Whenever I can't read a paper and I can't get it out of scientific research, I just want to change direction! It is to motivate yourself by typing the salary of an algorithm engineer in the search bar! Don't whip yourself! 
Yes, send a few!
It's time to record the necessary skills of algorithm engineering dogs, of course, hard power 
:
Published papers in well-known international conferences and journals in related fields ,
Or have participated in international and domestic algorithm competitions , and have achieved a certain ranking is preferred
Familiar with Python , C , C++ , matlab ;
numpy、pandas、matplotlib || sckicit-learn tensorflow || keras...
1. Scikit-learn (highly recommended)
www.github.com/scikit-learn/scikit-learn
Scikit-learn is a Python module built for machine learning based on Scipy. It features a variety of classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes classifiers, random forests, Gradient Boosting, clustering algorithms and DBSCAN. And also designed Python numerical and scientific libraries Numpy and Scipy
2. Keras (deep learning)
https://github.com/fchollet/keras
Keras is a deep learning framework based on Theano. Its design references Torch and is written in Python. It is a highly modular neural network library that supports GPU and CPU.
】Common algorithms such as computer vision and machine learning ,
Image Processing
Good understanding of data structures and algorithms
Familiar with Linux environment , familiar with Shell , familiar with at least one language of Python / Linux ;
Proficient in C/C++ language development under Linux-like platforms, proficient in using development tools such as gcc , gdb, Makefile , etc. Better to know STL .
Familiar with various deep neural network models , such as CNN, RNN, RBM, RCNN, RNN, etc.;
Familiar with one or several classification algorithms and their principles;
Familiar with basic tools such as OpenCV , Libcvb, Visual Studio , and various compilation environments under Linux.
Proficiency in using at least one data analysis tool , such as python, matlab
Familiarity with common algorithms such as regression, classification, ANN, ARMA
能够熟练的使用Caffe、Tensorflow、Theano、Torch等任一种主流的深度学习框架，并了解内部机制；
熟悉Python，Hadoop，Spark，pandas，xgboost，SQL，HIVE之一云计算等大数据技术
熟练掌握数据挖掘流程；
熟悉GPU加速、有CUDA编程实际应用经验者优先
滴滴：
1. 负责基于交通视频数据识别车辆、跟踪车辆；
2. 负责基于视频数据识别路况信息，获取道路的拥堵情况。
3. 具有扎实的数学基础，精通常见的机器学习、图像处理等相关算法，并在图像识别领域有应用经验；
4. 熟练掌握c/c++/python语言及常用数据结构算法，动手能力强，有较强的算法分析及实现能力；
5. 熟练阅读相关领域英文论文并能快速编程实现；
6. 具备良好的逻辑沟通能力和解决实际问题的能力；
7. 有基于深度学习的物体识别，人脸识别、车辆识别及物体检测算法经验者优先；
8. 有高质量的图像视觉学术论文的可加分。
计算机视觉：
-熟悉图像处理的常用算法；
- 熟悉机器视觉的相关方向，如双目视觉、摄像机标定、三维重建，熟悉经典的立体匹配算法；熟悉单目SFM；
- 熟悉常用的目标跟踪算法；
- 熟悉OpenCV的使用，熟练使用C++进行开发；熟悉Linux和Python。
- 熟悉导航经典滤波算法，如卡尔曼、EKF、粒子滤波等；
- 具有视觉导航项目资源优先；
- 具有扎实的代码实现能力，掌握C/C++，matlab，python等至少一种；
- Proficient in key theories and algorithms of computer vision (image recognition, detection, tracking, denoising, etc.), familiarity with face detection/recognition algorithms is preferred;
- In-depth understanding and technical implementation experience of machine learning (including deep learning);
- Proficiency in C/C++, Matlab or Python;
- Two or more years of research or work experience in computer vision, 3D reconstruction, SfM/SLAM and related fields
- Proficient in C/C++ programming, familiar with common algorithms and optimization
- Understand basic algorithms in computer vision or robotics
- Familiar with computer graphics or OpenGL programming experience is preferred
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For details, please pay attention to the official account: target detection and deep learning
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