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Alexa Fluor 647-Labeled Her2 His Tag 蛋白：精准检测与前沿研究的重要工具

人类表皮生长因子受体2（Her2）是ErbB受体酪氨酸激酶家族的关键成员，在调控细胞增殖、分化与存活信号通路中发挥核心作用。Her2基因扩增或蛋白过表达可见于约...

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人工智能之核心基础机器学习第十五章数据预处理

])labels = ['labeled']*len(X_labeled) + ['unlabeled']*len(X_unlabeled)# ......# 正确做法：仅用标签数据拟合 scaler = StandardScaler().fit(X_labeled) X_labeled_scaled = scaler.transform(X_labeled...(18, 70, n_labeled), 'income': np.random.exponential(50000, n_labeled), 'city': np.random.choice...(['北京', '上海', '广州', '深圳', '其他'], n_labeled), 'clicks': np.random.poisson(10, n_labeled), 'purchase...特征选择（仅用有标签数据）# ========================X_labeled = df_all.loc[labeled_mask, features]y_labeled = df_all.loc

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Kaggle: Detect toxicity - Basic EDA -1

associated with toxicity outnumbered neutral comments regarding the same identity ---- Dataset dataset labeled...[train_labeled_df['target'] >= .5][identities] non_toxic_df = train_labeled_df[train_labeled_df['target...== 0, other = 1).sum() non_toxic_count = non_toxic_df.where(train_labeled_df == 0, other = 1).sum()...[:, 1:].multiply(train_labeled_df.iloc[:, 0], axis="index").sum() # changing the value of identity to...1 or 0 only and get comment count per identity group identity_label_count = train_labeled_df[identities

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半监督学习算法及其实现

= 20 X_labeled = torch.tensor(X[:n_labeled], dtype=torch.float32) y_labeled = torch.tensor(y[:n_labeled..., y_labeled, X_unlabeled, adj_matrix, lambda_reg): labeled_loss = criterion(model(X_labeled), y_labeled...# 扩展标记数据集 X_labeled = torch.cat((X_labeled, X_pseudo_labeled)) y_labeled = torch.cat((y_labeled..., y_pseudo_labeled)) # 重新训练模型 dataset = TensorDataset(X_labeled, y_labeled) dataloader =..., y_labeled, X_unlabeled, lambda_reg): labeled_loss = criterion(model(X_labeled), y_labeled)

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【机器学习】机器学习重要方法—— 半监督学习：理论、算法与实践

from sklearn.semi_supervised import LabelPropagation # 构建有标签数据和无标签数据 X_labeled, X_unlabeled, y_labeled..., X_unlabeled)) y_combined = np.concatenate((y_labeled, y_unlabeled)) # 训练标签传播模型 label_propagation =...y_pred_unlabeled = np.argmax(model.predict(x_unlabeled), axis=1) x_labeled = np.vstack((x_labeled..., x_unlabeled)) y_labeled = np.concatenate((y_labeled, y_pred_unlabeled)) model.fit(x_labeled..., y_labeled, epochs=5, validation_data=(x_test, y_test), verbose=2) model2.fit(x_labeled, y_labeled,

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人工智能之核心基础机器学习第十八章经典实战项目

= int(0.05 * n_total)labeled_idx = np.random.choice(n_total, size=n_labeled, replace=False)y_semi =...np.full(n_total, -1)y_semi[labeled_idx] = y_all[labeled_idx] 伪标签法全流程# 1....初始模型训练（仅用有标签数据）X_labeled = X_tfidf[labeled_idx]y_labeled = y_all[labeled_idx]lr = LogisticRegression(...max_iter=1000)lr.fit(X_labeled, y_labeled)# 3....(max_iter=1000)clf.fit(X_labeled_features, y_labeled)# 步骤4: 评估test_acc = clf.score(X_test_features, y_test

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LeetCode 0133 - Clone Graph

OJ’s undirected graph serialization: Nodes are labeled uniquely....First node is labeled as 0. Connect node 0 to both nodes 1 and 2. Second node is labeled as 1....Third node is labeled as 2. Connect node 2 to node 2 (itself), thus forming a self-cycle.

