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一个偏远贫穷的小山村里，活泼善良的女娃小花，在爹娘和奶奶的呵护下，过着虽不富裕但却快乐的生活。小花娘就快生了，她爹为了多挣些钱去了黑煤窑打工。一个大雨滂沱的日子，小花爹被埋在了坍塌的煤窑里，小花娘也因为急火攻心加上淋雨引起大出血失去了生命。从此小花和瞎眼多病的奶奶过着相依为命的生活，懂事的小花用自己瘦小的身躯支撑着濒临解体的家，小花的眼泪和万般努力，最终没能留住唯一的亲人奶奶，小花从此成了孤儿。医学鬼才玉清落独自生下儿子南南，七年后，她将独门医术发扬光大，成为世人皆知的鬼医。在给儿子南南寻找父亲的过程中，结识了风苍国皇子夜修独。因夜修独长相酷似仇人杨修成，玉清落对他深感厌恶，多次试探后才确认只是巧合，而此时夜修独已经对她情根深种。面对夜修独无条件的保护和宠爱，玉清落渐渐沉溺其中。同时，玉清落因鬼医身份被迫卷入纷争，和夜修独一起与各方势力斗智斗勇，最终意外发现夜修独竟是儿子南南的生父。两人相处过程中，玉清落彻底沦陷于夜修独的深情，最终两情相悦，一家三口呼啸山林，携手余生。二十世纪的上海，有东方巴黎的美称，摩登繁华的背后却风雨飘摇。神探顾远紧急调任租界，只因当地怪案频发。在租界警局，他遇到了漕运商会的“黑二代”副探长康一臣，还有身世凄惨的车素薇法医，加上刁蛮任性的好事记者曹青萝，四人组成临时探案组，对租界内一系列的怪案进行“走近科学”式的调查。他们破获了来自西洋的“瘦高鬼”案，虽成功破获却反而失败的“时间杀人”案，来自前朝的“僵尸格格”案，神仙眷侣的慈善家“赶尸换寿”案，大魔术师的“冥婚杀人案”和当时的剧组现场“剧本杀人”案。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 ~The Second World War was the war with the largest number of casualties in history, with a total of 55-60 million dead and 130 million injured, totaling 190 million dead and injured. The following are the casualties in major countries:For details, please refer to: http://wd.tgnet.com/QuestionDetail/201511101178906132/1/