yunicer Goto Github PK

academic-kickstart

admm

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

admm-pruning

Prune DNN using Alternating Direction Method of Multipliers (ADMM)

admm-qp

algorithms

Bug-tracking for Jeff's algorithms book, notes, etc.

awesome-beamers

beamer template collection

boostfw

Boosting Frank-Wolfe by Chasing Gradients

c9-python-getting-started

Sample code for Channel 9 Python for Beginners course

coderchef-kitchen

The official repository for our programming kitchen which consists of 50+ delicious programming recipes having all the interesting ingredients ranging from dynamic programming, graph theory, linked lists and much more. All the articles contain beautiful images and some gif/video at times to help clear important concepts.

cs229_ml

🍟 Stanford CS229: Machine Learning

elegantnote

Elegant LaTeX Template for Notes

hmcpset-class

Harvey Mudd College Problem Set Class (LaTeX)

leetcode

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

leetcodeanimation

Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）

libpku

贵校课程资料民间整理

linear-programming-using-matlab

This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus. The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis.

machinelearning_columbiax

记录edx的哥伦比亚大学机器学习课程资料

net-trim

The latest version of Net-Trim which solves the actual constrained problem

noisy-dpcp

Code of paper "Noisy Dual Principal Component Pursuit", ICML 2019

paper-spray

List of interesting papers to read

practicalai

📚A practical approach to learning machine learning.

python

All Algorithms implemented in Python

rekcarc-tsc-uht

清华大学计算机系课程攻略 Guidance for courses in Department of Computer Science and Technology, Tsinghua University

stanford-introduction-to-mathematical-thinking

Keith Devlin's Introduction to Mathematical Thinking course on Coursera (2017 Spring)

stat-learning

Notes and exercise attempts for "An Introduction to Statistical Learning"

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

tensorflow-course

Simple and ready-to-use tutorials for TensorFlow

text-to-image

Generative Adversarial Text to Image Synthesis / Please Star -->

tyleransom.github.io

ucasthesis

[最新样式] **科学院大学学位论文 LaTeX 模板 LaTeX Thesis Template for the University of Chinese Academy of Sciences