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

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

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

arXiv:1810.04805, 2018. [3] Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference 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 in e-commerce search[J]. arXiv preprint arXiv:1805.08524, 2018. [11] Ai Q, Bi K, Guo J, et al. Learning a deep listwise context model for ranking refinement[C]//The 41st International ACM SIGIR Conference

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

= 16) ■ Survey with Rust community (S = 178) 4 Learning Rust ■ Rust is hard to learn. 5 Rust has “a near-vertical learning curve.” 6 Learning Rust ■ Rust is hard to learn. ■ Rust is more difficult than other languages 7 Learning Rust ■ Rust is hard to learn. ■ Rust is more difficult to learn than other languages ■ Easy to find solutions to problems 8 Learning Rust ■ Rust is hard to learn of the time the compiler is very, very good at telling you exactly what the problem is” 10 Learning Rust ■ Rust is hard to learn. ■ Rust is more difficult to learn than other languages ■ Easy

相对于机器学习，严重依赖于高端机，大量的 GPU 运算 • 高端机的成本高 开发工具 • 复杂的工具链 • 搭建环境花费时间 深度学习 à Azure Machine Learning 开发工具 à Azure Notebook Azure Machine Learning • 拥有不同运算性能的机器 • 降低成本，按需付费 • 支持不同的开源框架：TenserFlow、PyTorch、MXNet 等 Azure Azure Machine Learning SDK 2. 连接到 Azure Machine Learning 的 Workspace 3. 创建远程运算资源 4. 上传训练数据 5. 准备 training script 6. 把 training 任务提交到 Azure Machine Learning 1. 安装 Azure Machine Learning SDK 2. 连接到 连接到 Azure Machine Learning 的 Workspace 3. 创建远程运算资源 —— NC6 GPU machines 4. 上传训练数据 5. 准备 training script 6. 把 training 任务提交到 Azure Machine Learning 1. 创建 experiment 2. 创建 PyTorch estimator 3. 提交 training