MLPP/README.md

# ML++

Machine learning is a vast and exiciting discipline, garnering attention from specialists of many fields. Unfortunately, for C++ programmers and enthusiasts, there appears to be a lack of support for this magnificient language in the field of machine learning. As a consequence, this library was created in order to fill that void and give C++ a true foothold in the ML sphere to act as a crossroad between low level developers and machine learning engineers. 

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## Contents of the Library
1. ***Regression***
    1. Linear Regression 
    2. Logistic Regression
    3. Softmax Regression
    4. Exponential Regression
    5. Probit Regression
    6. CLogLog Regression
    7. Tanh Regression
2. ***Deep, Dynamically Sized Neural Networks***
    1. Possible Activation Functions
        - Linear
        - Sigmoid
        - Swish
        - Softplus
        - CLogLog
        - Gaussian CDF
        - GELU
        - Unit Step 
        - Sinh
        - Cosh
        - Tanh
        - Csch
        - Sech
        - Coth
        - Arsinh
        - Arcosh
        - Artanh
        - Arcsch
        - Arsech
        - Arcoth
    2. Possible Loss Functions
        - MSE
        - RMSE 
        - MAE
        - MBE
        - Log Loss
        - Cross Entropy
        - Hinge Loss
    3. Possible Regularization Methods
        - Lasso
        - Ridge
        - ElasticNet
    4. Possible Weight Initialization Methods
        - Uniform 
        - Xavier Normal
        - Xavier Uniform
        - He Normal
        - He Uniform
3. ***Prebuilt Neural Networks***
    1. Multilayer Peceptron
    2. Autoencoder
    3. Softmax Network
4. ***Natural Language Processing***
    1. Word2Vec (Continous Bag of Words, Skip-N Gram)
    2. Stemming
    3. Bag of Words
    4. TFIDF
    5. Tokenization 
    6. Auxiliary Text Processing Functions
5. ***Computer Vision***
    1. The Convolution Operation
    2. Max, Min, Average Pooling
    3. Global Max, Min, Average Pooling
    4. Prebuilt Feature Detectors
        - Horizontal/Vertical Prewitt Filter
        - Horizontal/Vertical Sobel Filter
        - Horizontal/Vertical Scharr Filter
        - Horizontal/Vertical Roberts Filter
6. ***Principal Component Analysis***
7. ***Naive Bayes Classifiers***
    1. Multinomial Naive Bayes
    2. Bernoulli Naive Bayes 
    3. Gaussian Naive Bayes
8. ***K-Means***
9. ***k-Nearest Neighbors***
10. ***Outlier Finder (Using z-scores)***
11. ***Linear Algebra Module***
12. ***Statistics Module***
13. ***Data Processing Module***
    1. Setting and Printing Datasets 
    2. Feature Scaling 
    3. Mean Normalization
    4. One Hot Representation 
    5. Reverse One Hot Representation