Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn the margin is defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating samples are so-called support vector, i.e., the vectors “supporting” the margin boundaries. We can redefine ω by w = � s∈S αsy(s)x(s) where S denotes the set of the indices of the support vectors 4 Kernel

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review parallely along ω (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx Scaling ! and " such that min& ' & !() & + " = 1 Feng Li (SDU) SVM December 28, 2021 14 / 82 Support Vector Machine (Primal Form) Maximizing 1/∥ω∥ is equivalent to minimizing ∥ω∥2 = ωTω min ω,b ωTω

However, the lighting gains would be substantial if we make structural changes to add a couple of windows and a balcony. Similarly, to gain orders of magnitude in terms of footprint or quality, we should more than two features? In those cases, we could use classical machine learning algorithms like the Support Vector Machine4 (SVM) to learn classifiers that would do this for us. We could rely on deep learning Lookup: Look up the embeddings for the inputs in the embedding table. 4 Support Vector Machine - https://en.wikipedia.org/wiki/Support-vector_machine 3. Train the model: Train the model for the task at hand5

TensorFlow ������� TensorFlow ������� • Ubuntu 16.04 or later • Windows 7 or later • macOS 10.12.6 (Sierra) or later (no GPU support) • Raspbian 9.0 or later �� pip �� TensorFlow tensorflow —Current —Current release with GPU support (Ubuntu and Windows) tf-nightly —Nightly build for CPU-only (unstable) tf-nightly-gpu —Nightly build with GPU support (unstable, Ubuntu and Windows) “Hello TensorFlow” Try

powerful and dynamic framework and comes up with the following advantages:  Larger community support.  Easy to test.  Keras neural networks are written in Python which makes things simpler. requirements of Keras. Prerequisites You must satisfy the following requirements:  Any kind of OS (Windows, Linux or Mac)  Python version 3.5 or higher. Python Keras is python based neural network with bin,lib and include folders in your installation location. Windows 2. Keras ― Installation Keras 4 Windows user can use the below command, py -m venv keras Step 2: Activate

大规模语言模型。Qwen1.5 已经正式成为 LM Studio 的一部分。祝你使用愉快！ 1.5 Ollama Ollama 帮助您通过少量命令即可在本地运行 LLM。它适用于 MacOS、Linux 和 Windows 操作系统。现在， Qwen1.5 正式上线 Ollama，您只需一条命令即可运行它： ollama run qwen 接着，我们介绍在 Ollama 使用 Qwen 模型的更多用法 1.5 text-generation-webui 你可以根据你的操作系统直接运行相应的脚本，例如在 Linux 系统上运行 start_linux.sh ，在 Windows 系统上运行 start_windows.bat ，在 MacOS 系统上运行 start_macos.sh ，或者在 Windows 子系统 Linux（WSL）上运行 start_wsl.bat 。另外，你也可以选择手动在 conda 环境中安装所需的依赖项。这 context window size or text chunk size depending on your computing resources. Qwen 1.5 model families support a maximum of 32K context window size. import torch from llama_index.core import Settings from llama_index

TensorFlow 2 支持的操作系统 • Python 3.5–3.7 • Ubuntu 16.04 or later • Windows 7 or later • macOS 10.12.6 (Sierra) or later (no GPU support) • Raspbian 9.0 or later 使用 pip3 安装 TensorFlow 2 在 Jupyter Lab

employs a high quality model with a reasonable translation accuracy would garner better consumer support. In this chapter, our focus will be on the techniques that enable us to achieve our quality goals Intelligence. Vol. 34. No. 05. 2020. TREC (Open Domain Questions) 6.6 2.8 45.0 WVA (Telco Customer Support) 2.1 4.5 23.0 Table 3-5: Accuracy improvements with synthetic data on various classification tasks i = i + 1 if i + 1 >= num_examples: break # STFT to extract the fourier transform on sliding windows of the input. # Apply STFT on the audio data, but keep only the magnitude. x = tf.abs(tf.signal

997 年，现在 应用最为广泛的循环神经网络变种之一 LSTM 被 Jürgen Schmidhuber 提出；同年双向循环 神经网络也被提出。 遗憾的是，神经网络的研究随着以支持向量机(Support Vector Machine，简称 SVM)为 代表的传统机器学习算法兴起而逐渐进入低谷，称为人工智能的第二次寒冬。支持向量机 拥有严格的理论基础，训练需要的样本数量较少，同时也具有良好的泛化能力，相比之 在领略完深度学习框架所带来的便利后，现在来着手在本地计算机环境上安装 PyTorch 最新版。PyTorch 框架支持多种常见的操作系统，如 Windows 10、Ubuntu、Mac OS 等，支持运行在 NVIDIA 显卡上的 GPU 版本和仅使用 CPU 完成计算的 CPU 版本。这 里以最为常见的 Windows 10 系统，NVIDIA GPU 和 Python 语言环境为例，介绍如何安装 PyTorch 框架及其它开发软件。 Anaconda 安装界面-1 图 1.23Anaconda 安装界面-2 安装完成后，怎么验证 Anaconda 是否安装成功呢？通过键盘上的 Windows 键+R 键， 即可调出运行程序对话框，输入“cmd”并回车即打开 Windows 自带的命令行程序 cmd.exe。或者点击开始菜单，输入“cmd”也可搜索到 cmd.exe 程序，打开即可。输入 conda list 命令即可查看

也在不断的演化改进。 在操作系统与 SDK 支持方面，Pytorch 从最初的单纯支持 Python 语言到如今支持 Python/C++/Java 主流编程语言， 目前已经支持 Linux、Windows、MacOS 等主流的操作系统、 同时全面支持 Android 与 iOS 移动端部署。 在版本发布管理方面，Pytorch 分为三种不同的版本分别是稳 定版本 (Stable Release)、Beta 回报。 1.2 环境搭建 Pytorch 的开发环境搭建十分的简洁，它的依赖只有 Python 语 言 SDK， 只 要 有 了 Python 语 言 包 支 持， 无 论 是 在 windows 平台、ubuntu 平台还是 Mac 平台都靠一条命令 行就可以完成安装。首先是安装 Python 语言包支持，当前 Pytorch 支持的 Python 语言版本与系统对应列表如下： 表 表 -1（参考 Pytorch 官网与 Github） 系统 Python3�6 Python3�7 Python3.8 Linux CPU/GPU 支持 支持 支持 Windows CPU/GPU 支持 支持 支持 Linux (aarch64) CPU 支持 支持 支持 Mac (CPU) 支持 支持 支持 当前最新稳定版本是 Pytorch 1.9.0、长期支持版本是 Pytorch