Merge branch 'main' of https://github.com/novak-99/MLPP into main

README.md

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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