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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
+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