ws13685555932 / 18.065_lecture_notes
lecture notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"
☆149Updated 5 years ago
Alternatives and similar repositories for 18.065_lecture_notes:
Users that are interested in 18.065_lecture_notes are comparing it to the libraries listed below
- 张志华机器学习导论 MOOC 讲义☆129Updated 3 years ago
- 斯坦福数值分析公开课的学习资料☆139Updated 5 years ago
- 🌀 Stanford CS 228 - Probabilistic Graphical Models☆122Updated 6 years ago
- Solutions to "Machine Learning: A Probabilistic Perspective"☆160Updated 4 years ago
- Statistical Learning Theory (CS229T) Lecture Notes☆716Updated 5 years ago
- Python implementations (on jupyter notebook) of algorithms described in the book "PRML"☆253Updated 3 years ago
- 台大机器学习课程作业详解☆299Updated 5 years ago
- Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Frie…☆287Updated 7 years ago
- NYU Data Science Course DSGA-1003 Machine Learning Assignments.☆30Updated 7 years ago
- A Chinese learning note with python codes for Pattern Recognition and Machine Learning.☆26Updated 6 years ago
- 机器学习白板推导系列笔记总结☆31Updated 5 years ago
- MIT 18.03 微分方程(Differential Equations)中文笔记☆90Updated this week
- ☆24Updated 7 years ago
- More PRML Errata☆80Updated 2 years ago
- Matrix Calculus via Differentials, Matrix Derivative, 矩阵求导教程☆282Updated 2 years ago
- AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics☆58Updated 2 years ago
- b站@shuhuai008的白板推导系列课程手写笔记,补充了一些用到的数学基础知识。☆71Updated 4 years ago
- Machine learning course materials.☆572Updated last year
- Solutions to the problems in the book: Linear Algebra and Learning from Data by Gilbert Strang, MIT☆275Updated 2 years ago
- Collaborative lecture notes for Spring '19 NYU DL class☆119Updated 5 years ago
- 🍟 Stanford CS229: Machine Learning☆24Updated 6 years ago
- ☆153Updated 4 years ago
- Stanford CS106B☆161Updated 8 years ago
- My Own Solution Manual of PRML☆981Updated 4 years ago
- This introduces a suggestion of mathematical notation protocol for machine learning.☆466Updated 9 months ago
- Berkeley CS182/282A Designing, Visualizing and Understanding Deep Neural Networks☆85Updated 2 years ago
- Quick, visual, principled introduction to pytorch code through five colab notebooks.☆426Updated 3 months ago
- This is a note on matrix derivatives and described my own experience in detail. Hope you'll like it.☆557Updated 6 years ago
- My solutions to Kevin Murphy Machine Learning Book☆537Updated 4 years ago
- PyTorch tutorials and best practices.☆1,680Updated last month