<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[2302_77608969的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/2302_77608969</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; 2302_77608969]]></copyright><item><title><![CDATA[OpenGL Programming Guide (7th Edition) - Chapter 1]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144027299</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144027299</guid><author>2302_77608969</author><pubDate>Fri, 10 Jan 2025 14:33:48 +0800</pubDate><description><![CDATA[假设绘制一帧画面几乎要花费整整 1/24 秒的时间。那么在绘制这一帧的过程中，最先绘制出来的那些图形元素（比如画面中的某些物体、线条等）会在整个 1/24 秒内一直保持可见，并且在屏幕上呈现出一个相对完整、稳定的图像。然而，那些接近这一帧末尾才绘制出来的图形元素就比较 “倒霉” 了，因为当程序开始着手绘制下一帧（也就是要先清除当前屏幕内容，然后开始新的绘制流程）时，它们几乎刚被画出来就马上被清除掉了，根本来不及在屏幕上完整地显示一段时间，供观众的眼睛和大脑形成稳定的视觉印象。]]></description><category></category></item><item><title><![CDATA[Perlin Noise算法]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144961184</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144961184</guid><author>2302_77608969</author><pubDate>Mon, 06 Jan 2025 13:41:04 +0800</pubDate><description><![CDATA[为了得到更平滑的噪声，有一种算法——Perlin noise：将随机的数字序列变为平滑连接的明暗板块。白噪声：像素点0-1之间随机赋值。噪声本质是随机性的表现。]]></description><category></category></item><item><title><![CDATA[Three.js Journey (notes4)]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144917217</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144917217</guid><author>2302_77608969</author><pubDate>Sat, 04 Jan 2025 15:27:08 +0800</pubDate><description><![CDATA[【代码】Three.js Journey (notes4)]]></description><category></category></item><item><title><![CDATA[Three.js Journey (notes3)]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144896711</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144896711</guid><author>2302_77608969</author><pubDate>Fri, 03 Jan 2025 20:09:13 +0800</pubDate><description><![CDATA[【代码】Three.js Journey (notes3)]]></description><category></category></item><item><title><![CDATA[Three.js Journey (notes2)]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144797134</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144797134</guid><author>2302_77608969</author><pubDate>Thu, 02 Jan 2025 22:40:15 +0800</pubDate><description><![CDATA[【代码】Three.js Journey (notes2)]]></description><category></category></item><item><title><![CDATA[Three.js Journey (notes)]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144771726</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144771726</guid><author>2302_77608969</author><pubDate>Sat, 28 Dec 2024 21:30:11 +0800</pubDate><description><![CDATA[除了three.js核心库以外，在threejs文件包中。three.js库可以在threejs官方文件包下面的。则更像是一个项目的全局管理和描述文件。目录下，还可以看到各种不同功能的扩展库。更像是一个具体的构建配置文件，而。]]></description><category></category></item><item><title><![CDATA[Seam Carving For Content-Aware Image Resizing]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144266009</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144266009</guid><author>2302_77608969</author><pubDate>Thu, 12 Dec 2024 17:58:03 +0800</pubDate><description><![CDATA[关键词：Image retargeting, image resizing, image cropping。]]></description><category></category></item><item><title><![CDATA[Segment Anything - Meta AI]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/144175810</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/144175810</guid><author>2302_77608969</author><pubDate>Sun, 01 Dec 2024 23:23:51 +0800</pubDate><description><![CDATA[Core：发布了 Segment Anything Model（SAM）和相应的有 10 亿个掩码及 1100 万张图像的数据集（SA-1B），网址为。]]></description><category></category></item><item><title><![CDATA[AI For Everyone - Week 4 - AI and Society]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143906300</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143906300</guid><author>2302_77608969</author><pubDate>Wed, 20 Nov 2024 11:51:18 +0800</pubDate><description><![CDATA[The way an AI system stores words is using a set of numbers. AI systems are already making important decisions today, and will continue to do so in the future as well. So, bias matters.So, it is susceptible to if an adversary makes changes to a picture tha]]></description><category></category></item><item><title><![CDATA[AI For Everyone - Building AI In Your Company - Week 3]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143748851</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143748851</guid><author>2302_77608969</author><pubDate>Wed, 20 Nov 2024 11:12:59 +0800</pubDate><description><![CDATA[This week you'll see you how multiple modules can come together to build a much more complex AI product such as a smart speaker or a self-driving car. You also learn what are the major roles in an AI team.  how do you build a piece of AI software to unders]]></description><category></category></item><item><title><![CDATA[MATLAB Learning Roadmap （1）]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143483220</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143483220</guid><author>2302_77608969</author><pubDate>Sat, 