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

neural network layers, deep learning optimizers, data loading utilities, and multi-gpu, and multi-node support. Functions are executed immediately instead of enqueued in a static graph, improving ease of use begin Before you can run an NGC deep learning framework container, your Docker ® environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in Running A Container Container and specify the registry, repository, and tags. About this task On a system with GPU support for NGC containers, when you run a container, the following occurs: ‣ The Docker engine loads the image

Shell 脚本中提供了一些 指南，并且此处将以 finetune.sh 这个脚本为例进行解释说明。 要为分布式训练（或单 GPU 训练）设置环境变量，请指定以下变量：GPUS_PER_NODE 、NNODES、NODE_RANK 、MASTER_ADDR 和 MASTER_PORT 。不必过于担心这些变量，因为我们为您提供了默认设置。在命令行中， 您可以通过传入参数 -m 和 -d 来分别指定模型路径和数据路径。您还可以通过传入参数 "assistant_tag": "assistant" } } 训练 执行下列命令： DISTRIBUTED_ARGS=" --nproc_per_node $NPROC_PER_NODE \ --nnodes $NNODES \ --node_rank $NODE_RANK \ --master_addr $MASTER_ADDR \ --master_port $MASTER_PORT " torchrun 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.core.node_parser import SentenceSplitter from llama_index

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. reload_layer = Dense.from_config(config) input_shape Get the input shape, if only the layer has single node. >>> from keras.models import Sequential >>> from keras.layers import Activation, Dense >>> get_weights() >>> layer_1.input_shape (None, 8) input Get the input data, if only the layer has single node. >>> from keras.models import Sequential >>> from keras.layers import Activation, Dense >>>

assigned longer codes. This is achieved with a simple Huffman Tree (figure 2-1 bottom). Each leaf node in the tree is a symbol, and the path to that symbol is the bit-string assigned to it. This allows state that in this book, we have chosen to work with Tensorflow 2.0 (TF) because it has exhaustive support for building and deploying efficient models on devices ranging from TPUs to edge devices at the time would lead to a 32 / 8 = 4x reduction in space. This fits in well since there is near-universal support for unsigned and signed 8-bit integer data types. 4. The quantized weights are persisted with the

预测服务 实时特征 实时数据 3 在线机器学习 实时样本 实时模型训练 实时更新参数 Task 训练预处理 Node 实时样本拼接 Node 在线模型训练 Node 离线样本拼接 Node 在线模型评估 Node 模型上线 Node 实时特征处理 Node 离线特征处理 Task Kafka输入 input process process output WeiFlow

值选择输出分支，直到叶子节点，将叶子 节点的存放的类别作为决策结果。 根节点 (root node) 叶节点 (leaf node) 5 1.决策树原理 根节点 (root node) 非叶子节点 (non-leaf node) (代表测试条件，对数据属性的测试) 分支 (branches) (代表测试结果) 叶节点 (leaf node) (代表分类后所获得的分类标记) ⚫ 决策树算法是一种归纳分类算法

(Contd.) We can recursively call the algorithm on G and/or H, or any other node in the tree. E.g., choose to split the node whose average dissimilarity is highest, or whose maximum dissimilarity is highest

has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `get_output_at(node_index)` instead. 好吧，通过下面的方法可以解决： assert lstm.get_output_at(0) == encoded_a assert lstm.get_output_at(1) 层节点和共享层的概念), 您可以使用以下函数: • layer.get_input_at(node_index) • layer.get_output_at(node_index) • layer.get_input_shape_at(node_index) • layer.get_output_shape_at(node_index) 关于 KERAS 网络层 59 5.2 核心网络层 5.2