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4 | Algorithms & Data Structures Guide 5 |

6 | 7 | #### A guide covering Data Structures and Algorithms such as Neural networks, MLPs, Arrays, Linked Lists, Trees, Hashtables, Stacks, Queues, Heaps, Graphs, Sorting & Searching, and Dynamic programming. 8 | 9 | **Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).** 10 | 11 |

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15 | 16 | # Table of Contents 17 | 18 | 1. [Algorithms & Data Structures Learning Resources](https://github.com/mikeroyal/Algorithms-and-Data-Structures#algorithms--data-structures-learning-resources) 19 | 20 | 2. [Algorithms](https://github.com/mikeroyal/Algorithms-and-Data-Structures#algorithms) 21 | 22 | 3. [Algorithmic Complexity Analysis](https://github.com/mikeroyal/Algorithms-and-Data-Structures#algorithmic-complexity-analysis) 23 | 24 | 4. [Data Structures](https://github.com/mikeroyal/Algorithms-and-Data-Structures#data-structures) 25 | 26 | 27 | ## Algorithms & Data Structures Learning Resources 28 | [Back to the Top](https://github.com/mikeroyal/Algorithms-and-Data-Structures#table-of-contents) 29 | 30 | [Algorithm](https://en.wikipedia.org/wiki/Algorithm) is a procedure of well-defined, computer implementabled instructions, typically to solve a class of specific problems or to perform a computation task such as data processing, automated reasoning, and machine learning. 31 | 32 | [Data Structure](https://www.w3schools.in/data-structures-tutorial/intro/) is a specific way to store and organize data in a computer's memory so that these data can be used efficiently later. Data may be arranged in many different ways, such as the logical or mathematical model for a particular organization of data is termed as a data structure. 33 | 34 | [VisualGo: Visualising data structures and algorithms through animation](https://visualgo.net/en) 35 | 36 | [Data Structure & Algorithms Visualizations](https://www.cs.usfca.edu/~galles/visualization/Algorithms.html) 37 | 38 | [Learning Data Structures and Algorithms courses on Coursera](https://www.coursera.org/specializations/data-structures-algorithms) 39 | 40 | [Learning Data Structures and Algorithms courses on Udemy](https://www.udemy.com/courses/search/?src=ukw&q=Data+Structures+and+Algorithms) 41 | 42 | [Learning Data Structures and Algorithms courses on edX](https://www.edx.org/search?q=Algorithms%20and%20Data%20Structures) 43 | 44 | [Intro to Data Structures and Algorithms courses on Udacity](https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513) 45 | 46 | [Learning Algorithms courses on Khan Academy](https://www.khanacademy.org/computing/computer-science/algorithms) 47 | 48 | [Data Structures and Algorithms course from Harvard University](https://online-learning.harvard.edu/course/data-structures-and-algorithms) 49 | 50 | [Data Structures and Algorithms course from Standford University(CS 166)](http://web.stanford.edu/class/cs166/) 51 | 52 | [Learning Data Structures and Algorithms on CodeChef](https://www.codechef.com/certification/data-structures-and-algorithms/prepare) 53 | 54 | [Learning Algorithms and Data Structures courses on Pluralsight](https://www.pluralsight.com/courses/algorithms-data-structures-part-one) 55 | 56 | [Data structure and algorithms online course on Learnbay.io](https://www.learnbay.io/data-structures-algorithms/) 57 | 58 | [Discrete Probability](https://en.wikibooks.org/wiki/High_School_Mathematics_Extensions/Discrete_Probability) 59 | 60 | [Mathematical Proofs](https://en.wikibooks.org/wiki/High_School_Mathematics_Extensions/Mathematical_Proofs) 61 | 62 | 63 | ## Algorithms 64 | [Back to the Top](https://github.com/mikeroyal/Algorithms-and-Data-Structures#table-of-contents) 65 | 66 | [Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming. 67 | 68 |

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72 | 73 | **Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)** 74 | 75 | [Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems. 76 | 77 |

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81 | 82 | **Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)** 83 | 84 | [Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another. 85 | 86 |

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90 | 91 | **Neural Networks Toplogies. Source: [Fjodor van Veen](https://www.asimovinstitute.org/author/fjodorvanveen/) from [Asimov institute](https://www.asimovinstitute.org/).** 92 | 93 |

