applicable models Filters
Browse and search through available filters in this category. Click on any filter to view related techniques.
| Description | Action | ||
|---|---|---|---|
agnostic | Techniques that work with any type of machine learning model | 63 | |
neural network | Techniques for general neural network architectures | 15 | |
cnn | Techniques specifically designed for Convolutional Neural Networks | 5 | |
transformer | Techniques for transformer-based architectures | 5 | |
llm | Techniques designed for Large Language Models | 4 | |
tree based | Techniques for decision trees, random forests, and gradient boosting | 3 | |
gan | Techniques for Generative Adversarial Networks | 2 | |
linear model | Techniques for linear and logistic regression models | 2 | |
ensemble | Techniques for models that combine multiple base learners | 1 | |
gam | Techniques for Generalized Additive Models | 1 | |
gaussian process | Techniques for Gaussian Process models | 1 | |
linear | Techniques for linear models including linear regression | 1 | |
logistic regression | Techniques specifically for logistic regression models | 1 | |
probabilistic | Techniques for probabilistic and Bayesian models | 1 | |
recurrent neural network | Techniques for RNN architectures | 1 | |
rnn | Techniques for Recurrent Neural Networks including LSTM and GRU | 1 |
Showing 16 of 16 filters