2016的目标：Web爬虫＋深度学习＋自然语言处理 ＝ ？ Microso� Apple AWS 今年最激动人心的事件？ 2016.1.28 “Mastering the game of Go with deep neural networks and tree search” 今年最激动人心的事件？ 2016年3月Alphago 4:1 击败李世石九段 人工智能 VS. 机器学习 VS. 深度学习 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

How Deep Do You Go? Contributing to the os Package Oliver Stenbom July 25th Gophercon 2019 On an unsuspecting Monday last July, the team I was working at the time received a bug report. The report

Solving Nim by the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes Thesis submitted for the degree of Master in Informatics: Programming and Networks the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes c⃝ 2019 Mikael Nielsen Røykenes Solving Nim by the Use of Machine Learning http://www.duo.uio 3.4 The Sprague-Grundy Theorem . . . . . . . . . . . . . . . . . . . 6 4 Machine Learning 6 4.1 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . 7 4.1.1 The Principle . . . . .

第三届中国 Rust 开发者大会 Learning by Contributing to Rust Compiler Yukang github.com/chenyukang Engineer @ Cryptape Leveling Up in Rust • 2011 ~ 2014 EDA startup C/C++ • 2014 ~ 2020 DJI the best team What I’ve learned • Stay curious, learn by doing • You don't need to master Rust; learning Rust by hacking Rust compiler is a great way • Treat it as a game, have fun, remain patient

Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu adrian.stanciu.pub@gmail.com CppCon, 2023 1 / 50About me ◮ I am a software engineer from Romania ◮ I have Image classification ◮ Hand gesture recognition ◮ Summary ◮ Q&A 4 / 50Motivation 5 / 50Machine Learning (ML) ◮ Subfield of Artificial Inteligence (AI) ◮ Enables computers to learn from data and then consumption ◮ May have real-time performance constraints 7 / 50Machine learning on embedded devices ◮ Alternative to cloud-based machine learning ◮ Advantages: ◮ Real-time processing ◮ Low latency ◮ Reduced bandwidth

micrograd++: A 500 line C++ Machine Learning Library Gautam Sharma Independent Researcher gautamsharma2813@gmail.com Abstract—micrograd++ is a pure C++ machine learning li- brary inspired by Andrej Karpathy’s for building and training machine learning models. By leveraging the performance efficiency of C++, micro- grad++ offers a robust solution for integrating machine learning capabilities directly into C++-based Traditionally, all machine learning libraries are extremely bulky and very hard to integrate as third party dependencies. This aspect scares practitioners to adopt a C++ based machine learning library for prototyping

第三届中国Rust开发者大会 Rust Atomic Deep Dive Pu Wang @ DatenLord 2023/06/17 Rust原子操作高性能实践 What are atomic operations in Rust? What Why need atomic operations? Why How 01 02 03 Memory order in atomic operations

进行特征的高阶组合。 模型结构 我们的模型结构参考 AutoInt[3] 结构，但在实践中，根据美团搜索的数据特点，我们 对模型结构做了一些调整，如下图 2 所示： 图 2 Transformer&Deep 结构示意图 算法 < 27 相比 AutoInt[3]，该结构有以下不同： ● 保留将稠密特征和离散特征的 Embedding 送入到 MLP 网络，以隐式的方式 学习其非线性表达。 Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. [3] Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive e-commerce recommendation in Alibaba[C]//Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. 2019: 1-4. [5] Pei C, Zhang Y, Zhang Y, et al. Personalized

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