xltry Goto Github PK

awesome-english-ebooks

经济学人(含音频)、纽约客、卫报、连线、大西洋月刊等英语杂志免费下载,支持epub、mobi、pdf格式, 每周更新

benchmark

bili-afan

B站费曼区UP主BILI-AFAN的资料汇总

cs-books

🔥🔥超过1000本的计算机经典书籍、个人笔记资料以及本人在各平台发表文章中所涉及的资源等。书籍资源包括C/C++、Java、Python、Go语言、数据结构与算法、操作系统、后端架构、计算机系统知识、数据库、计算机网络、设计模式、前端、汇编以及校招社招各种面经~

deeplearning4j

Suite of tools for deploying and training deep learning models using the JVM.

django_tutorials

Django tutorial series

drl_binpacking

3D bin packing is a classical and challenging combinatorial optimization problem in logistics and production systems. An effective bin packing algorithm means the reduction of total packing cost and increase in utilization of resources. Because the cost of packing materials, which is mainly determined by their surface area, occupies the most part of packing cost, and in many real business scenarios there is no bin with fixed size, so AI Department of Cainiao proposed a new type of 3D bin packing problem. The objective of this new type of 3D bin packing problem is to pack all items into a bin with minimized surface area. And a DRL method based on the sequence-to-sequence model is proposed to solve the problem. This is the research paper link: https://arxiv.org/abs/1708.05930. Source code of this method can be found in the project.

gorilla

Gorilla: An API store for LLMs

java27days

learngit

Frist reopsitory

machine_learning_python

通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

missing-semester-cn.github.io

the CS missing semester Chinese version

ner_bio

基于统计机器学习模型（最大熵模型、马尔科夫模型、条件随机场）和深度学习模型LSTM-CRF的中文分词（BIO）

numpy-ml

Machine learning, in numpy

paper2gui

Convert AI papers to GUI，Make it easy and convenient for everyone to use artificial intelligence technology。让每个人都简单方便的使用前沿人工智能技术

python

All Algorithms implemented in Python

spdptwlc-data

The experimental instances of the selective pickup and delivery problem with time window and loading cost (SPDPTWLC) , which are generated based on a classical data generator presented in (Ropkeand Cordeau, 2009; Ropke et al., 2007) for the PDPTW. The coordinates of customers are randomly selected within a [0,50]×[0,50] square according to a uniform distribution and the depot is located in (25,25). The number of available vehicles| K| is set to be N/4, where N is the number of customers. Same as the Ropke’s generator, the planned time horizon of route T is set by 600, the vehicle capacity Q is 20 and the width of time window w is 60 for all the test instances. The time window of each customer is constructed by randomly selecting ai in [0,T−di,n+1] and setting bi=ai+w.

statisticallearning_ustc

Statistical Learning course in USTC. 中科大统计学习（刘东）课程复习资料。

the-art-of-linear-algebra

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

xltry

Config files for my GitHub profile.