When she came to Qingdao, she had to mention Kang Yue, the pride of Qingdao women's lifting in recent years. She was not only the champion of the National Games and the national champion, but also the world champion in the World Championship. In her heyday, Kang Yue's result in the 75kg category was 30kg higher than that of the domestic runner-up, almost the king of 75kg. Speaking of the day when I first arrived at the sports school, Liu Eryong still remembers vividly, "I saw her for the first time when I was selecting materials. The little girl was dirty in this school uniform. Her family lived in a small temporary house built by the sea. Her parents made a living by fishing for sea vegetables and her family was very poor. However, she has a very good talent and worked very hard after choosing a sports school. She almost never missed classes or asked for leave. Sometimes when she was injured, she was not allowed to practice and let her rest. The child cried and begged us whether my arm was injured and I could practice my legs and waist. Don't let me rest. With talent and hard work, Kang Yue was chosen by the province after less than two years of practice in our country. In the third year, she won the championship of the City Games and began to rule the women's 69 and 75 kg categories. She ruled for 10 years. The result of Shenyang National Games was 30 kg higher than that of the second place and 50 kg higher than that of the 2012 Olympic champion. This gap is amazing! "年轻的卢卡斯和他的宗教姑妈住在一个安静的乡村小镇上。但这个小小的焦虑生活是由慈善阿姨宣布刚从监狱里出来的另一个侄子马里奥的到来而来的。字数超过限制, 部分内容不再显示出来.Good book!如此一来，郦食其可以说直接为韩信帮了一个大忙，齐国毫无防备的情况下，直接发起进攻。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 ~ 为参加葬礼，克莉丝蒂（Nora Zehetner 饰）返回了阔别已六年之久的家乡。当年的梦魇仍在继续，她受到姐夫及其家人的排斥，但是她始终认为姐姐的死另有隐情。克莉丝蒂偷偷展开调查，却发现这一连串罪恶的真相都与她多年来的噩梦息息相关……
