Interview](http://blog.gainlo.co/index.php/2015/10/22/8-things-you-need-to-know-before-system-design-interviews/)
@ -1273,7 +1272,7 @@ Supplemental:
See [Book List above](#book-list)
## Coding exercises/challenges
## Zadania/wyzwania programistyczne
Once you've learned your brains out, put those brains to work.
Take coding challenges every day, as many as you can.
@ -1318,13 +1317,13 @@ Mock Interviews:
- [Refdash: Mock interviews and expedited interviews](https://refdash.com/) - also help candidates fast track by skipping multiple interviews with tech companies.
## Once you're closer to the interview
## Gdy już jesteś bliżej rozmowy rekrutacyjnej
- Cracking The Coding Interview Set 2 (videos):
- [Cracking The Code Interview](https://www.youtube.com/watch?v=4NIb9l3imAo)
- [Cracking the Coding Interview - Fullstack Speaker Series](https://www.youtube.com/watch?v=Eg5-tdAwclo)
## Your Resume
## Twoje CV
- See Resume prep items in Cracking The Coding Interview and back of Programming Interviews Exposed
@ -1347,7 +1346,7 @@ Have a story, not just data, about something you accomplished.
- What did you learn at [job x / project y]?
- What would you have done better at [job x / project y]?
## Have questions for the interviewer
## Pytania dla rekrutera
Some of mine (I already may know answer to but want their opinion or team perspective):
@ -1384,7 +1383,7 @@ You're never really done.
---
## Additional Books
## Dodatkowe książki
These are here so you can dive into a topic you find interesting.
@ -1444,18 +1443,18 @@ You're never really done.
- 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
## Dodatkowe materiały
I added them to help you become a well-rounded software engineer, and to be aware of certain
technologies and algorithms, so you'll have a bigger toolbox.
Dodałem je, aby pomóc Ci zostać wszechstronnym inżynierem oprogramowania i mieć świadomość
technologii i algorytiki, dzięki czemu będziesz mieć większy zestaw narzędzi.
- ### Compilers
- ### Kompilatory
- [How a Compiler Works in ~1 minute (video)](https://www.youtube.com/watch?v=IhC7sdYe-Jg)
- [Harvard CS50 - Compilers (video)](https://www.youtube.com/watch?v=CSZLNYF4Klo)
- [C++ (video)](https://www.youtube.com/watch?v=twodd1KFfGk)
- [Understanding Compiler Optimization (C++) (video)](https://www.youtube.com/watch?v=FnGCDLhaxKU)
- ### Emacs and vi(m)
- ### Emacs oraz vi(m)
- Familiarize yourself with a unix-based code editor
- vi(m):
- [Editing With vim 01 - Installation, Setup, and The Modes (video)](https://www.youtube.com/watch?v=5givLEMcINQ&index=1&list=PL13bz4SHGmRxlZVmWQ9DvXo1fEg4UdGkr)
@ -1476,7 +1475,7 @@ You're never really done.
- [Writing C Programs With Emacs](http://www.cs.yale.edu/homes/aspnes/classes/223/notes.html#Writing_C_programs_with_Emacs)
- [(maybe) Org Mode In Depth: Managing Structure (video)](https://www.youtube.com/watch?v=nsGYet02bEk)
- ### Unix command line tools
- ### Narzędzia wiersza poleceń systemu Unix
- I filled in the list below from good tools.
- bash
- cat
@ -1490,7 +1489,7 @@ You're never really done.
- [strace](https://en.wikipedia.org/wiki/Strace)
- [tcpdump](https://danielmiessler.com/study/tcpdump/)
- ### Information theory (videos)
- ### Teoria informacji (filmy)
- [Khan Academy](https://www.khanacademy.org/computing/computer-science/informationtheory)
- more about Markov processes:
- [Core Markov Text Generation](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/waxgx/core-markov-text-generation)
@ -1506,19 +1505,19 @@ You're never really done.
- [Error correction](https://www.youtube.com/watch?v=JAMLuxdHH8o)
- [Error Checking](https://www.youtube.com/watch?v=wbH2VxzmoZk)
- ### Entropy
- ### Entropia
- also see videos below
- make sure to watch information theory videos first
- [Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)](https://youtu.be/JnJq3Py0dyM?t=176)
- ### Cryptography
- ### Kryptografia
- also see videos below
- make sure to watch information theory videos first
- [Khan Academy Series](https://www.khanacademy.org/computing/computer-science/cryptography)
- [Cryptography: Hash Functions](https://www.youtube.com/watch?v=KqqOXndnvic&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=30)
- [Cryptography: Encryption](https://www.youtube.com/watch?v=9TNI2wHmaeI&index=31&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- ### Compression
- ### Kompresja
- make sure to watch information theory videos first
- Computerphile (videos):
- [Compression](https://www.youtube.com/watch?v=Lto-ajuqW3w)
@ -1530,7 +1529,7 @@ You're never really done.
