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董佳音和夏鸣涛结婚三年都没有要孩子。董佳音是电视台社会新闻节目的制作人，夏鸣涛是妇产医院的主任医生，两人的事业都正处于一个上升期，无暇顾及生孩子的事。夏鸣涛的母亲见还抱不上孙子，一急之下从老家赶来，亲自监督小两口要孩子。结果婆媳之间频繁产生摩擦，懂事的董佳音体谅婆婆抱孙心切的心情，委曲求全，而夏鸣涛夹在中间，既心疼妻子，又顾念老母，左右为难。 　　蓝氏贸易的老板蓝天和妻子徐雅莉夫妻十年，伉俪情深。当年蓝天因打架伤人而坐牢，刑满释放，徐雅莉不惜跟娘家彻底断绝关系而嫁给了蓝天，两人一无所有地来到当地，患难与共，历尽艰辛，终于有了现在的财富和地位。徐雅莉很爱孩子，蓝天就想给她一个孩子，无奈却检查出不孕。痛苦的蓝天在夏鸣涛的帮助下进行各种治疗。 　　董佳音在一次采访中救助一个早产孕妇，结识了刚刚回国到妇产医院工作的周一敏。周一敏高超的医术令董佳音钦佩。周一敏其实是夏鸣涛原来大学时期的恋人，但是周一敏在毕业的时候却因为贪慕虚荣，抛弃夏鸣涛，远嫁国外。但是周一敏的婚姻并不幸福，她又被丈夫抛弃，感情遭当年一无所有的张海，今天之所以能有江南丝绸大户的美誉，完全是靠出身富家的何如玉丰厚的嫁妆而发迹。对何如玉，除了夫妻之情，还多了几分敬畏。然而精明的何如玉亦深深地明白，眼前的张海已经不是当年的穷小子，要抓住丈夫的心，最重要的是，要让张家有后。菠萝公司，是一家专做日用百货的地区代理商。风平浪静的日子被老板乔语丝和老板娘关秋月突然间提出分头经营的计划所打破，弄的人心惶惶。抠门的乔语丝为了向妻子证明自己的管理能力，花招百出，但二老板高音又是一个特别爱耍小聪明的人，结果导致公司业务发展并不太顺利。不过机灵的销售总监言立为、妩媚多娇的会计胡娜 等公司员工都凭借着自己的能力在公司找到了一席之地。别看菠萝公司的这一群人整天嬉戏打闹，业务上斗智斗勇，但当公司利益受到侵犯或者是某个同事遇到危机的时候，各个都能挺身而出，相互支持。渐渐的，大家一起经历了种种历练，友情也加深了。新年之夜，无法回家的同事和老板一起包起了饺子。温馨的画面让大家发现在职场中有很多值得珍惜的东西：有人情味的同事、纷杂吵闹却很温馨的公司、表面小气内心却很可爱的老板。Don't pay attention to the title, pure clickbait title, but there are dry goods! Yesterday, I saw a "Daniel" writing an article. On the homepage recommendation, It is called "Learning Artificial Intelligence with String Elder Brother", It was quite a surprise to see the title. After all, there were still bosses willing to write AI articles in the blog park, a technical forum dominated by. Net articles. So I clicked to look at it carefully and found that the style of writing was grandiose. Well, it doesn't matter, just have dry goods. As a result, I didn't find any dry goods in the end. I saw the bibliography, which was very interesting. Put a picture here:Immerse the medicine part into drinking water, stir, normally stand at room temperature for 1-2 hours, filter and boil. If the quantity does not meet the requirements, immerse the filter in a proper amount of boiling water, press slightly, and mix the obtained liquid with the previous liquid.Super Data Manipulator: I am still groping at this stage. I can't give too much advice. I can only give a little experience summarized so far: try to expand the data and see how to deal with it faster and better. Faster-How should distributed mechanisms be trained? Model Parallelism or Data Parallelism? How to reduce the network delay and IO time between machines between multiple machines and multiple cards is a problem to be considered. Better-how to ensure that the loss of accuracy is minimized while increasing the speed? How to change can improve the accuracy and MAP of the model is also worth thinking about.铁蹄过处，南雀军皆人仰马翻。