Release v1.0.7

What's Changed

• Implement Matrix Multiplication Using Strassen Algorithm #75 by @Samyssmile in #93
• tests(): Implement Multi-Threaded Matrix Multiplication #74 by @Samyssmile in #94
• feat():NVIDIA CUDA Support for Matrix Multiplication #36 by @Samyssmile in #95
• feat(#74) add implementation of multi-threaded matrix multiplication by @acsolle66 in #98
• refracor(): refractor code in classic MatrixArithmetic by @acsolle66 in #99
• chore(): refactoring by @Samyssmile in #100
• Chore/noheadcsv by @Samyssmile in #107
• feat(#102): Implementation of CudaMatrixVectorProduct #102 by @Samyssmile in #106
• feat(#101): implement MatrixVectorProduct by @acsolle66 in #110
• Augmentation Baseline Implemented by @Samyssmile in #113
• Feat/augmentation by @Samyssmile in #118
• Batch Processing and Random Augmentation for EDUX Image Augmentation by @Samyssmile in #119
• Implemented BrightnessAugmentation.java by @sam-wmd in #121
• chore(): shaman image added by @Samyssmile in #124
• Added a constructor to ResizeAugmentation.java with scaleFactor by @sam-wmd in #125
• refractor(#108): refractor dataProcessor class by @acsolle66 in #127
• Implementation of Single Image Augmentation Example by @fredabisai in #128
• release 1.0.7 by @Samyssmile in #129
• Create Median Imputation feature by @fredabisai in #130
• test(): new benchmark added by @Samyssmile in #132
• Remove ejml dependencies by @fredabisai in #133
• Change CudaMatrixArithmetic implementation to Singleton pattern by @fredabisai in #135
• Update Readme by @Samyssmile in #136

New Contributors

• @sam-wmd made their first contribution in #121
• @fredabisai made their first contribution in #128

Full Changelog: 1.0.6...1.0.7

Release v1.0.6

New Features

• Imputation Strategy
  In data analysis and machine learning, an "Imputation Strategy" refers to a method for handling missing or incomplete data within a dataset. The strategy involves substituting missing values with estimated ones based on various algorithms or statistical methods.

What's Changed

Main Features

• Issue 25 - refractored java files by @acsolle66 in #37
• Add example how to use RandomForest by @acsolle66 in #45
• Multilayerperceptronexample by @kisharnath in #52
• Multilayerperceptron example added with seaborndataset by @kisharnath in #51
• chore(53): Add github pages to the project #53 by @Samyssmile in #54
• chore(): CONTRIBUTING.md added by @Samyssmile in #56
• Pages by @Samyssmile in #57
• chore(): GitHub Page by @Samyssmile in #59
• chore(): GitHub Page by @Samyssmile in #60
• feat(#64): Drop Custom Data Preparation #64 by @Samyssmile in #66
• Simple math classes (improvements needed) by @GolemIron in #65
• chore(): SVM Iris example by @Samyssmile in #68
• chore(): random forest iris example added by @Samyssmile in #69
• fix(#70): correct leaf node counting in DecisionTree implementation by @Samyssmile in #71
• Feat(#23) Replace filterIncompleteRecords boolean with Imputation Enum for Enhanced Data Handling by @acsolle66 in #72
• chore():Reformat Codebase with Google Formatter #78 by @Samyssmile in #79
• chore(): Write JUnit Tests #80 by @Samyssmile in #81
• chore(#76): add examples for all ML Algorithms with Seaborn dataset by @acsolle66 in #82
• Release 1.0.6 by @Samyssmile in #83

New Contributors

• @acsolle66 made their first contribution in #37
• @kisharnath made their first contribution in #52

Full Changelog: 1.0.5...1.0.6

Release v1.0.5

What's Changed

• Update README.MD by @Samyssmile in #8
• Update README.MD by @Samyssmile in #9
• Merge pull request #9 from Samyssmile/Samyssmile-patch-4 by @Samyssmile in #10
• IntelliJ cant find edux module #18 by @Samyssmile in #19
• Updating readme file by @Samyssmile in #20
• Closes #14 by @GolemIron in #21
• feat(#17): Add New Example for knn: Utilizing the Seaborn Penguin Dat… by @Samyssmile in #22
• Create LICENSE by @Samyssmile in #24
• test(26): Write jUnit Tests for Multilayer NeuralNetwork #26 by @Samyssmile in #27
• Write jUnit Test for decision trees #28 by @Samyssmile in #29
• feature(#11): Implementation of Randrom Forest #11 by @Samyssmile in #30
• README update by @Samyssmile in #31
• chore(deps): bump actions/setup-java from 2 to 3 by @dependabot in #32
• chore(deps): bump actions/checkout from 2 to 4 by @dependabot in #33
• Feature/unified classifier by @Samyssmile in #38
• chore(): docs by @Samyssmile in #39
• chore(): release 1.0.5 by @Samyssmile in #40

New Contributors

• @GolemIron made their first contribution in #21
• @dependabot made their first contribution in #32

Full Changelog: 1.0.4...1.0.5

Release v1.0.4

Release 1.0.4

New Features:

• Implemented SVM Implementation (feat(): SVM Implementation).
• Implemented Decision Tree (feat(): Decision Tree implemented).

Chores and Enhancements:

• Prepared the publishing of Version 1.0.3 (chore(): Publish version 1.0.3).
• Renamed actions (chore(): Renaming actions).
• Added status badges (chore(): Status Badges added).
• Made adjustments to CodeQL (chore(): Adjusting codeql).
• Added Unit Tests for Weight Initialization (chore(): Unit Tests for Weight Initialization added).
• Added Multiplayer Perceptron Example (chore(): Multiplayer Perceptron Example added).
• Created build pipeline for releases (chore(): building release pipe).
• Various cleanup tasks performed (chore(): cleanup).
• Merges and Branching:

Several pull requests and branches have been merged or deleted, including the merging of code adjustments for CodeQL and SVM.
Please note that some of these changes may be internal or relevant to the development environment and may not be directly visible to end-users.

Release v1.0.0

Major

Create dependabot.yml