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出狱不久的神秘男子，终日游荡在这个城市里，三年前的这个城市还有温暖，如今只剩下了恨，他只有两个愿望，把女儿葬在一直向往的南山，然后杀掉那个毁灭他生活的人。血液病医生林萧峰，保险推销员纪凡，两人人生轨迹本无交集，但因为同样的问题生生的撞在了一起。Free throw:　　本来风牛马不相及，八竿子打不到一起的两个人，却因为孔雅婷无意间脱口而出的一个荒唐透顶的谎言，无奈地被卷入到一场结婚绯闻中,从此两个南辕北辙的人的命运交织在了一起。而玄奇俊的弟弟玄尚希（成俊 饰）对于这个男孩子气的假嫂子心生爱慕，暗暗展开追求。同样，玄基俊的前女友吴允珠（赵允熙 饰）从巴黎回来，对他念念不忘，展开了争夺战。这场四人感情大战会有如何意外的结局呢？凄美苍凉的情感传奇，寂寞凋零的绚丽生灵。一座建在悬崖峭壁上的客家围屋，有着奇诡怪异的历史。从清末至抗日战争爆发，更是在恐怖中衍生出几分荒唐与传奇：方圆百里的老少寡妇们齐集围屋，名为“清洁堂”，为的是用幽闭来保持她们的名节。可怜寡妇们只有面对孤灯，度日如年，在寂寞中凋谢着各自绚丽的生命……寡妇中最可敬的是堂主阿芸婆，美丽、善良的阿芸婆，丈夫惨遭不幸。为了保护儿子朱梁和自家财产，阿芸婆毅然带产入围，从此骨肉分离。外表端庄、贤淑的阿芸婆与表兄金标相互恋爱，在封建礼教的压迫下，有情人难成眷属。身为地下党的金标为了工作与同是共产党的杨飞燕假扮夫妻，深深刺痛了阿芸婆，心爱的儿子朱梁又被族人害死，阿芸婆痛不欲生，万念俱灰。寡妇中最可怜的是18岁的豆苗：新婚之夜，新郎暴死，她被送进清洁堂，但幽深的大院关不住少女对爱情的渴望，她与小叔子偷情怀孕，被割舌毁容，以身徇情；寡妇寡妇中最可悲的是戏子五娘。风情万种的五娘，贪慕虚荣，嫁入豪门为妾。后因与曹副官偷欢，被遣入清洁堂。不甘寂寞的她在堂中惹事生非，屡遭堂规惩懒癌患者安安是一家广告公司的插画师，拥有自由随性的性格加上天马行空的想法，每当繁重的工作之后亦或是灵感来了，安安都会为自己做一道“懒人餐”来满足自己的味蕾，同时记录下自己的心情，这个习惯招来了死党加同事加邻居加学长加上爱占便宜性格的卫斯理天天踩着饭点来蹭饭。一天广告公司突然空降了一位顶头上司李睿凡来做管理，不苟言笑的高富帅彻底打破了安安清闲自在的生活，另一边古灵精怪的表妹余果又要长期寄居在家里，正在安安焦头烂额之际，咖啡店老板娘林琦的回归给了安安一丝安慰，但她似乎也是带着麻烦回来的，此时睿凡的哈佛同学亚当穷游世界之后来投靠睿凡，欣喜的林琦以为亚当是追她而来，却发现亚当完全不记得她了，在这个公司、社区、咖啡店里六个人的故事发生着......Updated July 32. As a Cheng Yuan who has been engaged in machine learning for more than two years, seeing the computer series you recommended also thinks it is very unreliable. I have read most of the books you recommended. In particular, novice readers are not recommended to read "Introduction to Algorithms" and "Data Mining: Concepts and Technologies". These two books are thick and heavy. Although the content is also good, you will not know the year of the monkey when you finish reading them. What does a novice need? It's getting started! Secondly, have you really finished reading Python Core Programming? This book is not for Python newcomers. It is very thick and difficult. It is very unfriendly to newcomers. And if you just want to be AI, then many parts of this book don't need to be used. Is web development and Django framework really necessary for our AI engineers? No. Xiao Bai has no distinction between key and non-key points in a book. He spent a lot of time learning unnecessary knowledge, which really does more harm than good. Giving guiding and targeted recommendations is responsible recommendation.