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林聪转身，面色疑惑地看着胡钧：话虽如此说，可是他什么时候这么脆弱了，竟然让她留下陪他说话？胡钧招呼她坐下，道：让阿水去没错。In the Soviet Union, 26.8 million people died in World War II, Of these, 8.9 million (say 14 million) were killed by regular troops. The number of militias greatly exceeds this figure, The rest are civilians, The death toll of Soviet soldiers in World War II has always been a controversial issue. Because during the Great Patriotic War, many civilian men and women were temporarily incorporated into the Red Army, it was difficult to say whether they were regular soldiers or civilians or militiamen. Therefore, there were 8.9 million and 14 million soldiers killed. However, the West agreed with the former, with about 8.8 million soldiers killed in the Soviet-German battlefield. Less than 10,000 people died in the Soviet-Japanese battlefield. Nearly 90,000 people died in the battlefield of Sufen.走到玉米跟前，先放了两个山楂在他手心，然后握着个苹果问道：这个要不要？玉米忙点头道：要。(简介翻译：Chris@日菁字幕组 转载请注明出处)五个犯了罪的女人和一个憎恨犯罪的女狱警，当本该水火不容的双方心意相通之时，她们开始了针对一个男人的复仇。然而，复仇之路充满了意外。本剧通过描写在残酷的环境中恣意生活的女人，来表现羁绊、友情以及活着的意义，是一部前所未有的犯罪喜剧。怀着决心，梦中的中学生生活开始了♪大手家電メーカーを定年退職した武曾勤６５歳（松平健）。４３年間、家庭を顧みないで働き続けた企業戦士。これからは家庭で悠々自適の隠居生活を送るつもりだった。…が、現実は甘くなかった…。妻の幸子（浅田美代子）は習い事や友人とのつきあいで、毎日忙しく充実した日々を過ごしている。勤は「ヒマなんだから何かすれば？」と幸子に上から目線で言われてしまう。離婚した娘の都（真飛聖）が小１になった孫娘・友理奈を連れて実家に戻ってきてからは、友理奈の面倒をみるのは勤の役目となった。大企業の課長として激務の毎日を送る都は、小学校の初保護者会で、誰もが敬遠するＰＴＡ役員、しかも多忙極まる「副会長」のくじを引いてしまう･･･。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 ~