- [Compressor Head videos](https://www.youtube.com/playlist?list=PLOU2XLYxmsIJGErt5rrCqaSGTMyyqNt2H)
- [(optional) Google Developers Live: GZIP is not enough!](https://www.youtube.com/watch?v=whGwm0Lky2s)
- ### Computer Security
- ### Bezpieczeństwo komputerowe
- [MIT (23 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- [Introduction, Threat Models](https://www.youtube.com/watch?v=GqmQg-cszw4&index=1&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- [Control Hijacking Attacks](https://www.youtube.com/watch?v=6bwzNg5qQ0o&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh&index=2)
@ -1545,7 +1544,7 @@ You're never really done.
- [Network Protocols](https://www.youtube.com/watch?v=QOtA76ga_fY&index=12&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- [Side-Channel Attacks](https://www.youtube.com/watch?v=PuVMkSEcPiI&index=15&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- ### Garbage collection
- ### Garbage collection - Odśmiecanie pamięci
- [GC in Python (video)](https://www.youtube.com/watch?v=iHVs_HkjdmI)
- [Deep Dive Java: Garbage Collection is Good!](https://www.infoq.com/presentations/garbage-collection-benefits)
- [Deep Dive Python: Garbage Collection in CPython (video)](https://www.youtube.com/watch?v=P-8Z0-MhdQs&list=PLdzf4Clw0VbOEWOS_sLhT_9zaiQDrS5AR&index=3)
@ -1581,14 +1580,14 @@ You're never really done.
- [A* Pathfinding Tutorial (video)](https://www.youtube.com/watch?v=KNXfSOx4eEE)
- [A* Pathfinding (E01: algorithm explanation) (video)](https://www.youtube.com/watch?v=-L-WgKMFuhE)
- ### Fast Fourier Transform
- ### Szybka transformata Fouriera
- [An Interactive Guide To The Fourier Transform](https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/)
- [What is a Fourier transform? What is it used for?](http://www.askamathematician.com/2012/09/q-what-is-a-fourier-transform-what-is-it-used-for/)
- [What is the Fourier Transform? (video)](https://www.youtube.com/watch?v=Xxut2PN-V8Q)
- [Divide & Conquer: FFT (video)](https://www.youtube.com/watch?v=iTMn0Kt18tg&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=4)
- [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/)
- ### Bloom Filter
- ### Filtr Blooma
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
- [Bloom Filters (video)](https://www.youtube.com/watch?v=-SuTGoFYjZs)
- [Bloom Filters | Mining of Massive Datasets | Stanford University (video)](https://www.youtube.com/watch?v=qBTdukbzc78)
@ -1743,11 +1742,11 @@ You're never really done.
- [Geometric Algorithms: Graham & Jarvis - Lecture 10](https://www.youtube.com/watch?v=J5aJEcOr6Eo&index=10&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)
- [Divide & Conquer: Convex Hull, Median Finding](https://www.youtube.com/watch?v=EzeYI7p9MjU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=2)
- ### Discrete math
- see videos below
- ### Matematyka dyskretna
- zobacz wideo poniżej
- ### Machine Learning
- Why ML?
- ### Machine Learning - Uczenie maszynowe
- Czemu ML?
- [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70)
- [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY)
- [Deep Learning and Understandability versus Software Engineering and Verification by Peter Norvig](https://www.youtube.com/watch?v=X769cyzBNVw)
@ -1775,7 +1774,7 @@ You're never really done.
---
## Additional Detail on Some Subjects
## Dodatkowe szczegóły na niektóre tematy
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.
@ -1805,7 +1804,7 @@ You're never really done.
- [Path Compression](https://www.coursera.org/learn/data-structures/lecture/Q9CVI/path-compression)
- [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional)
- **More Dynamic Programming** (videos)
- **Bardziej dynamiczne programowanie** (wideos)
- [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=OQ5jsbhAv_M&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=19)
- [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=ENyox7kNKeY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=20)
- [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=ocZMDMZwhCY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=21)
@ -1814,11 +1813,11 @@ You're never really done.
- [6.046: Dynamic Programming: All-Pairs Shortest Paths](https://www.youtube.com/watch?v=NzgFUwOaoIw&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=15)
- [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12)
- **Advanced Graph Processing** (videos)
- **Zaawansowane przetwarzanie wykresów** (wideos)
- [Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=mUBmcbbJNf4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=27)
- [Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=kQ-UQAzcnzA&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=28)
- MIT **Probability** (mathy, and go slowly, which is good for mathy things) (videos):
- MIT **Prawdopodobieństwo** (matma, i idź po mału, co jest dobre dla takich rzeczy) (wideos):
- [MIT 6.042J - Probability Introduction](https://www.youtube.com/watch?v=SmFwFdESMHI&index=18&list=PLB7540DEDD482705B)
- [MIT 6.042J - Conditional Probability](https://www.youtube.com/watch?v=E6FbvM-FGZ8&index=19&list=PLB7540DEDD482705B)
- [MIT 6.042J - Independence](https://www.youtube.com/watch?v=l1BCv3qqW4A&index=20&list=PLB7540DEDD482705B)