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97 | 98 | **Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)** 99 | 100 | [Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes. 101 | 102 |

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106 | 107 | **Convolutional Neural Networks. Source: [CS231n](https://cs231n.github.io/convolutional-networks/#conv)** 108 | 109 | [Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data. 110 | 111 |

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115 | 116 | **Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)** 117 | 118 | [Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation. 119 | 120 |

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124 | 125 | **Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)** 126 | 127 | [Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems. 128 | 129 |

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133 | 134 | **Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)** 135 | 136 | [Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression. 137 | 138 |

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142 | 143 | ***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)** 144 | 145 | [Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features. 146 | 147 |

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202 | 203 | **Order of growth of algorithms specified in Big-O notation. Source: Big-O Cheat Sheet** 204 | 205 |

206 | 207 | **Complexity of operations on Data Structures. Source: Big-O Cheat Sheet** 208 | 209 | 210 |

211 | 212 | **Complexity of Sorting algorithms. Source: Big-O Cheat Sheet** 213 | 214 | 215 | ## Data Structures 216 | [Back to the Top](https://github.com/mikeroyal/Algorithms-and-Data-Structures#table-of-contents) 217 | 218 | [Arrays](https://www.cplusplus.com/doc/tutorial/arrays/) are a series of elements(numbers, booleans, or strings) of the same type placed in phyiscal memory locations that can be individually referenced by adding an index to a unique identifier. 219 | 220 | - [Vector](https://www.cplusplus.com/reference/vector/vector/) is a dynamic array, where the size can be increased when an element is inserted or deleted, with the storage being handled automatically by the container. 221 | 222 | [Linked Lists](https://en.wikipedia.org/wiki/Linked_list) is a linear collection of data elements whose order is not given by their physical placement in memory. Instead, elements in a linked list are linked using pointers. 223 | 224 | - [Singly Linked List](https://www.geeksforgeeks.org/data-structures/linked-list/singly-linked-list/) is a type of linked data structure where each node points to the next node in the sequence. It does not have any pointer that points to the previous node. 225 | 226 | - [Doubly-Linked List](https://en.wikipedia.org/wiki/Doubly_linked_list) is a type of linked data structure in which each node apart from storing its data has two links. A node consists of three parts: node data, a pointer to the next node in sequence (next pointer), pointer to the previous node (previous pointer). 227 | 228 | [Trees](https://en.wikipedia.org/wiki/Tree_(data_structure)) is a nonlinear data structure unlike arrays, linked lists, stacks and queues which are linear data structures. A tree can be empty with no nodes or in other instances a tree's structure consists of one node called the root and 0 or 1 or more subtrees. 229 | 230 | - Basic Tree 231 | - Binary Tree 232 | - Binary Search Tree 233 | - AVL Tree 234 | - Red-Black Tree 235 | - N-ary Tree 236 | 237 | [Hash Tables](https://docs.microsoft.com/en-us/powershell/scripting/learn/deep-dives/everything-about-hashtable?view=powershell-7.1) are a data structure, much like an array, except you store each value (object) using a key. It is a basic key/value store for mapping known as a hash function. 238 | 239 | [Stacks](https://www.thedshandbook.com/stacks/) is a linear data structure that store data in an order known as the Last In First Out (LIFO) order. 240 | 241 | - LIFO (Last In First Out) 242 | - FILO (First In Last Out) 243 | 244 | [Queues](https://www.thedshandbook.com/queues/) is a linear data structure that stores data in an order known as the First In First Out(FIFO) order. 245 | 246 | [Heaps](https://en.wikipedia.org/wiki/Heap_(data_structure)) is a specialized tree-based data structure which is essentially an almost complete tree that satisfies the heap property. 247 | 248 | - Max Heap 249 | - Min-Heap 250 | - Binary Heap 251 | 252 | [Graphs](https://www.thedshandbook.com/graphs/) is a data structure that represent relations between pairs of objects. It consists of nodes (known as vertices) that are connected through links (known as edges). 253 | 254 | ## Contribute 255 | 256 | - [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/Algorithms-and-Data-Structures-Guide/pulls). 257 | 258 | 259 | ## License 260 | [Back to the Top](https://github.com/mikeroyal/Algorithms-and-Data-Structures#table-of-contents) 261 | 262 | Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/). 263 | --------------------------------------------------------------------------------

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4 | Algorithms & Data Structures Guide 5 |