T-King-00

T-King-00's Stars

• Husna-POYRAZ/library-management-system

  With the library management system with C # and SQL Database Server, users can easily do their online transactions.

  Language:C#6539

• motazsaad/ai-csci4304

  AI Course

  Language:Jupyter Notebook1916

• T-King-00/text2uml

  1

• Maiiadel/UID

  Language:TypeScript2

• aeddamir/text-to-UML_POS

  Language:Jupyter Notebook2

• Jcharis/Python-Machine-Learning

  Tutorials on Machine Learning and Deep Learning with Python

  Language:Jupyter Notebook125140

• MeMartijn/text2uml

  Language:Jupyter Notebook33

• reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x

  In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

  Language:Jupyter Notebook5525

• akshaytheau/Data-Science

  This repo contains Data Science code snippet

  Language:Jupyter Notebook8346

• MarcelRobeer/User-Story-Statistics

  Language:Python1

• laxmimerit/machine_learning_examples

  A collection of machine learning examples and tutorials.

  Language:Python811

• mahmoud208/Multiclass-Arabic-Text-classification-Using-Keras

  (Multiclass Arabic Text classification Using Keras)

  Language:Jupyter Notebook54

• songyang-dev/uml-translation-3step

  Official repository for the paper Yang et al. 2022

  Language:Python82

• souidiyassine/text_to_uml_nlp

  Language:Jupyter Notebook11

• CAU20-OSS-Project-Team-5/ttum

  CAU20 OSS Project TTUM (Text-to-UML)

  Language:Jupyter Notebook23

• dhorseman1710/Decrypt-PluralSight-GUI

  121

• dohsimpson/kubernetes-doc-pdf

  Kubernetes PDF Documentation

  Language:Python289168

• tecoholic/ner-annotator

  Named Entity Recognition (NER) Annotation tool for SpaCy. Generates Traning Data as a JSON which can be readily used.

  Language:Vue566165

• songyang-dev/uml-classes-and-specs

  Repository that contains the data used for "Extraction of UML Class Diagrams from Natural Language Specification" (Yang et al. 2022)

  83

• MarcelRobeer/StoryMiner

  Part of Visual Narrator

  Language:Python21

• songyang-dev/UML-hand-class-diagrams

  A collection of about 100 UML class diagrams in yUML and their paired English description. Perfect for machine learning!

  Language:C4

• Maiiadel/frontend-gp

  Language:JavaScript2

• omenking/aws-bootcamp-cruddur-2023

  Language:JavaScript466747

• iqtheengineer/aws-bootcamp-cruddur-2023

  Language:JavaScript2

• ludev-ng-nl/ngUML.nlp.usecase

  NLP Models for Use Case extraction

  Language:Python1

• btholt/complete-intro-to-react-v8

  The Complete Intro to React, as taught by Brian Holt on Frontend Masters

  Language:JavaScript1.8k204

• RDA-DMP-Common/user-stories

  This repository is for collection of user stories describing evolving requirements of stakeholders involved in the research data lifecycle.

  12

• franktseng0718/User-Stories-and-Acceptance-criteria

  13

• mikecrabb/ac31007-user-stories_workshop

  23

• Selectus2/UMLGenNLP

  Software requirements are often specified in natural language (NL). However, requirements specified in NL can often be ambiguous, incomplete, and inconsistent. Moreover, the interpretation and understanding of anything described in NL has the potential of being influenced by geographical, psychological and sociological factors. It is the job of requirements analysts to detect and fix any potential ambiguities, inconsistencies, and incompleteness in the requirements specifications documents. We are going to use Natural language processing techniques to extract quantitative data from the unstructured requirements and these are given to Generative adversarial Networks to generate the UML diagram

  Language:Python2