Machine learning
Notes on machine learning.
Data
Explainabilty
Drift
Time-series
Clustering
Fairness
Metrics
Error metrics
Performance
Transformations
Optimisation
- Gradient descent
- Stochastic Gradient Descent
- Stochastic Gradient descent with momentum
- Mini-Batch Gradient Descent
- Adagrad
- RMSProp
- AdaDelta
- Adam
- Gradient-free optimisation
RNN
Statistics
Model selection
Kernel functions
Kernels can be interpreted as “similarity functions”. Typically they are functions that take two $n$-dimensional points and produce a scalar. In the following sections we look at some well-known kernels and their typical applications.
Unsupervised methods
Supervised methods
Methods pertaining to Supervised learning.
Techniques such as:
Regression
Frameworks
- Cookiecutter Data Science
- Scikit-learn
- Model serving