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πŸ€– My solutions to practice labs in Neural Network labs in Computer engineer department at Cairo University

License: MIT License

Jupyter Notebook 99.92% Python 0.08%
bay cufe-2024 labs neural-network python adaboostclassifier bayes-classifier classification component-analysis density-estimation implementation

neural-network-labs's Introduction

πŸ€– Neural Network Labs


πŸ“ Table of Contents


πŸ“™ Overview

  • My solutions to practice labs in Neural Network labs in Computer engineer department at Cairo University

  • The Content
    • Python Review (Link)
      • Operators
      • DataTypes
      • Containers
      • Flow Control Statements
      • Functions
      • Numpy
      • Pandas
      • MatPlot Lib
    • Density Estimation Techniques (Link)
      • Classification of multivariate data
      • The Bayes classification rule
    • Bayesian Classifier (Link)
      • Implement and assess the performance of the Bayesian Classifier
      • Machine Learning Terminlology
    • Density Estimation (Link)
      • Implement one of the density estimation methods
      • Choose a suitable bump function (Parzen window)
    • Principal Component Analysis (Link)
      • Implementing PCA
      • Dimensionality Reduction with PCA
      • Projecting the data onto the principal components
      • Reconstructing an approximation of the data
      • Visualizing the projections
    • AdaBoost Classifier (Link)
      • Classifiers Boosting Algorithms
      • Implementing Adaboost_classifier

πŸ‘‘ Contributors



Eslam Ashraf


πŸ”’ License

Note: This software is licensed under MIT License, See License for more information Β©EslamAsHhraf.

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