09 Nov 2024 21:22:41 +0800</pubDate><description><![CDATA[【代码】MATLAB Learning Roadmap （1）]]></description><category></category></item><item><title><![CDATA[AI For Everyone - Andrew Ng - Week 2]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143465304</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143465304</guid><author>2302_77608969</author><pubDate>Fri, 08 Nov 2024 11:01:47 +0800</pubDate><description><![CDATA[to summarize, the key steps of a machine learning project are to collect data, to train the model, the A to B mapping, and then to deploy the model. Throughout these steps, there is often a lot of iteration, meaning fine-tuning or adapting the model to wor]]></description><category></category></item><item><title><![CDATA[Take a glance at Computer Vision]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143466801</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143466801</guid><author>2302_77608969</author><pubDate>Sun, 03 Nov 2024 17:37:54 +0800</pubDate><description><![CDATA[在统计学中，零假设通常表示没有效果或没有差异的假设，而备择假设则是研究者想要证明的假设，即存在某种效果或差异。通过比较这两个假设，可以判断观察到的数据是否更倾向于支持备择假设，从而对研究问题得出结论。通过应用贝叶斯定理，我们可以将观察到的证据与先验信念结合起来，计算出更新后的信念（后验概率），从而更准确地预测或推断事件的可能性。]]></description><category></category></item><item><title><![CDATA[AI For Everyone -Andrew Ng - week 1]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143442723</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143442723</guid><author>2302_77608969</author><pubDate>Sun, 03 Nov 2024 14:12:43 +0800</pubDate><description><![CDATA[In summary, supervised learning just learns input, output, or A to B mappings. On one hand, input, output A to B seems quite limiting.But when you find the right application scenario, this turns out to be incredibly valuable.The most important idea in AI h]]></description><category></category></item><item><title><![CDATA[Image Processing：Edge Detection - note(1)]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143393414</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143393414</guid><author>2302_77608969</author><pubDate>Thu, 31 Oct 2024 15:51:57 +0800</pubDate><description><![CDATA[The main purpose of edge detection is to simplify the representation of an image by reducing it to its structural framework, which consists of lines and points. This simplification is crucial for higher-level image analysis tasks such as object recognition]]></description><category></category></item><item><title><![CDATA[MongoDB Roadmap : MongoDB Basics]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/143358164</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/143358164</guid><author>2302_77608969</author><pubDate>Wed, 30 Oct 2024 11:53:37 +0800</pubDate><description><![CDATA[SQL (Structured Query Language) databases are also called relational databases.NoSQL databases: non-relational databases that don't follow a fixed schema. Storage formats: JSON documents, key-value pairs, graphs. Popular examples: MongoDB, Cassandra, Redis]]></description><category></category></item><item><title><![CDATA[Datawhale X 李宏毅苹果书 AI夏令营 Task3打卡]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/141831268</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/141831268</guid><author>2302_77608969</author><pubDate>Mon, 02 Sep 2024 22:53:36 +0800</pubDate><description><![CDATA[首先需要确定一个性能指标，比如准确率、损失函数值等，用于评估模型在验证集上的表现。]]></description><category></category></item><item><title><![CDATA[Datawhale X 李宏毅苹果书 AI夏令营 Task2打卡]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/141650430</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/141650430</guid><author>2302_77608969</author><pubDate>Sat, 31 Aug 2024 23:24:00 +0800</pubDate><description><![CDATA[是一种常用的人工神经网络中的。]]></description><category></category></item><item><title><![CDATA[Datawhale X 李宏毅苹果书 AI夏令营 Task1打卡]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/141469094</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/141469094</guid><author>2302_77608969</author><pubDate>Tue, 27 Aug 2024 23:31:31 +0800</pubDate><description><![CDATA[损失函数(loss function)：指的是在机器学习中用来衡量模型预测结果与实际结果之间差异的函数。其值越小，代表模型预测结果越接近实际结果。在训练模型时，需要通过不断调整模型参数来最小化损失函数的值。常见的损失函数包括均方误差、交叉熵等。对于简单的模型（函数）y=b+w*x，可以有损失函数L(b,w)来表示损失，其输入是模型的未知参数b或w，输出是模型在当前参数下的预测结果与实际结果之间差异值。对于差异值的计算，有不同的计算方法，其中：①。]]></description><category></category></item><item><title><![CDATA[Datawhale AI夏令营-大模型技术（微调）Task2打卡]]></title><link>https://blog.youkuaiyun.com/2302_77608969/article/details/141164193</link><guid>https://blog.youkuaiyun.com/2302_77608969/article/details/141164193</guid><author>2302_77608969</author><pubDate>Tue, 13 Aug 2024 16:00:28 +0800</pubDate><description><![CDATA[增量预训练微调 (使用场景：让基座模型学习到一些新知识，如某个垂类领域的常识训练数据：文章、书籍、代码等指令跟随微调 (使用场景：让模型学会对话模板，根据人类指令进行对话训练数据：高质量的对话、问答数据。]]></description><category></category></item></channel></rss>