Datasets:
codeparrot
/
github-jupyter-parsed

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GoogleCloudPlatform/training-data-analyst
quests/serverlessml/02_bqml/solution/first_model.ipynb
apache-2.0
[ "First BigQuery ML models for Taxifare Prediction\nIn this notebook, we will use BigQuery ML to build our first models for taxifare prediction.BigQuery ML provides a fast way to build ML models on large structured and semi-structured datasets.\nLearning Objectives\n\nChoose the correct BigQuery ML model type and sp...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
Jim00000/Numerical-Analysis
5_Numerical_Differentiation_And_Integration.ipynb
unlicense
[ "★ Numerical Differentiaion and Integration ★", "# Import modules\nimport math\nimport sympy as sym\nimport numpy as np\nimport scipy \nimport matplotlib.pyplot as plt\nimport plotly\nimport plotly.plotly as ply\nimport plotly.figure_factory as ply_ff\nfrom IPython.display import Math\nfrom IPython.display import...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mF2C/COMPSs
tests/sources/python/9_jupyter_notebook/src/simple_mpi.ipynb
apache-2.0
[ "Test suite for Jupyter-notebook\nSample example of use of PyCOMPSs from Jupyter with mpi worker\nFirst step\nImport ipycompss library", "import pycompss.interactive as ipycompss", "Second step\nInitialize COMPSs runtime\nParameters indicates if the execution will generate task graph, tracefile, monitor interva...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
walkon302/CDIPS_Recommender
notebook_versions/Exploring_Data_v2.ipynb
apache-2.0
[ "Data Exploration", "import sys \nimport os\nsys.path.append(os.getcwd()+'/../')\n\n# other\nimport numpy as np\nimport glob\nimport pandas as pd\nimport ntpath\n\n#keras\nfrom keras.preprocessing import image\n\n# plotting\nimport seaborn as sns\nsns.set_style('white')\nimport matplotlib.pyplot as plt\n%matplotl...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
ledeprogram/algorithms
class6/donow/Devulapalli_Harsha_Class6_DoNow.ipynb
gpl-3.0
[ "1. Import the necessary packages to read in the data, plot, and create a linear regression model", "import pandas as pd\n%matplotlib inline\nimport matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)\nimport statsmodels.formula.api as smf", "2. Read in the hanford.csv file", ...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
hankcs/HanLP
plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb
apache-2.0
[ "<h2 align=\"center\">点击下列图标在线运行HanLP</h2>\n<div align=\"center\">\n <a href=\"https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n ...
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flsantos/startup_acquisition_forecast
.ipynb_checkpoints/3_dataset_exploration-checkpoint.ipynb
mit
[ "Dataset Exploration\nHere we'll be exploring how each of the features we have so far relates to the target variable \"status\"\nImporting the dataset", "import pandas as pd\nstartups = pd.read_csv('data/startups_2.csv', index_col=0)\nstartups[:3]", "Let's start exploring the numerical features\nLet's see a hea...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
francof2a/APC
sources/TempScript.ipynb
gpl-3.0
[ "TempScrip\nScript to developing and test partial functionality.", "import dataset as ds\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n\n# Download database\nds.download('UCI HAR')", "Reading Dataset\nIdea here is to read the files in the dataset to extract data for training, ...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
antoniomezzacapo/qiskit-tutorial
community/terra/qis_adv/vaidman_detection_test.ipynb
apache-2.0
[ "<img src=\"../../../images/qiskit-heading.gif\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">\nThe Vaidman Detection Test: Interaction Free Measurement\nThe latest version of this notebook is available on https://g...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
vzg100/Post-Translational-Modification-Prediction
old/Phosphorylation Sequence Tests -MLP -dbptm+ELM -scalesTrain-VectorAvr..ipynb
mit
[ "Template for test", "from pred import Predictor\nfrom pred import sequence_vector\nfrom pred import chemical_vector", "Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\nIncluded is N Phosphorylation however no benchmarks are available, yet. \nTraining da...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
AndysDeepAbstractions/deep-learning
gan_mnist/Intro_to_GANs_Exercises.ipynb
mit
[ "Generative Adversarial Network\nIn this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
zzsza/TIL
Tensorflow-Extended/TFDV(data validation) example.ipynb
mit
[ "Github\nPython2에서 진행\nPython3에서도 되긴 하는데, 몇 기능이 안될듯(Apache Beam이 아직 파이썬2만 지원)", "from __future__ import print_function\nimport sys, os\nimport tempfile, urllib, zipfile\n# Confirm that we're using Python 2\nassert sys.version_info.major is 2, 'Oops, not running Python 2'\n\n# Set up some globals for our file path...
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yw-fang/readingnotes
machine-learning/McKinney-pythonbook2013/chapter07-note.ipynb
apache-2.0
[ "阅读笔记\n 作者：方跃文 \n Email: fyuewen@gmail.com \n** 时间：始于2018年11月17日， 结束写作于 2018年\n第七章 数据规整化：清理、转换、合并、重塑\nPANDAS 的产生是以运用为导向的，因此它包含了许多实际工作中需要的数据清理方式。\n合并数据集\npandas对象可以通过一些内置的方法进行合并：\n\n\npandas.merge, 可以根据一个或者多个key将数据进行连接\n\n\npandas.concat ， 可以沿着一条轴将多个数据堆叠在一起。\n\n\n实例方法中的 combine.fist 可以将重复的数据编排在一起，并且用一个对象中的值填缺另一个对象中的...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
cfjhallgren/shogun
doc/ipython-notebooks/structure/FGM.ipynb
gpl-3.0
[ "General Structured Output Models with Shogun Machine Learning Toolbox\nShell Hu (GitHub ID: hushell)\nThanks Patrick Pletscher and Fernando J. Iglesias García for taking time to help me finish the project! Shoguners = awesome! Me = grateful!\nIntroduction\nThis notebook illustrates the training of a <a href=\"http...
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acmiyaguchi/data-pipeline
reports/engagement_ratio/MauDau.ipynb
mpl-2.0
[ "Overall Firefox Engagement Ratio\nCompute the Engagement Ratio for the overall Firefox population as described in Bug 1240849. The resulting data is shown on the Firefox Dashboard, and the more granular MAU and DAU values can be viewed via the Diagnostic Data Viewer.\nThe actual Daily Active Users (DAU) and Monthl...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
yongtang/tensorflow
tensorflow/compiler/xla/g3doc/tutorials/autoclustering_xla.ipynb
apache-2.0
[ "Copyright 2019 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...
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