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The Hitchhiker's Guide to Machine Learning Algorit
  • README
    • Title Page
    • Introduction
    • Half Title
    • Authors
    • Dedication
    • Acknowledgements
    • Preface
    • Copyright
  • Chapters
    • Actor-critic
    • AdaBoost
    • Adadelta
    • Adagrad
    • Adam
    • Affinity Propagation
    • Apriori
    • Asynchronous Advantage Actor-Critic
    • Averaged One-Dependence Estimators
    • Back-Propagation
    • Bayesian Network
    • Boosting
    • Bootstrapped Aggregation
    • C5.0
    • CatBoost
    • Chi-squared Automatic Interaction Detection
    • Classification and Regression Tree
    • Conditional Decision Trees
    • Convolutional Neural Network
    • Decision Stump
    • Deep Belief Networks
    • Deep Boltzmann Machine
    • Deep Q-Network
    • Density-Based Spatial Clustering of Applications with Noise
    • Differential Evolution
    • Eclat
    • Elastic Net
    • Expectation Maximization
    • eXtreme Gradient Boosting
    • Flexible Discriminant Analysis
    • Gated Recurrent Unit
    • Gaussian Naive Bayes
    • Genetic
    • Gradient Boosted Regression Trees
    • Gradient Boosting Machines
    • Gradient Descent
    • Hidden Markov Models
    • Hierarchical Clustering
    • Hopfield Network
    • Independent Component Analysis
    • Isolation Forest
    • Iterative Dichotomiser 3
    • k-Means
    • k-Medians
    • k-Nearest Neighbor
    • Label Propagation Algorithm
    • Label Spreading
    • Latent Dirichlet Allocation
    • Learning Vector Quantization
    • Least Absolute Shrinkage and Selection Operator
    • Least-Angle Regression
    • LightGBM
    • Linear Discriminant Analysis
    • Linear Regression
    • Locally Estimated Scatterplot Smoothing
    • Locally Weighted Learning
    • Logistic Regression
    • Long Short-Term Memory Network
    • M5
    • Mini-Batch Gradient Descent
    • Mixture Discriminant Analysis
    • Momentum
    • Monte Carlo Tree Search
    • Multidimensional Scaling
    • Multilayer Perceptrons
    • Multinomial Naive Bayes
    • Multivariate Adaptive Regression Splines
    • Nadam
    • Naive Bayes
    • Ordinary Least Squares Regression
    • Partial Least Squares Regression
    • Particle Swarm Optimization
    • Perceptron
    • Policy Gradients
    • Principal Component Analysis
    • Principal Component Regression
    • Projection Pursuit
    • Proximal Policy Optimization
    • Q-learning
    • Quadratic Discriminant Analysis
    • Radial Basis Function Network
    • Random Forest
    • Recurrent Neural Network
    • Reinforcement Learning
    • Ridge Regression
    • RMSProp
    • Rotation Forest
    • Sammon Mapping
    • Self-Organizing Map
    • Semi-Supervised Support Vector Machines
    • Simulated Annealing
    • Spectral Clustering
    • Stacked Auto-Encoders
    • Stacked Generalization
    • State-Action-Reward-State-Action
    • Stepwise Regression
    • Stochastic Gradient Descent
    • Support Vector Machines
    • Support Vector Regression
    • t-Distributed Stochastic Neighbor Embedding
    • TD-Lambda
    • Weighted Average
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  • Authors
  • Devin Schumacher
  • Francis LaBounty Jr.
  • Co-Authors & Contributors
  1. README

Authors

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Last updated 1 year ago

Thank you to all the authors, co-authors, editors & contributors!

Authors

Devin Schumacher

Devin Schumacher

Devin Schumacher is an American entrepreneur, internet personality, author, actor, music producer, podcaster, teacher, hacker, philanthropist. He is the founder of SERP, the parent company for a variety of brands that operate in the technology sector, specifically within digital marketing, media, software development, artificial intelligence and education; and is widely considered to be the world's best SEO & grumpy cat impersonator.

Francis LaBounty Jr.

Francis LaBounty Jr. doesn't like writing bios... and so he shant continue writing this one.

Co-Authors & Contributors

Francis LaBounty Jr.