好消息 | 2023年，这个岗位需求暴增！
1）For each user, determine her country based on the numeric IP address.
2）Build a model to predict whether an activity is fraudulent or not. Explain how different assumptions about the cost of false positives vs false negatives would impact the model.
3）Your boss is a bit worried about using a model she doesn't understand for something as important as fraud detection. How would you explain her how the model is making the predictions? Not from a mathematical perspective (she couldn't care less about that), but from a user perspective. What kinds of users are more likely to be classified as at risk?What are their characteristics?
4）Let's say you now have this model which can be used live to predict in real time if an activity is fraudulent or not. From a product perspective, how would you use it? That is,what kind of different user experiences would you build based on the model output?
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以应用场景对宠物进行识别为例，换到自然状态下获取的照片，受到自然光线的影响，算法鲁棒性是否依然存在？ 是否可以做到实时识别，可以达到多少fps？ 相关系统如何运作？ 系统架构如何搭建，模块部署如何完成？ 为了性能考虑，是否需要使用C++？ 重要算法可否进行并行优化？ 算法内核是否可以改进来增加识别准确率？ 所部署的目标平台是什么？ 结果的检测标准(KPI/Quality Matrix)是什么？
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