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danielfrg/danielfrg.github.io-source | content/blog/notebooks/2013/01/copper-easy-data-analysis-machine-learning-python.ipynb | 1 | 44636 | {
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"name": "Post_1"
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"As part of my Master in IT & Management this semester I am taking a class called \"Advanced Business Intelligence\" which ... | apache-2.0 |
beangoben/lerningMachin | Pandas/Untitled0.ipynb | 1 | 181 | {
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wcmckee/signinlca | pggNumAdd.ipynb | 1 | 7566 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 17, 27, 31, 41, 53, 66, 73, 88, 96]\n"
]
}
],
"source": [
"# IPython log file\n",
"... | mit |
harishkrao/DSE200x | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | 1 | 12784 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\">\n",
"\n",
"Classification of Weather Data <br><br>\n",
"using scikit-learn\n",
"<br><br>\n",
"</p>"
]
}... | mit |
nkmk/python-snippets | notebook/string_line_break.ipynb | 1 | 11834 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Line1\n",
"Line2\n",
"Line3\n"
]
}
],
"source": [
"s = 'Line1\\nLine2\\nLine3'\n",
"print(s)"
]
}... | mit |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/advance-theme/Singleton.ipynb | 1 | 3829 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python 单例模式"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"# 1 \\__new\\__ 方法\n",
"+ \\__new\\__(cls, \\*args, \\*\\*kwargs) 创建对象时调用，返回当前对象的一个实例;注意：这里的第一个参数是cls即class本身 ... | mit |
tsavo-sevenoaks/garth | ipython_101_notebook-Copy1.ipynb | 1 | 127418 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# iPython 101 - using numpy, scipy, matplotlib, sqlite3 and Bokeh.\n",
" it takes a long time to load - be patient!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"... | gpl-3.0 |
JackDi/phys202-2015-work | assignments/assignment09/IntegrationEx02.ipynb | 1 | 12713 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Integration Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"met... | mit |
serpilliere/miasm | doc/ir/lift.ipynb | 3 | 111112 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prerequisite: the reader is encouraged to read the documentation of `expression` and `locationdb` before this part.\n",
"\n",
"# Miasm Intermediate representation\n",
"The intermediate representation of Miasm allows to repr... | gpl-2.0 |
emreyamangil/Convex.jl | examples/max_entropy.ipynb | 6 | 2692 | {
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:0268061fda1c421f741f0884df8c4b09e9fe4f215aa0f622aaa67f6c166afe1b"
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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Entropy Maximizat... | bsd-2-clause |
ledeprogram/algorithms | class4/homework/wang_zhizhou_4_3.ipynb | 1 | 178411 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assigment 3\n",
"* Using the heights_weights_genders.csv, analyze the difference between the height weight correlation in women and men."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colla... | gpl-3.0 |
bdestombe/flopy-1 | examples/Notebooks/flopy3_ZoneBudget_example.ipynb | 1 | 80553 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FloPy\n",
"\n",
"### ZoneBudget Example\n",
"\n",
"This notebook demonstrates how to use the `ZoneBudget` class to extract budget information from the cell by cell budget file using an array of zones.\n",
"\n",
... | bsd-3-clause |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Lecture Examples/Morten Lecture Data Examples.ipynb | 1 | 55661 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#!pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
... | cc0-1.0 |
SiggyF/notebooks | seaice.ipynb | 1 | 80613 | {
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"worksheets": [
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language"... | gpl-3.0 |
tensorflow/docs-l10n | site/ko/probability/examples/Bayesian_Gaussian_Mixture_Model.ipynb | 1 | 35343 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "htW5SiGzeXYm"
},
"source": [
"##### Copyright 2018 The TensorFlow Probability Authors.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell... | apache-2.0 |
moksh100/juliasets | juliasets3.ipynb | 1 | 3173500 | null | mit |
Joshuaalbert/IonoTomo | src/ionotomo/notebooks/Atmosphere.ipynb | 1 | 13416 | {
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Center of array: <SkyCoord (ITRS: obstime=J2000.000): (x, y, z) in m\n",
" (1656795.53... | apache-2.0 |
