Tag: Machine Learning

12 Posts

【笔记】实用机器学习 – 模型整合
李沐老师实用机器学习笔记: Model Combination 5.1 方差和偏差 5.2 Bagging 5.3 Boosting 5.4 Stacking 模型整合(Model Combination) 授课材料 ?️ 授课视频: 授课视频【斯坦福21秋季:实用机器学习中文版】_哔哩哔哩_bilibili 5.1 方差和偏差 5.2 Bagging 5.3 Boosting 5.4 Stacking ? 课件: 讲义下载地址 7 - Model Combination Bias and variance Bagging Boosting Stacking 偏差(bias)和方差(variance) 定义 参考自: https://zhuanlan.zhihu.com/p/412268381 泛化误差可分解为偏差、方差与噪声之和。 偏差度量了学习算法的期望预测与真实结果的偏离程度,即刻画了算法本身的拟合能力。 方差度量了同样大小的训练集的变动所导致的学习性能变化,即刻画了数据扰动所造成的影响。 噪声则表达了在当前任务上任何学习算法所能达到的期望泛化误差的下界,即刻画了学习问题本身的难度。 偏差度量的是单个模型的学习能力,而方差度量的是同一个模型在不同数据集上的稳定性。 设计机器学习模型时,我们需要考虑: 准。bias表示模型相对真实模型的差异。想要低bias,需要复杂化模型,增加参数,但是容易过拟合造成high variance。low…
【笔记】实用机器学习 – Data 爬虫
网页数据的抓取-爬虫 实验环境 OS: windows 11 Python 3.6 Chrome (我的版本是 98.0.4758.102) 使用的python工具是 selenium. 从https://chromedriver.chromium.org/home下载对应版本的chromedriver。我的chrome版本是98,所以chromedriver也是98。把解压出来的chromedriver.exe 放到工程目录下。 参考文章: https://selenium-python-zh.readthedocs.io/en/latest/getting-started.html 测试样例 from selenium import webdriver options = webdriver.ChromeOptions() options.add_argument('headless') driver = webdriver.Chrome(executable_path="./chromedriver.exe", chrome_options=options) driver.get("https://www.baidu.com/") print(driver.title) driver.close() 流程: 首先打开一个chrome浏览器。指定chromedriver的地址 "./chromedriver.exe"。打开百度网页,输出题目。 通过百度获取天气 xpath 可以通过chrome里右键单击所需元素“检查”,后右键单击元素选择“复制”==》“复制xpath” from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.support.ui import WebDriverWait…
[Reading Notes] Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Source Paper: [ICCV'2017] https://arxiv.org/abs/1703.06868 Authors: Xun Huang, Serge Belongie Code: https://github.com/xunhuang1995/AdaIN-style Contributions In this paper, the authors present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. Arbitrary style transfer: takes a content image $C$ and an arbitrary style image $S$ as inputs,…
[Reading Notes] Collaborative Distillation for Ultra-Resolution Universal Style Transfer
Source Authors: Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan YangPaper: [CVPR2020] https://arxiv.org/abs/2003.08436Code: https://github.com/mingsun-tse/collaborative-distillation Contributions It proposes a new knowledge distillation method "Collobrative Distillation" based on the exclusive collaborative relation between the encoder and its decoder. It proposes to restrict the students to learn linear embedding of the teacher's…
Scaled-YOLOv4: Scaling Cross Stage Partial Network
Scaled-YOLOv4: Scaling Cross Stage Partial Network In this reading notes: We have reviewed some basic model scaling method: width, depth, resolution, compound scaling. We have computed the operation amount of residual blocks, and showed the relation with input image size (square), number of layers (linear), number of filters (square). We…
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Paper Information Paper: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design Authors: Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wang Paper: https://arxiv.org/abs/2009.05697 Github: https://github.com/nightsnack/YOLObile Objective: Real-time object detection for mobile devices. Study notes and presentation: Download: https://connectpolyu-my.sharepoint.com/:p:/g/personal/18048204r_connect_polyu_hk/EcRbix5iqshBglmxuLurS-sBBFmbrk8chRkim1y54-yOXw?e=8Qdfmd This is an…
DANet: Dual Attention Network for Scene Segmentatio
Abstract The paper introduces a position attention module and a channel attention module to capture global dependencies in the spatial and channel dimensions respectively. The proposed DANet adaptively integrates local semantic features using the self-attention mechanism. 摘要 本文引入了位置关注模块和通道关注模块,分别在空间和通道维度上捕捉全局依赖性。 所提出的DANet利用自注意力机制自适应地集成局部语义特征。 Outline Brief Review: attention mechanism, SE net DANet: Dual Attention Network…
Deep Learning 2: Basic Theory of Convolutional Neural Network
Objectives Deep learning is a recently hot machine learning method. The deep learning architectures are formed by the composition of several nonlinear transformations with the goal to yield more abstract and extract useful representations/features. (i) Start with a revision of the basic principle of Neural Networks, neutron structure, examples of…