3.4 Note:
A man who told me to be a mature adult
自己一心想着要子夜快些生下孩子，却没有顾及到侄女和孩子的安危，子夜可是自己唯一的亲人了。
There are 31 events in the Olympic swimming competition, which is the second largest gold medalist after track and field.
除了可以用来救人外，本性阴毒的蛇人也是极佳杀人工具，誓要毁灭眼中任何障碍，于是利用这个技术与霸基集团副总裁路易斯勾结暗杀现任总裁与继任总裁安德鲁。关伟、浩天在在阻止这些生物的逃窜和猎杀当中历经犯罪集团劫杀、蛇人小灵的攻击、政府官僚的阻挠等困难逐步接近了一个不为人知的杀人目地……
The death toll from World War II in China has been unclear because the statistics are unclear. Scholars estimate that there are 12-18 million people. However, after the 1990s, the data gradually became clear: about 18 million. Among them, the death toll of soldiers is about 1.48 million, of which 1.35 million are from the national army, more than 100,000 to 120,000 are from the communist army (including guerrillas), and the rest are civilians.
葫芦见他神色异常，忙带他进屋，又喊小葱进去。
2. Classification of flame retardants
在一次和日军的战役中，国民党军队败退，但一个掌握着重要情报的部门被日军围困，下落不明。因此国民党军队派一个连回去营救。而新四军的一支游击队在行军途中恰好遇到并营救下该国民党情报部门主任苏白，在与日寇的战斗中，随后赶到的国民党连队与游击队前后夹攻，暂时击退日军分队，充满戏剧性的故事由此展开。两支共产党和国民党的“友军”本来也相互是敌人，但由于国共抗日一致对外而成为“友军”，但彼此的戒备并未消除。两个部队戏剧化的相遇，因为不熟悉差点冲突，在抗击日军的过程中互不服气，直至手挽手联合杀敌成为生死兄弟，最后全军覆没战死沙场。
乡下人出城的故事桥段出自周星驰之手。女主角彩凤在中国农村长大，最爱跳舞。她到上海闯荡，受骗之后终于在一家舞蹈学院找到一份工作，只是当个门房，其天分何时才能显露？机会来得有点神奇：敌对舞蹈学校的校长要跟学院校长比试，学院校长却不屑一顾：“我们的清洁女工，也比你们的明星学生出色。”于是，舞蹈皇后出场了。
永平帝诧异地问：就这样？赵培土点头道：就是这样。
//Subclasses override methods to implement their own business logic
superstar？Yes，曾经的superstar。眼下的境况是知名度下滑+家族生意遇到瓶颈。一堆破事要怎么办？干脆到国外去散心探望朋友并学习经营之道。时来运转，贵人降临。可是这个懂得那么多的工人怎么老爱捉弄她?和他做朋友？似乎也不赖只是，事情真的仅仅就那么简单？是机缘巧合，还是命中注定？一切等待她回到泰国后，谜底终将揭晓
90年代在阿尔及利亚，18岁的女大学生娜吉玛热衷于时尚设计，她不愿因阿尔及利亚内战而影响她的正常生活，白天是倡导自由平等的普通学生，晚上则和闺蜜们一起出游玩乐。但随著社会风气变得愈来愈保守，她拒绝接受国家激进派订定的新禁令，在经历一段悲剧之后，决定为这国家的种种不平等奋斗，破天荒举办阿尔及利亚史上第一场时装展。
香坂（长谷川博己）行动力强，能力突出，以成为搜查一课课长为目标努力着，他本人也被现任搜查一课课长看好，众人也默认他会是“未来的搜查一课课长”。在某次取证调查时，香扳犯了错误，没想到却受到了降职处分，而下达处分命令的则是一直提拔自己的课长。降职之後的香扳受到同事们的调侃，他就在苦恼中艰苦地贯彻着自己心中的正义.....
受地主家阶级和日本鬼子的逼迫，庚伢子雷正兴的成长中，父母兄弟先后受地主谭家和资本家逼迫而死。母亲雷一嫂也因受到地主谭少爷欺凌而自杀。孤苦伶仃的庚伢子直到解放军到来，才得以解救。他也就此立志加入解放军。
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 ~
