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阿里大吃一惊，知道自己判断失误，心直往下沉。素有神童之称的渔家子弟周计春与富家女孔令仪邂逅于海边，又各自到上海求学深造。初次见面，计春便遭到令仪的百般刁难。大学入学考试中，计春设计让令仪名落孙山，使令仪对他怀恨在心，决计报复。令仪的男友陈子布与她的死党袁佩珠结成新欢，孔、袁二人反目成仇。令仪示图把纯朴的计春培养成翩翩公子，以报复陈子布移情别恋，遂与袁佩珠打赌。在令仪的指领下，计春真正步入上海这个十里洋场的花花世界。计春凭借其数学天赋，开始在赌场崭露头角。上海滩股票大亨陈建廷慧眼识珠，将计春引进风起云涌的证券股票市场，意欲将他培养成自己的接班人。计春和令仪的关系此时也由恨变爱，进而出双入对。在此之后，刘邦到底是生是死，影响根本不是那么大。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 ~还有，跑龙套的演员，他也是演员啊。DNS Query Flood(five) shall not organize or participate in forging or altering certification materials to defraud, embezzle or misappropriate medical security funds;