lonelysnk Goto Github PK

12306

12306智能刷票，订票

algorithm

Awesome algorithm refactor from 'Algorithm 4th Edition'

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

architect-awesome

后端架构师技术图谱

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

chinesebqb

🇨🇳Chinese sticker pack,More joy / Github最有毒的仓库, **表情包大集合, 聚欢乐~

cpp-learning

cpp11webserver

A High Performance HTTP Web Server in C++11

css

ddns

使用阿里云解析实现动态域名解析

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

donkey

self driving car

face_recognition

The world's simplest facial recognition api for Python and the command line

latex

lihang-code

《统计学习方法》的代码实现

mathmodel

研究生数学建模，数学建模竞赛优秀论文，数学建模算法，LaTeX论文模板，算法思维导图，参考书籍，Matlab软件教程，PPT

mss

Marine Systems Simulator (MSS)

panorama

Image alignment and stitching with MATLAB

panoramic-image-stitching

Panoramic image stitching using SIFT and RANSAC in matlab.

play-with-machine-learning-algorithms

Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

prmlt

Matlab code for machine learning algorithms in book PRML

pythonrobotics

Python sample codes for robotics algorithms.

recommenders

Best Practices on Recommendation Systems

reproducible-image-denoising-state-of-the-art

Collection of popular and reproducible image denoising works.

ros-academy-for-beginners

**大学MOOC《机器人操作系统入门》课程代码示例

ros_robotics_projects

Example codes of new book ROS Robotics Projects

space

Primary

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.