rustyml 0.14.0

A high-performance machine learning & deep learning library in pure Rust, offering ML algorithms and neural network support
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# Summary

[Introduction](./Introduction.md)

- [Getting Started](./Chapter-01/1.0._Getting_Started.md)
  - [What is RustyML](./Chapter-01/1.1._What_is_RustyML.md)
  - [Installation and Feature Flags](./Chapter-01/1.2._Installation_and_Feature_Flags.md)
  - [Working with ndarray](./Chapter-01/1.3._Working_with_ndarray.md)
  - [Your First End-to-End Model](./Chapter-01/1.4._Your_First_End_to_End_Model.md)
  - [The Prelude and Imports](./Chapter-01/1.5._The_Prelude_and_Imports.md)
  - [Error Handling](./Chapter-01/1.6._Error_Handling.md)
- [Classical Machine Learning](./Chapter-02/2.0._Classical_Machine_Learning.md)
  - [Linear Regression](./Chapter-02/2.1._Linear_Regression.md)
  - [Logistic Regression](./Chapter-02/2.2._Logistic_Regression.md)
  - [K-Nearest Neighbors](./Chapter-02/2.3._K_Nearest_Neighbors.md)
  - [Decision Trees](./Chapter-02/2.4._Decision_Trees.md)
  - [Support Vector Machines](./Chapter-02/2.5._Support_Vector_Machines.md)
  - [Linear Discriminant Analysis](./Chapter-02/2.6._Linear_Discriminant_Analysis.md)
  - [KMeans Clustering](./Chapter-02/2.7._KMeans_Clustering.md)
  - [DBSCAN](./Chapter-02/2.8._DBSCAN.md)
  - [Mean Shift](./Chapter-02/2.9._Mean_Shift.md)
  - [Principal Component Analysis](./Chapter-02/2.10._Principal_Component_Analysis.md)
  - [Kernel PCA](./Chapter-02/2.11._Kernel_PCA.md)
  - [t-SNE](./Chapter-02/2.12._t-SNE.md)
  - [Isolation Forest](./Chapter-02/2.13._Isolation_Forest.md)
- [Neural Networks](./Chapter-03/3.0._Neural_Networks.md)
  - [The Sequential Model](./Chapter-03/3.1._The_Sequential_Model.md)
  - [Dense Layers and Activations](./Chapter-03/3.2._Dense_Layers_and_Activations.md)
  - [Loss Functions](./Chapter-03/3.3._Loss_Functions.md)
  - [Optimizers](./Chapter-03/3.4._Optimizers.md)
  - [Convolutional Layers](./Chapter-03/3.5._Convolutional_Layers.md)
  - [Pooling Layers](./Chapter-03/3.6._Pooling_Layers.md)
  - [Recurrent Layers](./Chapter-03/3.7._Recurrent_Layers.md)
  - [Regularization and Normalization Layers](./Chapter-03/3.8._Regularization_and_Normalization_Layers.md)
  - [Saving and Loading Weights](./Chapter-03/3.9._Saving_and_Loading_Weights.md)
- [Data Preprocessing](./Chapter-04/4.0._Data_Preprocessing.md)
  - [Train-Test Split](./Chapter-04/4.1._Train_Test_Split.md)
  - [Standardization and Normalization](./Chapter-04/4.2._Standardization_and_Normalization.md)
  - [Label Encoding](./Chapter-04/4.3._Label_Encoding.md)
- [Model Evaluation](./Chapter-05/5.0._Model_Evaluation.md)
  - [Regression Metrics](./Chapter-05/5.1._Regression_Metrics.md)
  - [Classification Metrics](./Chapter-05/5.2._Classification_Metrics.md)
  - [Clustering Metrics](./Chapter-05/5.3._Clustering_Metrics.md)
- [Math Utilities](./Chapter-06/6.0._Math_Utilities.md)
  - [Distance Metrics](./Chapter-06/6.1._Distance_Metrics.md)
  - [Matrix Multiplication](./Chapter-06/6.2._Matrix_Multiplication.md)
  - [Parallel Reductions](./Chapter-06/6.3._Parallel_Reductions.md)
- [Advanced Topics](./Chapter-07/7.0._Advanced_Topics.md)
  - [Reproducibility and Random Seeds](./Chapter-07/7.1._Reproducibility_and_Random_Seeds.md)
  - [Model Persistence in Depth](./Chapter-07/7.2._Model_Persistence_in_Depth.md)
  - [Performance Tuning and Parallelism](./Chapter-07/7.3._Performance_Tuning_and_Parallelism.md)
  - [Minimal Builds and Modular Integration](./Chapter-07/7.4._Minimal_Builds_and_Modular_Integration.md)