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世界第一的暗杀者投胎成暗杀世家的长男重生了。他们要是突然有北上之意，取代闽越，那么越国后方将永远不会有安宁。于是，这同时并举的救援游戏就玩成功了。　　和孝公主”出嫁那天，在和珅刻意安排下，永琰的奶娘被杀，死前向永琰透露出他真正的身世…… 永琰不顾乾隆反对，决定前往台湾寻找亲生母亲---那拉皇后。然和珅不肯善罢干休，派遣自己的儿子”丰绅殷德”尾随至台湾，进行暗杀任务……Sorry to force a wave of chicken soup. Originally, I planned to write a machine learning series last year, but after writing three articles for work and physical reasons, there was no more. In the first half of this year, I was tired to death after doing a big project. In the second half of this year, I just took a breath of relief, so the follow-up that I owed before will definitely continue to be even more. In order not to let everyone worship blindly, I decided to write a series of in-depth study, one article per week, which will end in about three months. Teach Xiaobai how to get started. And finished! All! No! Fei! ! It is not simply to write demo and tuning parameters that are available on the Internet. Reject demo, start with me! If you don't understand, please leave a message under my article. I will try my best to reply when I see it. This series will mainly adopt the in-depth learning framework of PaddlaPaddle, and will compare the advantages and disadvantages of Keras, TensorFlow and MXNET (because I have only used these four frameworks, there are too many people writing TensorFlow, and I am using PaddlePaddle well at present, so I decided to start with this). All codes will be put on github (link: https://github.com/huxiaoman7/PaddlePaddle_code). Welcome to mention issue and star. At present, only the first article () has been written, and there will be more in-depth explanation and code later. At present, I have made a simple outline. If you are interested in the direction, you can leave me a message, and I will refer to the addition ~那学生听见两人如此说，为难地看向秦枫。有谁敢利用一个致命的疾病去对付在六岁病童体内的另一个疾病？结果出人意料。