GkAntonius/feynman | docs/auto_examples/Particle_Physics/plot_LFV.ipynb | 1 | 1937 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\nLFV\n===\n\nThe LFV ... | gpl-3.0 |
feststelltaste/software-analytics | demos/20181213_EuregJUG_Aachen/Race Condition Analysis Cypher Kernel Edition.ipynb | 2 | 2357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# Context\n",
"\n",
"Race Conditions are bad because..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},... | gpl-3.0 |
uwkejia/Clean-Energy-Outlook | examples/Extra/Jupyter Notebooks/SVR with cross-validation for Wind.ipynb | 1 | 195201 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kejiawu/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This m... | mit |
lemonyhermit/CodingYoga | python-for-developers/Chapter3/Chapter3_Control_flow.ipynb | 2 | 3991 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Python for Developers](http://ricardoduarte.github.io/python-for-developers/#content)\n",
"===================================\n",
"First edition\n",
"-----------------------------------\n",
"\n",
"Chapter 3: Contr... | gpl-2.0 |
johnpfay/environ859 | PySpatial/Interactive.ipynb | 1 | 156320 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 0,
"width": 4
... | gpl-3.0 |
mazenbesher/simplex | sympy_version/specific/baltt4_aufgabe2_ii.ipynb | 1 | 14715 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"given LP\n"
]
},
{
"data": {
"text/latex": [
"$$x_{3} = x_{0} + 2 x_{1} + x_{2}... | mit |
Enjoying-Learning/Enjoying | docs/3. 完整神经网络样例程序.ipynb | 1 | 4340 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from numpy.random import RandomState"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1. 定义神经网络的参数，... | mit |
JavierVLAB/DataAnalysisScience | AutoMPG/AutoMPG.ipynb | 1 | 468115 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Exploration of Auto MPG</h1>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import math\n",
"import numpy\n",
"import pand... | gpl-3.0 |
ericagol/celerite | paper/figures/rotation/rotation.ipynb | 3 | 16892 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config IPython.matplotlib.backend = \"retina\"\n",
"from matplotlib import rcParams\n",
"rcParams[\"savefig.dpi\"] = 300\n",
"rcParams[\"figure.dpi\"] =... | mit |
CalPolyPat/phys202-2015-work | assignments/project/Base Network Tangent Cost Function and L2 Regularization wAdaptive learning constantspeed of learning.ipynb | 3 | 1462105 | null | mit |
ColeLab/informationtransfermapping | MasterScripts/ManuscriptS3_ComputationalModelGroupAnalysis_TopDownAndBottomUp.ipynb | 1 | 722038 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Computational model for group analysis\n",
"\n",
"### Demo code for Ito et al., 2017. Generates exact figures from Supplementary Fig. 3, and several comparable figures to Fig. 4.\n",
"\n",
"#### Author: Takuya Ito (ta... | gpl-3.0 |
PYPIT/COS_REDUX | docs/nb/Coadd_script.ipynb | 1 | 8669 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coadd script"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# imports\n",
"import glob\n",
"import pdb\n",
"\n",
"im... | bsd-2-clause |
davidvilla/python-course | numpy.ipynb | 1 | 50976 | {
"metadata": {
"name": "",
"signature": "sha256:b2c6b63633de46f21d7e507ff9b026fb5bdf7b9f4c98c469bbc08e92526e25b9"
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# numpy\n",
"\n",
"Es un m\u00... | gpl-2.0 |
Merinorus/adaisawesome | Homework/01 - Pandas and Data Wrangling/temp/Data Wrangling with Pandas.ipynb | 1 | 671823 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1\"><a href=\"#Data-Wrangling-with-Pandas\"><span class=\"toc-item-num\">1 </span>Data Wrangling with Pandas</a></div><div class=\"lev2\"><a href=\"#Date... | gpl-3.0 |
atcemgil/notes | DecisionTree.ipynb | 1 | 971878 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import tree\n",
"\n",
"X = [[0, 0], [1, 1]]\n",
"Y = [0, 1]\n",
"decision_tree_classifier = tree.DecisionTreeClassifier()\n",
"decision_t... | mit |
joelmpiper/bill_taxonomy | notebooks/Congress_Text_Analysis.ipynb | 1 | 339528 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"postgres://Joel@localhost/bills_db\n"
]
}
],
"source": [
"## Python packages - you may have... | mit |
diegocavalca/Studies | phd-thesis/benchmarkings/am207-NILM-project-master/CO.ipynb | 2 | 216158 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Karen Yu, Nick Vasios, Thibaut Perol\n",
"\n",
"# AM207 Final Project\n",
"\n",
"## Energy Disaggregation from Non-Intrusive Load Monitoring"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [... | cc0-1.0 |
