eriklindernoren/ML-From-Scratch

Features

• Machine Learning Toolkits - Provides a collection of fundamental algorithms implemented from scratch to demonstrate core learning mechanics.
• Supervised Learning - Trains models on labeled datasets to predict outcomes or classify observations.
• Clustering Algorithms - Partition datasets into clusters by iteratively assigning points to the nearest center and updating positions to minimize variance.
• Deep Learning Architectures - Builds custom deep learning models by stacking layers and activation functions.
• Multilayer Perceptrons - Solve complex classification problems by defining hidden layers and backpropagation logic to learn non-linear mappings from input data.
• Random Forest Ensembles - Improve predictive accuracy and reduce variance by combining the outputs of multiple decision trees trained on random data subsets.
• Recurrent Neural Networks - Implements backpropagation through time to train sequential models by unrolling recurrent structures.
• Reinforcement Learning - Develops agents that learn to make sequences of decisions to maximize cumulative rewards.
• Unsupervised Learning - Discovers hidden structures and patterns in unlabeled data using clustering and dimensionality reduction.
• Educational Examples - Provides a collection of machine learning models and algorithms built from scratch for educational purposes.
• Machine Learning Tutorials - Provides fundamental machine learning algorithm implementations built from scratch for educational purposes.
• Ensemble Learning - Combines multiple weak learners into robust models using boosting and bagging strategies.
• Expectation-Maximization Models - Group data points by iteratively calculating membership probabilities to optimize the fit of statistical distributions to the data.
• Gradient Boosting - Minimize prediction errors by combining multiple decision trees into a single ensemble model through iterative gradient descent.
• Logistic Regression Models - Predict the likelihood of binary outcomes by applying the sigmoid function and optimizing weights through gradient descent.
• Neural Network Architectures - Constructs custom neural networks by defining specific layers, activation functions, and loss functions.
• Neural Network Frameworks - Constructs complex neural network architectures by chaining independent functional blocks.
• Clustering Suites - Provides a collection of density and centroid-based algorithms for grouping unlabeled data.
• Computer Vision - Recognizes visual patterns in images by training convolutional neural networks to extract features.
• Decision Trees - Builds decision-based models by repeatedly splitting datasets into subsets based on feature thresholds.
• Deep Learning Frameworks - Constructs custom neural network architectures to demonstrate backpropagation and gradient descent mechanics.
• Generative Adversarial Networks - Creates synthetic images of handwritten digits by training generative adversarial networks.
• Naive Bayes Classifiers - Classify data points by calculating the probability of class membership based on the statistical distribution of input features.
• Optimization Algorithms - Updates model parameters iteratively by calculating partial derivatives of the loss function.
• Sequential Learning - Processes time-series data by using recurrent neural networks to capture temporal dependencies.
• General Machine Learning - Educational implementations of ML models.
• Machine Learning - Implementations of machine learning algorithms from the ground up.
• Autoencoders - Reduces data complexity by training encoder and decoder networks to reconstruct original inputs.
• Density-Based Clustering - Identifies clusters of arbitrary shapes by analyzing local data density.
• Ensemble Learning Libraries - Ships a suite of boosting and bagging implementations to improve predictive performance.
• Evolutionary Algorithms - Optimizes neural network architectures and weights using evolutionary strategies.
• Perceptron Classifiers - Create simple linear models by iteratively adjusting weights based on prediction errors to separate labeled data points.
• Algorithm References - Provides clean, readable code examples for standard statistical and neural network architectures.
• Association Rule Learning - Discovers frequent patterns and relationships within transactional datasets using the Apriori algorithm.
• Boltzmann Machines - Represents complex data structures by training Boltzmann machines to learn underlying feature patterns.
• Dimensionality Reduction - Identify linear combinations of features that maximize class separation to simplify datasets or improve classification performance.
• Gaussian Mixture Models - Implements Gaussian mixture models to represent complex data distributions using expectation-maximization.
• Genetic Algorithms - Solves optimization problems by simulating natural selection processes to evolve candidate solutions.
• K-Medoids Clustering - Groups data points by minimizing total dissimilarity to representative medoids.
• Numerical Computing Libraries - Performs mathematical operations on multidimensional arrays to accelerate linear algebra calculations.

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