文的自动分类 半监督学习 - 介于监督学习和无监督学习之间，算法: Graph Inference 或者Laplacian SVM 强化学习- 通过观察来学习做成如何的动作, 算法：Q-Learning以及时间差学习 机器学习－ 方法及流程 输入特征选择 – 基于什么进行预测 目标 – 预测什么 预测功能 – 回归、聚类、降维... Xn -> F(xn) -> T(x) 机器学习－ (NYU,2002), Facebook AI, Google Deepmind Theano (University of Montreal, ~2010), 学院派 Kersa, “Deep Learning library for Theano and TensorFlow” Caffe (Berkeley)，卷积神经网络,贾扬清 TensorFlow (Google) Spark MLLib

et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. [2] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications[J] [3] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. [4] Goodfellow I, Bengio Y, Courville A. Machine learning basics[J]. Deep learning, 2016, 1(7): 98-164. [5] Paillier P. Public-key residuosity classes[C]//International conference on the theory and applications of cryptographic techniques. Springer, Berlin, Heidelberg, 1999: 223-238. [6] Lo H K. Insecurity of quantum secure computations[J]

et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. [2] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications[J] [3] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. [4] Goodfellow I, Bengio Y, Courville A. Machine learning basics[J]. Deep learning, 2016, 1(7): 98-164. [5] Paillier P. Public-key residuosity classes[C]//International conference on the theory and applications of cryptographic techniques. Springer, Berlin, Heidelberg, 1999: 223-238. [6] Lo H K. Insecurity of quantum secure computations[J]

et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. [2] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications[J] [3] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. [4] Goodfellow I, Bengio Y, Courville A. Machine learning basics[J]. Deep learning, 2016, 1(7): 98-164. [5] Paillier P. Public-key residuosity classes[C]//International conference on the theory and applications of cryptographic techniques. Springer, Berlin, Heidelberg, 1999: 223-238. [6] Lo H K. Insecurity of quantum secure computations[J]

et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. [2] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications[J] [3] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. [4] Goodfellow I, Bengio Y, Courville A. Machine learning basics[J]. Deep learning, 2016, 1(7): 98-164. [5] Paillier P. Public-key residuosity classes[C]//International conference on the theory and applications of cryptographic techniques. Springer, Berlin, Heidelberg, 1999: 223-238. [6] Lo H K. Insecurity of quantum secure computations[J]

Data + Machine Learning[R1] + Powerful Computation[R2] • 完全意义上的自下而上的方式 • 从海量的数据中去挖掘异构、动态、碎片化的知识 e.g., 从Web corpora、搜索日志等都可挖掘出有价值的知识 R1, http://www.erogol.com/brief-history-machine- learning/ R2, https://openai Pipeline of Knowledge Graph Construction by Data-driven manner Figure 1: Data-driven KG construction techniques 刘峤，李杨等人, 知识图谱构建技术综述, 计算机研究与发展, 53 (3)，2016  Data acquisition • 结构化的数据（工业界常用） • 半结构化的数据（工业界常用）

and more and more schools to open source courses. We hope the follow-up can be achieved in the learning of computers, compiling principles, software engineering, and other theoretical knowledge at most eye-catching one in China is PingCAP/TiDB, whose open source strategy and tactics are worth learning. 堵俊平：这两年，一个很明显的趋势是越来越多的初创企业参与开源。这一方面得益于 ToB 赛道成为市场和政策导向的热点，另一方面开源所代表的开放式创新也被投资界所认 可。尤其是开源与数据（数据库&大数据）以及 communicate, which can be open and transparent, and settle down the discussion process and reduce the learning cost of new entrants. Domestic developers are currently used to discussing issues in WeChat

示等等。 很多人不知道的是，看似简单的个性化信息展示背后，涉及大量的数据、算法以及工程架构技术，这些足 以让大部分互联网公司望而却步。究其根本原因，个性化信息展示背后的技术是排序学习问题（Learning to Rank）。市面上大部分关于排序学习的文章，要么偏算法、要么偏工程。虽然算法方面有一些系统性 的介绍文章，但往往对读者的数学能力要求比较高，也比较偏学术，对于非算法同学来说门槛非常高。而 得到预测函数。 Wikipedia的对机器学习定义如下： “Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to learn with 什么是排序学习？ 什么是排序学习？ Wikipedia的对排序学习的定义如下： “Learning to rank is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for

NeurIPS 2021 ｜ Twins：重新思考高效的视觉注意力模型设计 339 目录 iv > 2022年美团技术年货 美团获得小样本学习榜单 FewCLUE 第一！ Prompt Learning+ 自训练实战 353 DSTC10 开放领域对话评估比赛冠军方法总结 368 KDD 2022 | 美团技术团队精选论文解读 382 ACM SIGIR 2022 | 美团技术团队精选论文解读 ConvNets Great Again, https://arxiv.org/ pdf/2101.03697 [5] CSPNet: A New Backbone that can Enhance Learning Capability of CNN, https://arxiv.org/abs/1911.11929 [6] Path aggregation network for instance abs/2103.14259 [8] Computer Architecture: A Quantitative Approach [9] SIoU Loss: More Powerful Learning for Bounding Box Regression, https:// arxiv.org/abs/2205.12740 6. 作者简介 楚怡、凯衡、亦非、程孟、秦皓、一鸣、红亮、林园等，均来自美团基础研发平台

daunting when first trying to grasp the basics. However, with a bit of motivation and the right learning material, it is possible to familiarize yourself with Blender after a few hours of practice. This tool. Great artists do not create masterpieces by pressing buttons or manipulating brushes, but by learning and practicing subjects such as human anatomy, composition, lighting, animation principles, etc you see any name end with (, then that is a function. We will make use of this a lot to help our learning the API faster. Now that you got a hang of this, lets proceed to investigate some of modules in