https://github.com/claudio-code/data-structure-algorithm-and-neural-network

:scroll: I' m learning new algorithms and how create small and basic neural network
https://github.com/claudio-code/data-structure-algorithm-and-neural-network

algorithms big-o data-structures neural-network

Last synced: 8 months ago
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:scroll: I' m learning new algorithms and how create small and basic neural network

• Host: GitHub
• URL: https://github.com/claudio-code/data-structure-algorithm-and-neural-network
• Owner: Claudio-code
• License: gpl-3.0
• Created: 2021-10-03T04:53:52.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-10-14T01:56:58.000Z (about 1 year ago)
• Last Synced: 2025-02-15T20:54:18.832Z (10 months ago)
• Topics: algorithms, big-o, data-structures, neural-network
• Language: Java
• Homepage:
• Size: 181 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


  :blue_heart: Data struture, algorithm :blue_heart:





# Asymptotic analysis

- The analysis small values has ignored and focus in big values of **n**.

  - f(n) = n + 10

- Small values of **n**, any algorithm cost little to be executed, even the inefficient algorithms.

- Mathematically speaking, the asymptotic analysis is method to describe behavior of the limits.

- It is desirable to express time consumption of algorithm without depending on language.

- Example, in sequential search, the number of times the query key is compared with the key of each record.

  - Worst case: f(n) = n

  - Mid case: f(n) = (n + 1) / 2

  - Best case: f(n) = 1

- The best case matches with small-time of execution about all possibles size inputs of **n**.

- The worst case matches with big time of execution about all possibles size inputs of **n**.

- The mid-case matches with average time of execution about all possibles size inputs of **n**.

  - It is one distribution of probabilistic about size of set **n**.



- [Stack](#stack)

- [Queue](#queue)

- [Deck](#deck)

- [Linked List](#linked-list)

- [Binary Tree](#binary-tree)

- [Priority Queue](#priority-queue)

- [Binary Heap](#binary-heap)

- [Hash table](#hash-table)

- [Dictionary](#dictionary)

- [Fork](#fork)





## Stack

- They are data structure LIFO type `Last-In-First-Out`, where the last element to be inserted will be the first to be retired. So a stack allows access to one item of data the last inserted. 





## Queue

- Queue is an abstract data structure, somewhat similar to Stacks. Unlike stacks, a queue is open at its ends. One end is always used to insert data (enqueue) and the is used to remove data (dequeue). Queue follows `First-In-First-Out` methodology. 





## Deck

- Deck is an irregular acronym of double-ended queue. Double-ended queues are sequence containers with dynamic sizes that can be expanded or contrasted on both ends.





## Linked List

- A linked list or a current list is a dynamic, linear data structure. It is composed of several cells that are interconnected through pointers, that is, each cell has a pointer that points to the memory address of the next cell.





## Binary tree

- Is a data structure by: Or has no element. Or it has a distinct element, called the root, with two pointers to two different structures, called the left subtree and right subtree.





## Priority queue

- It is a queue where each element has a priority. This priority determines the position of an element in the queue, so it determines who should be removed from the queue first.





## Binary Heap

- The binary Heap is binary three complete or almost.

- Have a Min-heap e Max-heap.

  - Min-heap:

    - The value of each node is greater than or equal to its parent's value, the lowest value is in the root.

  - Max-heap:

    - The value of each node is lowest than or equal to its parent's value, the greater value is in the root.

  - The elements are arranged in heap so that the father always has greater or equal priority than their children's priority.





## Hash table

- Hash table is a data structure that associates lookup keys with values.

- It is also called a scatter or disclosure table.

- Purpose: do a quick search and get the desired value.





## Dictionary

- It is example of Hash table.

- Hash table is representation of associates arrays.

- each value have key associate.

- utilize one function hash to compute one index in slots of arrays.





## Fork

- Fork is entity composted two parts:

  - vertices (nodes)

  - edges (rows)

- The nodes are the little balls (entities you want to model).

- Edges are the the relations of theses entities.

### Adjacency Matrix

- The first form represent one fork called adjacency Matrix.

- It is math structure organized in table form with lines and column.

- Adjacency: close, proximity.




#### Example









- The row ***A*** and column ***E*** has filled with 0 indicating that there is not connection between ***A*** and ***E***.

- The row ***C*** and column ***A*** has filled with 1 indicating that there is connection between ***C*** and ***A***.