Blogs
Machine Learning Study Notes
Course note for Machine Learning (Fall 2022). Primarily based on slides from Prof. Vatsal Sharan.
- Lecture 1 - ML Framework, Linear Regression and Optimization Methods
- Lecture 2 - Perceptron and Logistic Regression
- Lecture 3 - Generalization, Overfitting and Regularization
- Lecture 4 - Regularization and Kernel Methods
- Lecture 5 - SVMs
- Lecture 6 - Multiclass Classification and Nerual Network
- Lecture 7 - CNN, Markov Models and RNN
- Lecture 8 - Decision Trees and Ensumble Learning
- Lecture 9 - PCA
- Lecture 10 - K-Means, GMM and EM
- Lecture 11 - KDE, Naive Bayes and Multi-armed Bandits
Presentation Slides
Some presentation slides.- Rethinking the Effectiveness of Masked Adapter
- What can Transformers Learn In-Context?
- Language-driven 3D Stylization
- Interpretable White-box Deep Nets
- Difference Masking
PyTorch Study Blogs (in Mandarin)
Here're some study blogs and basic tutorials (in Mandarin) on PyTorch, which already harvested more than 120K views and thousands of bookmarks. If you're interested, click the Tutorial Link and browse on your own.Data Science Course Notes (in Mandarin)
Course note for Foundations of Data Science, wrote in Mandarin. Primarily based on slides from Prof. Ye Yuan, book Mathmatics in Machine Learning, and book Machine Learning.- Lesson 1 Equation Solving
- Lesson 2 Functional Imitation
- Lesson 3 Fundamentals of Optimizing Method
- Lesson 3.5 Estimator of Probability Density Function(MLE, MAP, Bayes, KNN, Parzen, GMM, EM)
- Lesson 4 Regression (Linear, NonLinear, Ridge, Lasso)
- Lesson 5 Classification (Perceptron, Fisher, Logistic, Softmax, Bayes)
- Lesson 6 SVMs
- Lesson 7 Information Theory and Decision Treee
- Lesson 8 Clustering and Dimension Reduction
- Lesson 9 Convex Optimization
- Lesson 10 Stochastic Process and Reinforcement Learning
- Lesson 11 Neural Network and Deep Learning
- Lesson 12 Time Series
- Lesson 13 Ensemble Learning