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 ~
李敬文给爹娘请过安，出了上房，站在院子里静静仰望天空。
我有一种感觉，下一期的剧情势必是‘文不惊人死不休的超神转折。
From the defender's point of view, this type of attack has proved (so far) to be very problematic, because we do not have effective methods to defend against this type of attack. Fundamentally speaking, we do not have an effective way for DNN to produce good output for all inputs. It is very difficult for them to do so, because DNN performs nonlinear/nonconvex optimization in a very large space, and we have not taught them to learn generalized high-level representations. You can read Ian and Nicolas's in-depth articles (http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-attaching-machine-learning-is-easier-than-defending-it.html) to learn more about this.
影片讲述了在某舞蹈学校中，几名参加培训的年轻男女无意触碰了恐怖“午夜十二点”的禁忌，之后他们进入了这个学校最神秘的127无人宿舍，紧接着恐怖灵异事件接连频发：白衣游魂、床下鬼手、夜半钟声纷纷显现，发疯、死亡的诅咒时刻笼罩着他们，而后他们意外得知127宿舍竟然是该学校的恐怖禁地，凡是午夜十二点接近该宿舍的人都将受到诅咒，他们能够顺利逃脱这个恐怖诅咒吗？
For example, if you use Word to make a test paper, a test paper is usually divided into two or three columns, and the page number should be displayed under each column.
殷离表妹逝世，我非常难过。
可张爷爷张奶奶不许，说胡闹，说这不是吃顿饭的事，这是开门立户的大事，要祭祖的，回头秦家的祖宗要生气的，说儿孙连祖宗都不要了。
葫芦没吱声。
纺织教授Rerin(Aff)需要时间思考和未婚夫Thanin的关系，于是到清迈度假，在那儿她邂逅了皇族后裔、餐厅老板Suriyawong(Aum)。Suriyawong对Rerin一见钟情并深信她就是期盼已久的真名天女。可Suriyawong的亲戚Wongprajan从中作梗，谎称自己是他的未婚妻。
这两位宛如神衹一般强大的对手于一场壮观的战争中相遇，彼时世界命运正悬于一线。为了找到真正的家园，金刚与他的保护者们踏上了一次艰难的旅程。与他们一道前行的还有一个年轻的孤儿女孩——吉雅，这个女孩与金刚之间存在着一种独特而强大的紧密联系。但意想不到的是，他们在前行的航道上与愤怒的哥斯拉狭路相逢，也由此在全球引起了一系列破坏。一股无形的力量造成了这两只巨兽之间的巨大冲突，深藏在地心深处的奥秘也由此揭开。
女主人公姜宪重生之后，依靠自己力量平衡朝堂各方势力，最终得到幸福的励志故事，堪称一部“开国皇后成长记”。
Blood type with leadership ability from an early age
故事描述一个爱尔兰女人（Sharon Horgan）与一个美国男人（Rob Delaney）在英国伦敦相遇并相爱，但对文化差异巨大的两人来说，在一起生活并非易事。
这是一部以秋山香织的漫画为基础的爱情喜剧，以“长鹿之羽”而著称。 主题是妄想和美食，它描绘了一家出版公司Madoka Tokoro的漫画编辑人员，他吃了与他相同的食物并重新体验以接近男性销售人员Naoya Hachikaku。
千王司徒省（潘志文 饰）在不同场合下认识各具能力的七个人，包括：千术了得的翟冠一（陈展鹏 饰），魔术师伍柏义（陈山聪 饰），雀后何正花（林夏薇 饰），MMA拳手丁权（林子善 饰），负责接收情报的吴世峰（黄长兴 饰），接载走佬车手李享（徐荣 饰），以及千脸变身女郎苏丽芬（赵希洛 饰），将她们招揽到自己旗下的老千团队。
可既然出来了，又不能不理会。
香荽道：说说笑笑的，就觉不得晕船了。
香儿抿嘴一笑，扯着他胳膊踮起脚。
