ctgk/PRML

Features

• Machine Learning Implementations - Provides code implementations of core machine learning algorithms, including supervised and unsupervised statistical modeling.
• Pattern Recognition - Provides a toolkit for identifying theoretical patterns and structures within numerical datasets.
• Supervised Learning Models - Implements predictive models for regression and classification to map input data to target values.
• Layered Architectures - Structures neural networks as sequences of independent operational layers to solve nonlinear mapping problems.
• Gradient Optimization Techniques - Provides techniques for adjusting model gradients during training to improve stability and convergence.
• Neural Network Frameworks - Provides a modular framework for building and executing artificial neural networks.
• Neural Network Implementations - Builds and executes artificial neural networks to solve complex nonlinear data mapping problems.
• Gradient Descent Algorithms - Implements iterative optimization algorithms that update model parameters via the negative gradient.
• Python Machine Learning Libraries - Provides a Python-based collection of regression, classification, and neural network algorithms.
• Supervised Learning - Trains models for regression and classification to predict target values based on historical data.
• Machine Learning Algorithms - Implements foundational machine learning algorithms for regression, classification, and neural networks.
• Statistical Modeling - Implements mathematical frameworks for analyzing data and predicting future trends.
• Vectorized Array Operations - Performs high-speed matrix calculations and tensor manipulations using vectorized array operations.
• Linear Algebra - Implements high-performance mathematical routines for vector and matrix operations using NumPy.
• Statistical Analysis Libraries - Implements a comprehensive library for applying probability models and clustering to numerical datasets.
• Modular Implementations - Organizes distinct machine learning models into independent modules to allow isolated testing.
• Mathematical Function Implementations - Translates theoretical probability and regression formulas into executable Python functions.
• Algorithm Decomposition - Organizes machine learning models into independent modules for isolated testing and extension.
• Model State Management - Encapsulates model parameters and training weights within class instances to maintain state.
• Weight State Encapsulation - Stores model parameters and training weights within class instances to maintain consistency.

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