lh913137941's Stars
codecrafters-io/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
practical-tutorials/project-based-learning
Curated list of project-based tutorials
TheAlgorithms/Python
All Algorithms implemented in Python
krahets/hello-algo
《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing
Anduin2017/HowToCook
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Simplified Chinese only).
josephmisiti/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
geekxh/hello-algorithm
🌍 针对小白的算法训练 | 包括四部分：①.大厂面经 ②.力扣图解 ③.千本开源电子书 ④.百张技术思维导图（项目花了上百小时，希望可以点 star 支持，🌹感谢~）推荐免费ChatGPT使用网站
halfrost/LeetCode-Go
✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解
keon/algorithms
Minimal examples of data structures and algorithms in Python
dibingfa/flash-linux0.11-talk
你管这破玩意叫操作系统源码 — 像小说一样品读 Linux 0.11 核心代码
ddbourgin/numpy-ml
Machine learning, in numpy
Jack-Cherish/Machine-Learning
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
itcharge/LeetCode-Py
⛽️「算法通关手册」：超详细的「算法与数据结构」基础讲解教程，从零基础开始学习算法知识，850+ 道「LeetCode 题目」详细解析，200 道「大厂面试热门题目」。
meta-llama/llama-models
Utilities intended for use with Llama models.
cerlymarco/MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
Yimeng-Zhang/feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
ZhongYi-LinuxDriverDev/CS-EmbeddedLinux-Book
嵌入式，计算机常用电子书籍整理，并且附带下载链接，涵盖：ARM体系与架构，C/C++语言，汇编语言，操作系统，计算机网络，计算组成原理，Linux驱动，Linux内核，单片机开发，程序员认知成长，笔试面试技巧等书籍。长期更新中，欢迎star~
chinhsuanwu/mobilevit-pytorch
A PyTorch implementation of "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer"
kaushalshetty/FeatureSelectionGA
Feature Selection using Genetic Algorithm (DEAP Framework)
anujdutt9/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
solegalli/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
fengyang95/tiny_ml
numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法
cheery/json-algorithm
Now even your pet rock can parse JSON.
doubleZ0108/Leetcode
Leetcode solutions @doubleZ0108 from Peking University.
PacktPublishing/Deep-Learning-for-Time-Series-Data-Cookbook
B21145 - Deep Learning for Time Series Data Cookbook
wenhan123/ML-Python-
主要是在学习李航的统计学习方法和周志华的机器学习西瓜书的笔记和相关的代码实现
gbrlcustodio/forecasting
Auto tunned hybrid model for time series prediction using ARIMA and ANN
lordflavio/PEMF-Time-Series
Predictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or Kriging-Linear), sample data on which to train the model, and hyper-parameter values (e.g., shape factor in RBF) to apply to the model. As output, it provides a predicted estimate of the median and/or the maximum error in the surrogate model. PEMF has been reported to be more accurate and robust than typical leave-one-out cross-validation, in providing surrogate model error measures (for various benchmark functions). The current version of PEMF has been implemented with RBF (included in this package), Kriging (DACE package), and SVR (Libsvm package), PEMF (has been and) can be readily used for the following purposes: 1. Surrogate model validation 2. Surrogate model uncertainty analysis 3. Surrogate model selection 4. Surrogate-based optimization (to guide sequential sampling) Other perceived broader applications of PEMF include testing of machine learning models and uncertainty analysis with data-driven models (and other areas where leave-one-out or k-fold cross-validation is typically used).
cl3to/MC889
Repositório para armazenar o conteúdo produzido no projeto final da disciplina de Introdução a Criptografia