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学界 | 如何用未标注样本解决单标注样本下的视频行人重识别问题？

用标注数据和 pseudo-labeled 数据一起来更新 CNN 模型。...我们通过逐步增大每次加入训练的 pseudo-labeled 数据量，从而逐渐去利用更难识别的，包含更多信息多样性的视频片段。...这里有两个值得注意的点：（1）如何决定每次选取多少 pseudo-labeled 数据做训练我们用一种动态测量，逐渐增加选取的样本。...在开始的循环中，只有一小部分 pseudo-labeled 数据被选中，之后会有越来越多样本被加进来。...下面我们展示一下算法选出来的 pseudo-labeled 样本。 ?

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半监督学习的工程落地方式：安全攻防中的标签稀缺解决方案

数据准备 # 假设我们有少量标注数据和大量未标注数据 X_labeled = np.load('labeled_malware_features.npy') y_labeled = np.load('labeled_malware_labels.npy...(X_labeled, y_labeled, epochs=epochs, batch_size=32, validation_split=0.1, verbose=1) # 迭代伪标签生成...数据准备 X_labeled = np.load('labeled_network_traffic.npy') y_labeled = np.load('labeled_network_labels.npy...)) X_labeled_shuffled = X_labeled[indices] y_labeled_shuffled = y_labeled[indices]...= X_labeled_shuffled[start_idx:end_idx] y_batch_labeled = y_labeled_shuffled[start_idx:end_idx

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精灵标注助手目标检测数据集格式转VOC或者yolo

361 385 labeled...>1692452783787labeled> labeled>truelabeled> 640 426labeled...":1692452770011,"labeled":true,"size":{"width":640,"height":478,"depth":3}} 很显然xml文件格式不是VOC格式，真正VOC格式类似下面

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【leetcode】Palindrome Partitioning II

(num_line); for(int i=0;i<num_line;i++) { dist[i]=BIG; labeled[i]=false; } dist[0]=0;...labeled[0]=true; int current_line=0; for(int i=1;i<num_line;i++) { for(int d=current_line+1...labeled[d]) { if (dist[current_line]+isPalindrome[current_line*num_line+d]<dist[d]) {...labeled[i]) { if(mindist>dist[i]) { minindex=i; mindist=dist[i]; }...} } current_line=minindex; labeled[minindex]=true; } return dist[num_line-1]-1; } }; 参考推荐

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人工智能之核心基础机器学习第十七章 Scikit-learn工具全解析

= 30 # 仅30个标签# 随机选择有标签样本labeled_idx = np.random.choice(n_samples, size=n_labeled, replace=False)y_semi...= np.full(n_samples, -1)y_semi[labeled_idx] = y[labeled_idx]# 方法1: Label Propagationlp = LabelPropagation...= 1000labeled_idx = np.random.choice(len(X), size=n_labeled, replace=False)X_labeled = X_features[labeled_idx...]y_labeled = y[labeled_idx] # 假设有标签# 训练线性分类器from sklearn.linear_model import LogisticRegressionclf =...LogisticRegression(max_iter=1000)clf.fit(X_labeled, y_labeled)# 评估test_acc = clf.score(X_features[60000

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人工智能之核心基础机器学习第十二章半监督学习

= int(0.01 * n_total) # 1%有标签# 随机选择有标签样本labeled_indices = np.random.choice(n_total, size=n_labeled,...replace=False)y_semi = np.full(n_total, -1) # -1 表示无标签y_semi[labeled_indices] = y_true[labeled_indices...]# 方法1：伪标签法（用逻辑回归）lr = LogisticRegression(max_iter=1000)lr.fit(X[labeled_indices], y_true[labeled_indices...= np.random.choice(1000, size=10, replace=False)y_semi = np.full(1000, -1)y_semi[labeled_idx] = y_true...[labeled_idx]# 1.

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VXLAN是Overlay的唯一选择吗？

在这种情况下使用Labeled BGP(带有标签分发能力的BGPV4)而不是VxLAN也是个很好的选择。...Labeled BGP标签映射信息被携带在多协议扩展属性的NLRI中。AFI标识关联的路由条目，SAFI值为4表示NLRI包含标签。...服务提供商通过支持MPLS和Labeled BGP的路由器，可以使用MPLS在数据中心之间建立隧道，BGP标签则作为在不同BGP之间交换MPLS标签的信令机制。...三、白盒交换机的支持随着SDN技术的发展以及相应带动的白盒化进程，基于商用交换芯片的普通白牌交换机也可以支持丰富的路由功能包括Labeled BGP。...VxLAN对于企业实现大二层是个扩展性很强的新技术，但是对于现有设备已经支持MPLS的服务商来讲，Labeled BGP等老技术仍然可以开新花。

