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

• ZJUGuoShuai / MachineLearningLectureNotes
  张志华机器学习导论 MOOC 讲义
  ☆130Updated 3 years ago
• kushagra06 / CS228_PGM
  🌀 Stanford CS 228 - Probabilistic Graphical Models
  ☆122Updated 6 years ago
• Doraemonzzz / CS205A-Mathematical-Methods-for-Robotics--Vision--and-Graphics
  斯坦福数值分析公开课的学习资料
  ☆143Updated 6 years ago
• econti / cs229
  Stanford CS229 (Autumn 2017)
  ☆366Updated 7 years ago
• liigoQi / Whiteboard_ML_Notes
  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。
  ☆73Updated 4 years ago
• soloice / Matrix_Derivatives
  This is a note on matrix derivatives and described my own experience in detail. Hope you'll like it.
  ☆559Updated 6 years ago
• amber-kshz / PRML
  Python implementations (on jupyter notebook) of algorithms described in the book "PRML"
  ☆256Updated 4 years ago
• MLaPP-Solution-Manual / Solutions-to-Machine-Learning-A-Probabilistic-Perspective-
  Solutions to "Machine Learning: A Probabilistic Perspective"
  ☆162Updated 4 years ago
• marsggbo / machine_learning_basics_notes
  机器学习白板推导系列笔记总结
  ☆31Updated 5 years ago
• davidbau / how-to-read-pytorch
  Quick, visual, principled introduction to pytorch code through five colab notebooks.
  ☆432Updated 7 months ago
• PKUAI26 / Deep-Learning-Advanced-Topics
  ☆154Updated 5 years ago
• israfelsr / CS231n
  CS231n Assignments Solutions - Spring 2020
  ☆48Updated 4 years ago
• LynnHo / Matrix-Calculus-Tutorial
  Matrix Calculus via Differentials, Matrix Derivative, 矩阵求导教程
  ☆285Updated 2 years ago
• florist-notes / CS228_PGM
  🌲 Stanford CS 228 - Probabilistic Graphical Models
  ☆136Updated 11 months ago
• jingwenh / Computer-Vision-CMU-16720B
  ☆28Updated 6 years ago
• nikolajohn / Pattern-Recognition-And-Machine-Learning-
  Pattern Recognition And Machine Learning 相关的学习资源
  ☆151Updated 6 years ago
• Doraemonzzz / ML-Foundation-and-ML-Techniques
  台大机器学习课程作业详解
  ☆301Updated 6 years ago
• graykode / distribution-is-all-you-need
  The basic distribution probability Tutorial for Deep Learning Researchers
  ☆1,635Updated 4 years ago
• 2019ChenGong / Machine-Learning-Notes
  ☆520Updated 4 years ago
• tengyuma / cs229m_notes
  ☆232Updated 2 years ago
• dalmia / Deep-Learning-Book-Chapter-Summaries
  Attempting to make the Deep Learning Book easier to understand.
  ☆1,080Updated 6 years ago
• shuhuai007 / cs229
  Stanford CS229 (Autumn 2017)
  ☆30Updated 7 years ago
• niudd / kaggle-cloud
  ☆121Updated 5 years ago
• mazhengcn / suggested-notation-for-machine-learning
  This introduces a suggestion of mathematical notation protocol for machine learning.
  ☆477Updated last year
• Doraemonzzz / Learning-from-data
  记录Learning from data一书中的习题解答
  ☆991Updated 6 years ago
• zhuzilin / NP_ML
  A tool library of classical machine learning algorithms with only numpy.
  ☆222Updated 4 years ago
• yousuketakada / prml_errata
  More PRML Errata
  ☆80Updated 2 years ago
• hova88 / MIT-18.03-ode-notes
  MIT 18.03 微分方程（Differential Equations）中文笔记
  ☆92Updated 3 months ago
• inessus / CMU_11785_Deep_Learning_Code
  ☆20Updated 7 years ago
• Theigrams / Mathematical-optimization
  数学系本科三年级《最优化理论与方法》
  ☆167Updated 4 years ago