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Mathematical Notation Powered by CodeCogs
Models and Modeling
Models and modeling provide the tools to create algorithms that can predict conditions and actions in the real world.
Previously, we might use machine learning in a few sub-components of a system. Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces.
Algorithm Libraries
Artificial Neural Networks
Attention
Automated Machine Learning
Backpropagation
Causal Embedding
Classification
Cluster Analysis
Collaborative Filtering
Convolutional Neural Networks
Cross Decomposition
Curve Fitting
Decision Trees
Deep Learning
Deep Reasoning
Ensemble Learning
Feature Selection
Fourier Analysis
Gaussian Analysis
Generative Adversarial Networks
Gradient Boosting
Histogram of Oriented Gradients
Image Processing
K-Means Clustering
Linear Regression
Logistic Regression
Long Short-Term Memory
Markov Chains
Model Categories
Modeling Process
Naive Bayes
Nearest Neighbors
Probabilistic Graphical Models
Random Forest
Recurrent Neural Networks
Regression Analysis
Regularization
Reinforcement Learning
Supervised Learning
Support Vector Machines
Transformer Neural Networks
Unsupervised Learning
Word Embedding