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gpt-4o 生成前端图示 Prompt 指南

A rectangle labeled "Lexical Analyser" in the center....icon labeled "Internet"....Divide this layer into two smaller side-by-side rectangles:Left: Labeled "Service".Right: Labeled "Cloud...A box labeled "Square".Under "Color": Create two branches leading to: A box labeled "Red"....(labeled "I can't walk.").A bird (labeled "I can't swim.").Panel 2:Transform these animals into a single

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人工智能之核心基础机器学习第十四章半监督与自监督学习总结归纳

= int(0.02 * n_total) labeled_idx = np.random.choice(n_total, size=n_labeled, replace=False) y_semi...= np.full(n_total, -1) y_semi[labeled_idx] = y_true[labeled_idx] # ======================== # 步骤2:...], y_true[labeled_idx]) proba = lr.predict_proba(X_ae) pseudo_labels = lr.predict(X_ae) high_conf =...np.max(proba, axis=1) > 0.9 # 构建增强训练集 train_idx = np.concatenate([labeled_idx, np.where(high_conf)[0...], y_true[labeled_idx]) acc_baseline = accuracy_score(y_test, lr_baseline.predict(X[test_idx])) print

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Leetcode 133 Clone Graph

OJ's undirected graph serialization: Nodes are labeled uniquely....First node is labeled as 0. Connect node 0 to both nodes 1 and 2. Second node is labeled as 1....Third node is labeled as 2. Connect node 2 to node 2 (itself), thus forming a self-cycle.

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实战 | Kaggle竞赛：预测二手车每年平均价值损失

= les[l].transform(dedups[l]) dedups.loc[:, l + '_feat'] = pd.Series(tr, index=dedups.index) labeled...['name_feat'].unique()) / len(labeled['name_feat']) #输出：0.6224184813880769 #name列的标签占总数的62%。...labeled.drop(['name_feat'], axis='columns', inplace=True) 05 关联性分析让我们看看功能如何相互关联，更重要的是，与价格。...代码： #所有属性间的关联 plot_correlation_map(labeled) labeled.corr() labeled.corr().loc[:,'price'].abs().sort_values...06 准备模型代码： Y = labeled['price'] X = labeled.drop(['price'], axis='columns', inplace=False) matplotlib.rcParams

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Leetcode: Clone Graph

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使用Keras构建具有自定义结构和层次图卷积神经网络(GCNN)

, 1-labeled_portion]) labeled_data = data[mask, :] unlabeled_data = data[~mask, :] labeled_data_labels..., labeled_data_labels, epochs=epochs, batch_size=batch_size, verbose=0) acc_model1.append(sum(argmax..., 1-labeled_portion]) labeled_data = data[mask, :] unlabeled_data = data[~mask, :] labeled_data_labels..., labeled_data_labels, epochs=epochs, batch_size=batch_size, shuffle=False, verbose=0) acc_model2.append..., 1-labeled_portion]) unlabeled_data_labels = labels[~mask] # Randomize the labels of unlabeled

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Alexa Fluor 647-Labeled Her2 His Tag 蛋白：精准检测与前沿研究的重要工具

人工智能之核心基础机器学习第十五章数据预处理

Kaggle: Detect toxicity - Basic EDA -1

半监督学习算法及其实现

【机器学习】机器学习重要方法—— 半监督学习：理论、算法与实践

人工智能之核心基础机器学习第十八章经典实战项目

LeetCode 0133 - Clone Graph

学界 | 如何用未标注样本解决单标注样本下的视频行人重识别问题？

半监督学习的工程落地方式：安全攻防中的标签稀缺解决方案

精灵标注助手目标检测数据集格式转VOC或者yolo

【leetcode】Palindrome Partitioning II

人工智能之核心基础机器学习第十七章 Scikit-learn工具全解析

人工智能之核心基础机器学习第十二章半监督学习

VXLAN是Overlay的唯一选择吗？

gpt-4o 生成前端图示 Prompt 指南

人工智能之核心基础机器学习第十四章半监督与自监督学习总结归纳

Leetcode 133 Clone Graph

实战 | Kaggle竞赛：预测二手车每年平均价值损失

Leetcode: Clone Graph

使用Keras构建具有自定义结构和层次图卷积神经网络(GCNN)

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