isSeymour

Node2Vec 图嵌入: 《悲惨世界》人物关系 参考资料： Elior Cohen 代码：https://github.com/eliorc/node2vec Elior Cohen 博客：https://maelfabien.github.io/machinelearning/graph_5/ 1. 环境准备 安装工具包 1!pip install node2vec networkx numpy matplotlib 导入工具包 123456789101112# 图数据挖掘import networkx as nx# 数据分析import numpy as np# 随机数import random# 数据可视化import matplotlib.pyplot as plt 2. 数据 初始化 12345# 空手道俱乐部# G = nx.karate_club_graph()# 悲惨世界 人物G = nx.les_miserables_graph() 1G.nodes NodeView(('Napoleon', 'Myriel', 'MlleBaptistine', 'MmeMa ...

DeepWalk 图嵌入：维基百科词条 1. 环境参考 参考资料 https://github.com/prateekjoshi565/DeepWalk 安装工具包 1!pip install networkx gensim pandas numpy tqdm scikit-learn matplotlib 导入工具包 123456789101112131415# 图数据挖掘import networkx as nx# 数据分析import pandas as pdimport numpy as np# 随机数与进度条import randomfrom tqdm import tqdm# 数据可视化import matplotlib.pyplot as plt%matplotlib inline 2. 数据 获取数据 爬虫网站：https://densitydesign.github.io/strumentalia-seealsology/ 设置 distance 输入链接： https://en.wikipedia.org/wiki/Computer_vision htt ...

Homework 5 - Sequence-to-sequence If you have any questions, feel free to email us at: ntu-ml-2021spring-ta@googlegroups.com (4/21 Updates) Link to reference training curves. (4/14 Updates) Link to tutorial video part 1 part 2. Now defaults to load "avg_last_5_checkpoint.pt" to generate prediction. Expected run time on Colab with Tesla T4 Baseline Details Total Time Simple 2m 15s $\times$30 epochs 1hr 8m Medium 4m $\times$30 epochs 2hr Strong 8m $\times$30 epochs (backwa ...

HW4: Speaker Prediction goal: learn to use transformer 赛事：https://www.kaggle.com/competitions/ml2021spring-hw4/overview Baselines: Easy: Run sample code and know how to use transformer. Medium: Know how to adjust parameters of transformer. Hard: Construct conformer which is a variety of transformer. Other links Kaggle: link Slide: link Data: link Video (Chinese): link Video (English): link Solution for downloading dataset fail.: link 1. Data Dataset 12345678910111213141516171819202 ...

HW3 : Convolutional Nueral Network In this homework, you are required to build a convolutional neural network for image classification, possibly with some advanced training tips. There are three levels here: Easy: Build a simple convolutional neural network as the baseline. (2 pts) Medium: Design a better architecture or adopt different data augmentations to improve the performance. (2 pts) Hard: Utilize provided unlabeled data to obtain better results. (2 pts) https://www.kaggle.com/competitio ...

HW2: Phoneme Classification Platform : Kaggle Sample Code : Google Colab 0. Prepare TIMIT This homework is a multiclass classification task, we are going to train a deep neural network classifier to predict the phonemes for each frame from the speech corpus TIMIT. Google Colab 下载：（或官网下载也行） 123!gdown --id '1HPkcmQmFGu-3OknddKIa5dNDsR05lIQR' --output data.zip!unzip data.zip!ls zsh:1: command not found: gdown unzip: cannot find or open data.zip, data.zip.zip or data.zip.ZIP. HW02.pdf ...

HW1: COVID-19 Cases Prediction (Regression) Platform : Kaggle Sample Code : Google Colab Objectives: Solve a regression problem with deep neural network. (DNN) Understand basic DNN training tips. Get familiar with PyTorch. 0. Prepare 12import warningswarnings.filterwarnings('ignore') 预览数据 12tr_path = 'input/covid.train.csv'tt_path = 'input/covid.test.csv' 123456# 预览数据import pandas as pddata = pd.read_csv(tr_path)print("Shape:",data.shape)data.iloc[:,4 ...

来源：Kaggle 官方课程 Intro to Deep Learning 2024-09-01@isSeymour Intro to Deep Learning Exercise: Binary Classification 1.导入数据 123456789101112import pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScaler, OneHotEncoderfrom sklearn.impute import SimpleImputerfrom sklearn.pipeline import make_pipelinefrom sklearn.compose import make_column_transformerhotel = pd.read_csv('../input/hotel.csv')print(" ...

来源：Kaggle 官方课程 Data Visualization 2024-09-01@isSeymour Data Visualization 总览： 展现内容： Trends 趋势 代码 功能 lineplot 折线图 用于展示数据随时间或其他连续变量的变化趋势 Relationship 关系 代码 功能 barplot 柱状图 用于展示不同类别或分组数据的数量或频率 heatmap 热图 用于显示数据的密度或强度，通过颜色的深浅来表示数值的大小 scatterplot 散点图 用于展示两个变量之间关系 swarmplot 蜂群图 用于显示数据分布的可视化图表，通过将数据点以散点的方式展示在类别上，并避免数据点重叠。它适用于展示单变量或多变量的离散数据分布，特别是当数据量较小或希望看到每个数据点的具体位置时 regplot 回归图 用于展示回归分析结果的数据可视化工具。它通常包括一个散点图和一条回归线，帮助直观地展示两个变量之间的关系及其回归模型的拟合效果 lmplot 线性回归图 用于展示线性回归分析结果的 ...

来源：Kaggle 官方课程 | Intermediate Machine Learning 2024-08-31@isSeymour Intermediate Machine Learning 1. Missing Value 缺失值 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.metrics import mean_absolute_errorfrom sklearn.impute import SimpleImputer# 数据准备X_full = pd.read_csv('../input/train. ...