Stanford CS229 Assignment Solutions
- Assignment 0 (Summer 2020)
- Assignment 1 (Summer 2020)
- Linear Classifiers (logistic regression and GDA)
- Incomplete, Positive-Only Labels
- Poisson Regression
- Convexity of Generalzied Linear Models
- Linear regression: linear in what?
- Assignment 2 (Summer 2020 & Autumn 2018)
- Logistic Regression: Training stability
- Model Calibration
- Spam classification
- Constructing kernels
- Kernelizing the Perceptron
- Neural Networks: MNIST Image classification
- Bayesian Interpretation of Regularization
- Assignment 3 (Summer 2020 & Autumn 2018)
- A Simple Neural Network
- KL divergence and Maximum Likelihood
- KL Divergence, Fisher Informatino, and the Natural Gradient
- K-means for compression
- Semi-supervised EM
- Independent components Analysis
- Assignment 4 (Autumn 2018)