For codes of the same length, theoretically, the further the coding distance between any two categories, the stronger the error correction capability. Therefore, when the code length is small, the theoretical optimal code can be calculated according to this principle. However, it is difficult to effectively determine the optimal code when the code length is slightly larger. In fact, this is an NP-hard problem. However, we usually do not need to obtain theoretical optimal codes, because non-optimal codes can often produce good enough classifiers in practice. On the other hand, it is not that the better the theoretical properties of coding, the better the classification performance, because the machine learning problem involves many factors, such as dismantling multiple classes into two "class subsets", and the difficulty of distinguishing the two class subsets formed by different dismantling methods is often different, that is, the difficulty of the two classification problems caused by them is different. Therefore, one theory has a good quality of error correction, but it leads to a difficult coding for the two-classification problem, which is worse than the other theory, but it leads to a simpler coding for the two-classification problem, and it is hard to say which is better or weaker in the final performance of the model.
Deep Learning with Python: Although this is another English book, it is actually very simple and easy to read. When I worked for one year before, I wrote a summary (the "original" required bibliography for data analysis/data mining/machine learning) and also recommended this book. In fact, this book is mainly a collection of demo examples. It was written by Keras and has no depth. It is mainly to eliminate your fear of difficulties in deep learning. You can start to do it and make some macro display of what the whole can do. It can be said that this book is Demo's favorite!
枪林弹雨之中，随父母一起转移的留美归国少女丁芷寒，在地下党员祝中华的舍身相救之下侥幸逃脱。然而，她的父母却跟其他所有科学家一样难逃厄运，暴尸街头。她的救命恩人祝中华也因受伤被捕。多次行动的失败，使上海地下党认识到自己队伍中隐藏着一个叛徒，他就象一根毒刺般深刺在我地下党组织中。而且，这数次失败的行动都是由特别行动组组长祝中华负责，偏偏每次也只有他能够从敌人的包围圈中全身而退。
指倚天剑中的武功秘籍，只要勤加修炼，便可纵横江湖，谁与争锋。
4. Exposure Compensation
皇家侍卫展熊飞被贬到偏僻的琼鹿县当捕快，但他刚到不久，该县就发生了命案。县太爷的儿子包小包与展雄飞立刻组成了专案小组，需在皇城考察团驾临之前限期破案。可惜事情越来越失控，死亡案件缕缕发生，两人也掉入了凶手罗织的巨大阴谋网中，随着线索增多，如果说这个神秘县城跨越十余年的连环命案是一座冰山，两人已经渐渐接近了水面之下的庞然大物。
There are hundreds of thousands of manuscripts in this article. If you can't see the update in Jinjiang, go in from the last chapter!
"Showtime"曾被用作剧名，表示湖人队的"showtime era"。但由于与美国有线台Showtime撞名，并且该台是HBO竞争对手，剧集为避免混淆而放弃了沿用书名。
俗话说，三百六十行，行行有本难念的经。现代社会压力越来越大，本剧通过主角阿毛的一系列夸张而爆笑的工作遭遇，深情演绎（疯狂吐槽）各行各业的心酸事、奇葩事。
也许要不了多久，我也会坐在你这里。
90年代初，孤女简小爱因家境贫困被迫辍学，辗转来到广州打工。在广州，她当过餐馆小工，外贸公司茶水小妹；凭借聪明、善良以及jijiKb.com敢打敢拼的斗志，成长为出色的外贸跟单员和业务经理。但简爱并不满足于已经实现的身份跨越，几经周折，她成立了自己的外贸公司，并且在经历了98年东南亚金融风暴和2008年金融危机之后，依托中国大陆改革开放的成果，依托不断蓬勃发展的中国外贸大势，最终成长为跨国集团老总。在成长路上，简爱也曾经被诱惑，也曾经迷惘，也因为走捷径而付出代价，但最终，她穿越了所有的迷惘，凭借自己的奋斗，取得了事业成功。而在寻找到自己的人生价值的同时，简小爱与马来西亚华商林恒之的感情，也在经历了风雨之后，从最初的男强女弱走向势均力敌，并成为最终的人生伴侣。
田遥在他们身后找了一圈。
有村架纯将主演人气漫画《前科者》改编的同名电影和日剧，导演是2018年凭借《啊，荒野》获得众多奖项的岸善幸。
FBI特别调查员OLIVIA DUNHAM（新人Anna Torv饰演）被叫到现场进行调查。在她的搭档，特别调查员JOHN SCOTT（Mark Valley饰演，曾出演过《波士顿法律》）调查中险些被杀时，绝望的Olivia疯狂的找寻着能帮助她的人，无意中她发现了 DR. WALTER BISHOP（John Noble饰演，曾出演过《指环王之王者归来》）我们这年代的“爱因斯坦”。但他近20年来都被收容，要想和他有所交流，那就必须在他疏离已久的儿子 peter（Joshua Jackson饰演，曾出演过《恋爱时代》）的带领下 。
"Well-how do you say this? According to the appearance, it should be a dog, but the dog is very ugly. I don't recognize what breed it is, and I have never seen such a fierce dog." Liu Guangyuan said.
温州女人春枝只身闯荡京城，来到何教授家当保姆，她此行的真正目的是为女儿择校。春枝生性活泼，而何教授则古板刻薄，两人经常为琐事发生分歧，由于何妻不久前在家中自杀，他目前正处在巨大的痛苦与自责中。在海外的女儿田田因个人婚姻亮起了红灯，无暇顾及同样痛苦的父亲，春枝正是在这时踏进了何家的大门。面对一个农村女人，身为艺术史教授的何淳安简直与春枝没有一点共同语言，琢磨着怎样将她赶走，而春枝则想兑现对女儿的承诺，先在北京站稳脚跟。最终，同样因为对各自女儿的爱，让两个本不能相处的人完成了对方的心灵救赎。
年代巨作《富贵在天》讲述了一个1914年发生在一个富商家庭里的情感故事。富商千金卓曼君与下人朱德纲好不容易获得了家族许可得以成婚，却因朱德纲船难失踪而分离。为了寻找女婿，卓父亲自下江南，不料死在恶仆富贵的手中。卓曼君失去了一切。富贵欲乘机取代朱德纲并侵占卓家的财产，卓曼君识破了富贵，在义仆高大海的相助下，带着孩子渡过了难关。后因别无选择，她嫁给了高大海。七年之后，一个偶然的机缘，卓曼君与朱德纲突然相遇。朱德纲仍然坚守着当初的承诺，卓曼君却不得不面对前夫有情、后夫有义的两难处境。
第三节的纪念年，与一个课长有很大关系的新出演者加入！！
