I've decided to start trying to write up my notes on ML in a more structured form, and I'm posting them here.
These notes are not comprehensive overviews of the topics, but more like a re-write of some core ideas to make them seem as simple/comprehensible as possible.
A warning: these notes vary in quality - some are very thorough and I hope rather interesting. Others, not so much 🙃
BackpropagationGradient DescentGenerative ModellingClusteringHypothesis TestingInformation TheoryK-Nearest NeighborsLinear AlgebraLinear RegressionLogistic RegressionProbabilitySamplingSupport Vector MachinesTime SeriesTree-Based MethodsAcceleratorsDifferential PrivacyOptimisationConvolutional Neural NetworksRecurrent Neural NetworksPractical MethodologiesApplicationsAttention & TransformersDeep Reinforcement LearningNumber FormatsDistributed TrainingEvaluating Language ModelsEinsumWhat to plotMLP-centric modelsHyperdimensional ComputingValue scaling
⬇️ work in progress - need to move more of my notes over