useful links about machine learning

机器学习与深度学习教程
最新推荐文章于 2025-09-05 14:24:44 发布
最新推荐文章于 2025-09-05 14:24:44 发布
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文章标签： 人工智能
原文链接：http://www.cnblogs.com/guohaoyu110/p/7078580.html
本教程提供关于机器学习及深度学习的基础知识介绍，包括章节资料与配套视频课程资源，适合初学者入门与进阶学习。
AdamTechLouis's talk: Deep Learning with Knowledge Graphs
weixin_41697507的博客
06-03 422
Last week I gave a talk at Connected Data London on the approach that we have developed at Octavian to use neural networks to perform tasks on knowledge graphs. Here’s the recording of the talk, from ...
Andrej Karpathy: 软件 2.0 —— 软件（1.0）正在吞噬世界，而现在人工智能（软件2.0）正在吞噬软件
AI天才研究院
09-28 392
最后，也是最重要的是，神经网络是比你或我在大部分有价值的垂直领域中所能想到的任何代码都更好的代码，这些垂直领域目前至少涉及与图像/视频和声音/语音有关的任何内容。然而，在软件 2.0 中，我们可以采用我们的网络，删除一半的通道，重新训练，然后它的运行速度恰好是原来的两倍，但工作效果会更差。从长远来看，这种范式的未来是光明的，因为越来越清楚的是，当我们开发AGI时，它肯定会用软件2.0来编写。在许多应用领域，我们可以选择使用我们理解的 90% 准确率的模型，或者我们不理解的 99% 准确率的模型。
A Tour of Machine Learning Algorithms
omnispace的博客
09-20 809
In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so m
Coursera | Mathematics for Machine Learning 专项课程 | Linear Algebra
NJ_Xavier的博客
01-09 3350
本文为学习笔记，记录了由Imperial College London推出的Coursera专项课程——Mathematics for Machine Learning中Linear Algebra中全部Programming Assignment代码。
Machine Learning Trends from NIPS 2014
klzhang的专栏
03-14 954
Machine Learning Trends from NIPS 2014 ML Blog Team 16 Dec 2014 6:00 AM 3 This blog post is authored by John Platt, Deputy Managing Director and Distinguished Scientist
An Introduction to Hashing in the Era of Machine Learning
omnispace的博客
05-08 1558
In December 2017, researchers at Google and MIT published a provocative research paper about their efforts into “learned index structures”. The research is quite exciting, as the authors state in the ...
Machine Learning Overview
好习惯成就伟大
12-19 816
Training Data In machine learning algorithms there is notion of training data. Training data includes several components: A set of training samples. Each training sample is a vector of values (in Co...
Courses of Data Mining & Machine Learning & Pattern Recognition
fovwin的专栏
05-05 2341
Data Mining The subject of Knowledge Discovery and Data Mining (KDD) concerns the extraction of useful information from data. Since this is also the essence of many sub-areas of computer sc
Machine learning for predictive maintenance: where to start?
思索的蜗牛的专栏
01-22 728
Ref: https://medium.com/bigdatarepublic/machine-learning-for-predictive-maintenance-where-to-start-5f3b7586acfb Think about all the machines you use during a year, all of them, from a toaster every m...
【论文翻译】Machine learning: Trends,perspectives, and prospects
pp1234234的博客
07-31 1524
Machine learning: Trends,perspectives, and prospects M. I. Jordan1* and T. M. Mitchell2* 机器学习：趋势、观点、前景 M. I. Jordan1* and T. M. Mitchell2* Machine learning addresses the question of how to build computers that improve automatically through experience. It i
机器学习(Machine Learning)&深度学习(Deep Learning)资料 <2>
yangminggg 的博客
10-26 1367
    机器学习(Machine Learning)&amp;深度学习(Deep Learning)资料(Chapter 2)   《Image Scaling using Deep Convolutional Neural Networks》 介绍:使用卷积神经网络的图像缩放. 《Proceedings of The 32nd International Conference on M...
aws 机器学习_我如何为AWS机器学习专业认证做准备
weixin_26731327的博客
10-15 2677
aws 机器学习介绍(Introduction) I have just recently passed my AWS Certified Machine Learning — Speciality exam with over 95%, with about 4 weeks of explicit preparation.我最近刚通过我的AWS认证机器学习-专业考试，合格率超过95％，经过大约4...
机器学习数据集_机器学习数据集的选择
cullen2012的博客
09-10 1739
机器学习数据集 Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the e...
【Advanced】Image Depth Estimation in MATLAB: Using Deep Learning for Image Depth Estimation
A Deep Neural Network (DNN) is a machine learning model with multiple processing units (neurons) organized in layers connected by weighted links. The architecture of a DNN typically consists of an ...
STM32传感器模块编程实践(十六)DIY人脸识别智能垃圾桶模型
光子物联
09-05 737
本文介绍了一种基于STM32单片机的人脸识别智能垃圾桶模型。该系统由STM32F103C8T6主控芯片、ASR-PRO语音交互模块、SG90舵机和人脸识别模块组成。语音模块用于验证口令，只有口令正确才能录入人脸；识别成功后舵机带动垃圾桶盖打开。控制流程包括人脸识别、语音交互和舵机驱动，通过串口指令实现人脸录入与识别。系统采用STM32CubeMX配置GPIO、PWM和串口参数，Keil5编写控制程序，最终实现人脸识别控制垃圾桶
怎么快速构建一个deep search模型呢
最新发布
qq_57565004的博客
09-05 695
本文提出了一种快速构建具备深度信息检索能力的智能体系统（DeepSearch）的方法。该方法采用分阶段开发策略：首先基于RAG架构搭建基础检索链路，再通过LangChain框架整合大语言模型和搜索引擎API，赋予系统自主规划与迭代搜索能力，最终实现多源信息整合和结构化报告生成。技术选型上强调利用开源工具（如LangChain、ChromaDB）和预训练模型，通过"思考-检索-整合-迭代"的智能体工作流，高效构建可验证原型的核心功能。
2025年09月03日最热门的开源项目(Github)
09-04 633
模型常见训练超参数介绍(1)
my java博客园
09-02 1735
【摘要】本文总结了BERT/RoBERTa模型调参的关键策略：1）训练轮数需平衡过拟合与欠拟合，NER任务通常10+轮，分类任务3-5轮，推荐使用早停机制；2）学习率建议2e-5~5e-5，过高易震荡，过低收敛慢；3）batch size需适配显存，大batch需配合高学习率；4）warmup steps设为总步数5-20%，稳定训练初期；5）weight decay建议0.01-0.1，防止过拟合。调参时应监控训练/验证损失曲线，结合任务复杂度动态调整参数组合。（149字）
机器学习基础-day03-机器学习中的线性回归
XZSSWJS的博客
09-04 844
初学小小白，个人笔记使用，仅供参考
how are machine learning used in math proof?
04-01
Machine learning can be used in math proof in the following ways: 1. Automated theorem proving: Machine learning algorithms can be trained to automatically prove mathematical theorems. The algorithm ...
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