#concept-pamphlet #todo: this is an ongoing work in progress over the next year
Machine Learning
Terms
#todo : fill in terms from CS 229 Lecture Notes - Spring 2023.pdf
Structure is currently pulled from CS 229 Lecture Notes - Spring 2023.pdf [1] :
- linear regression
- least mean squares (LMS)
- classification
- logistic regression
- generalized linear models (GLM)
- gaussian discriminant analysis (GDA)
- naive bayes
- kernels
- support vector machines
- deep learning
- back-propagation
- unsupervised learning
- k-means algorithm
- expectation-maximization algorithm (EM)
- principal component analysis (PCA)
- independent component anlaysis
- pre-training transformers
- transformer
on the components needed to recreate a brain
- visual
- auditory
- reasoning
- kinesthetic
- smell
- taste
On structured resources for these topics
- MATH 51
- CS221
- CS229 Lecture Notes Questions
- CS229 Psets on linear algebra
- Deep Learning Textbook (Goodfellow 2016)
- Reinforcement learning
- cs224w
- CS224n
- CS238 - Decision making under uncertainty
Build a neural network 🧸
- forward pass
- tensor
- loss
- gradient descent
- step value
- topological sort
- backward pass
- backpropagation
- gradient
- chain rule
- neural network
- weight
- bias
- multi-layer perceptron
- layer
- activation functions
- (B, T, C) tensor
Reinforcement Learning
- reinforcement learning from human feedback (RLHF)
- markov decision processes (MDPs)
- linear quadratic regulation (LQR)
- differential dynamic programming (DDP)
- linear quadratic gaussian (LQG)