Partial side of computer knowledge, two partial face image processing, preparing a lot of knowledge of machine learning, machine learning did not ask the basic question in addition to their own records related to the resume:
Side:
1. Do you understand what linear regression, can not simply introduce the next?
2. logistic regression and general linear regression What is the difference?
3. gradient descent What are the disadvantages?
4. Understand what red-black tree? The difference between binary tree and black tree?
5. There are two pictures of different rates, how to find the same object?
6. What is the principle of support vector machine?
7. In the n × n grid (no coordinate direction), from one vertex to another vertex (on the diagonal) departure, what guarantee does not come to the starting point of the policy?
8. How to use AI automatically push the tower games (such as the king of glory push the tower), so that if you achieve, what is your step?
Two faces:
1. What are your methods to extract image areas of interest to know?
2. What opencv commonly used image processing functions, you know?
3. What is the principle of edge detection is?
What is the principle 4.sift algorithm is?
5. What is the convolution core is?
6. How to choose the convolution kernel size?
7. What is the role pooling layer is?
8. How do you improve the efficiency of the model? Change parameters? Change the loss function?
9. There is no loss of function through their own design?
Parallel image processing algorithms 10. understand it?
11. What semantic image segmentation is?
12. reinforcement learning to understand it?