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Automation - Tutorials - IBM Developer
of 2 pages 1 2 of 2 pages Previous page Next...developer.ibm.com/technologies/automation/tutorials/ -
PyTorch - IBM Developer
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Compose 文件 方法 1: 单独下载文件 方法 2: 使用 Git 克隆仓库 步骤 2: 确认 Docker Compose 文件...1: 使用快照功能（推荐） 方法 2: 整体备份目录 Docker 版的备份 步骤 2: 停止当前版本 步骤 3: 安装新版本...fess.codelibs.org/zh-cn/archives.html -
1.7. Gaussian Processes — scikit-learn 1....
log-transformed [[ -inf 2.30258509] [ -inf 2.30258509] [ -inf 2.30258509]]...\left(1 + \frac{d(x_i, x_j)^2}{2\alpha l^2}\right)^{-\alpha}\] The...scikit-learn.org/stable/modules/gaussian_process.html -
Optimizing GPU accelerated VMs with KubeVirt
GPU(column) bandwidth (GB/s) 0 1 2 3 4 5 6 7 0 12 . 61 12 . 52 15...NUMA node: 1 Thread of device 2 running on CPU: 152 , NUMA node:...developer.ibm.com/articles/gpu-vms-kubevirt-numa-nodes/ -
mutual_info_classif — scikit-learn 1.8.0 ...
[ 2 ] ( 1 , 2 ) A. Kraskov, H. Stogbauer...neighbors distances as described in [2] and [3] . Both methods are based...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html -
Novelty detection with Local Outlier Factor (LO...
2 ) X_train = np . r_ [ X + 2 , X - 2 ] # Generate...randn ( 20 , 2 ) X_test = np . r_ [ X + 2 , X - 2 ] # Generate...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
Simple 1D Kernel Density Estimation — sci...
subplots ( 2 , 2 , sharex = True , sharey = True...1.05 ) axi . set_xlim ( - 2.9 , 2.9 ) ax [ 0 , 1 ] . set_title...scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html -
OrdinalEncoder — scikit-learn 1.8.0 docum...
transform ( X_test ) array([[2.], [0.], [1.], [2.], [3.], [4.]]) fit (...scikit-learn 1.2 Release Highlights for scikit-learn 1.2 Release Highlights...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html -
Post-tuning the decision threshold for cost-sen...
(2) in Elkan’s paper [ 2 ] . import numpy as...metric" , ) axs [ 2 ] . legend () axs [ 2 ] . set_xlabel ( "Decision...scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html