ML Cheatsheet

Basics

Linear Regression
Gradient Descent
Logistic Regression
Glossary

Math

Calculus
Linear Algebra
Probability (TODO)
Statistics (TODO)
Notation

Neural Networks

Concepts
Forwardpropagation
- Simple Network
  - Steps
  - Code
- Larger Network
Backpropagation
Activation Functions
- Linear
- ELU
- ReLU
- LeakyReLU
- Sigmoid
- Tanh
- Softmax
Layers
- BatchNorm
- Convolution
- Dropout
- Linear
- LSTM
- Pooling
- RNN
Loss Functions
- Cross-Entropy
- Hinge
- Huber
- Kullback-Leibler
- MAE (L1)
- MSE (L2)
Optimizers
- Adadelta
- Adagrad
- Adam
- Conjugate Gradients
- BFGS
- Momentum
- Nesterov Momentum
- Newton’s Method
- RMSProp
- SGD
Regularization
Architectures
- Autoencoder
- CNN
- GAN
- MLP
- RNN
- VAE

Algorithms (TODO)

Classification
Clustering
- Centroid
- Density
- Distribution
- Hierarchical
- K-Means
- Mean shift
Regression
- Lasso
- Linear
- Ordinary Least Squares
- Polynomial
- Ridge
- Splines
- Stepwise
Reinforcement Learning

Resources

Datasets
Libraries
Papers
Other

Contributing

How to contribute

ML Cheatsheet

Docs »
Probability
Edit on GitHub

Probability¶

Basic concepts in probability for machine learning.

References

[1]

Example

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