Intelligence,’ a term he coined 1/62: Arthur Samuel, an IBM computer scientist, creates a self-learning program that proves capable of defeating a top USA checkers champion AI ‘Winter1’ (1967-1996) Trending = Unprecedented37 Machine-Learning Model* Trending = In 2015... Industry Surpassed Academia as Data + Compute + Financial Needs Rose *Machine Learning = A subset of AI where machines learn AI Index data provider, uses the term ‘notable machine learning models’ to designate particularly influential models within the AI/machine learning ecosystem. Epoch maintains a database of 900 AI models

corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters Fine-Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Evaluation Results . demon- strate our efforts in alignment, encompassing Supervised Fine-Tuning (SFT), Reinforcement 5 Learning (RL), the evaluation results, and other discussion (Section 4). Finally, we summarize the conclusion

data science lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative large-scale machine learning  Optimizer and runtime of Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization Algebra for Large-Scale Machine Learning. PVLDB 2016] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Large-Scale Machine Learning. VLDB Journal 2018 27(5)] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. Commun.

TVM: Where are we going Tianqi ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized

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

#2 “Big Data”  MR/Spark: BigBench, HiBench, SparkBench  Array Databases: GenBase  #3 Machine Learning Systems  SLAB, DAWNBench, MLPerf, MLBench, AutoML Bench, Meta Worlds, TPCx-AI Experiments and text Experiments and Result Presentation [Matthias Boehm et al: SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR 2020] 17 706.015 Introduction to Scientific Interpretation [Matthias Boehm et al: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. PVLDB 11(12) 2018] 19 706.015 Introduction to Scientific Writing – 03 Experiments

. . . . . . 7 2.3.2 监督微调（Supervised Fine-Tuning, SFT） . . . . . . 7 2.3.3 强化学习（Reinforcement Learning, RL） . . . . . . . 7 3 DeepSeek-R1 精华图解 . . . . . . . . . . . . . . . . . . . . . . . 7 3.1 DeepSeek-R1 据集，让模型在特定任务上优化表现。调整参数，使其更符合人类需求，如 问答、对话生成等任务。 2.3.3 强化学习（Reinforcement Learning, RL） 采用强化学习（RL）方法进行优化，主要通过人类反馈强化学习（RLHF, Reinforcement Learning from Human Feedback）： 强化学习（RLHF）优化过程 • 步骤 1：人类标注者提供高质量回答。 • 虽然展现出惊人的推理能力提升，但是也出现了回复时 语言混合，非推理任务回复效果差的问题，为了解决这些问题，DeepSeek 提出通用强化学习训练框架。 如图7所示，通用强化学习（General Reinforcement Learning）基于 SFT- checkpoint，模型进行通用强化学习（RL）训练，优化其在推理任务和其他 教程作者：郭震，工作 8 年目前美国 AI 博士在读，公众号：郭震 AI，欢迎关注获取更多原创教程。资

Creating New Footprints Linking Symbols, Footprints, and 3D Models Where To Go From Here More Learning Resources Help Improve KiCad 2 2 2 3 6 7 9 9 9 10 11 13 13 15 17 18 20 20 20 Discord or IRC for real-time discussion with users and developers. Check the KiCad website for learning resources made by the KiCad community. 3 Basic Concepts and Workflow The typical workflow in From Here More Learning Resources For more information on how to use KiCad, see the manual. Other resources include the official KiCad user forum, Discord or IRC, and additional learning resources from

Creating New Footprints Linking Symbols, Footprints, and 3D Models Where To Go From Here More Learning Resources Help Improve KiCad 2 2 2 3 6 7 9 9 9 10 11 13 14 15 17 18 20 20 20 Discord or IRC for real-time discussion with users and developers. Check the KiCad website for learning resources made by the KiCad community. 3 Basic Concepts and Workflow The typical workflow in From Here More Learning Resources For more information on how to use KiCad, see the manual. Other resources include the official KiCad user forum, Discord or IRC, and additional learning resources from

Creating New Footprints Linking Symbols, Footprints, and 3D Models Where To Go From Here More Learning Resources Help Improve KiCad 2 2 2 3 6 7 9 9 9 10 11 13 13 15 17 18 20 20 20 Discord or IRC for real-time discussion with users and developers. Check the KiCad website for learning resources made by the KiCad community. 3 Basic Concepts and Workflow The typical workflow in From Here More Learning Resources For more information on how to use KiCad, see the manual. Other resources include the official KiCad user forum, Discord or IRC, and additional learning resources from