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.

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