amandersillinois/landlab | notebooks/tutorials/flexure/flexure_1d.ipynb | 2 | 9234 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"../../landlab_header.png\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using the Landlab 1D flexure component\n",
"\... | mit |
kinverarity1/gpgLabs | Seismic/NMO/SeismicNMOapp.ipynb | 1 | 1238835 | null | mit |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/trimDataset/dataset_info.ipynb | 1 | 650346 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# General info on the fullCyc dataset (as it pertains to SIPSim validation)\n",
"\n",
"* Simulating 12C gradients\n",
"* Determining if simulated taxon abundance distributions resemble the true distributions\n",
"* Simu... | mit |
sdpython/pyquickhelper | _unittests/ut_helpgen/notebooks_utf8/simple_example.ipynb | 1 | 3460 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple example"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pyquickhelper"
]
},
{
"cell_type": "code",
"execut... | mit |
cbuntain/TwitterFergusonTeachIn | session_05.ipynb | 1 | 1429458 | null | mit |
davidparks21/qso_lya_detection_pipeline | docs/nb/Garnett16_analysis.ipynb | 1 | 148311 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some Analysis on Garnett 16"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib notebook"
]
},
{
"cell_type": "code"... | mit |
dianafprieto/SS_2017 | 02_NB_IntroductionNumpy.ipynb | 1 | 6070 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- <img src=\"files/images/python-screenshot.jpg\" width=\"600\"> -->\n",
"<img src=\"imgs/header.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basics of Numerical Python Arrays (nump... | mit |
jaduimstra/nilmtk | notebooks/experimental/testing_nilmtk_V0.2.ipynb | 7 | 113919 | {
"metadata": {
"name": "",
"signature": "sha256:ae9eeecb4861554fc335f71cafefc5e1af05964d717c6b335b83ab4f4d975449"
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from nilmtk import HDFDataStore, ElecMeter\... | apache-2.0 |
gibiansky/blog | posts/speech-recognition-neural-networks/post.ipynb | 1 | 494531 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've previously talked about using [recurrent neural networks for generating text](http://andrew.gibiansky.com/blog/machine-learning/recurrent-neural-networks/), based on a similarly titled [paper](http://www.cs.utoronto.ca/~ilya/pubs... | gpl-2.0 |
jingr1/SelfDrivingCar | TowDHistogramFilter/TowDHistogramFilter.ipynb | 1 | 69930 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two Dimensional Histogram Filter - Your First Feature (and your first bug).\n",
"Writing code is important. But a big part of being on a self driving car team is working with a **large** existing codebase. On high stakes engineer... | mit |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/sysmon_powershell_network_connection.ipynb | 1 | 4018 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PowerShell Network Connections\n",
"Detects a Powershell process that opens network connections - check for suspicious target ports and target systems - adjust to your environment (e.g. extend filters with company's ip range')"
... | gpl-3.0 |
stharrold/2015_Harrold_SDSSJ1600 | ipython_notebooks/20150727T203000_SDSS_J160036.83+272117.8_combined.ipynb | 2 | 8648110 | null | mit |
CivicKnowledge/metatab-py | examples/Pandas Reporter Example.ipynb | 3 | 8899 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pandasreporter as pr\n",
"\n",
"\n",
"# B17001, Poverty Status by Sex by Age\n",
"b17001 = pr.get_dataframe('B17001'... | bsd-3-clause |
acdh-oeaw/defc-app | import_nofk_vocabs.ipynb | 1 | 2971 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import csv, re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file = \... | mit |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l08c04_time_windows.ipynb | 1 | 7250 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Za8-Nr5k11fh"
},
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"i... | apache-2.0 |
wcmckee/wcmckee.com | posts/niktrans.ipynb | 1 | 14008 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>NikTrans</h1>\n",
"\n",
"Python script to create Nikola sites from a list of schools. Edits conf.py file for site name and licence. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"coll... | mit |
timothydmorton/transit-fitting | notebooks/k2_testing.ipynb | 1 | 1378190 | null | mit |
Alexander-Schiendorfer/active-learning-collectives | SamplingAbstraction-Experiments/analysis/readCentral.ipynb | 1 | 1673311 | null | mit |
tjwei/HackNTU_Data_2017 | Week07/HW1-Neural Matching.ipynb | 1 | 755 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"實作 https://github.com/tjwei/Neural-Matching/blob/master/matching-theano-VGG-one-patch.ipynb\n",
"\n",
"可參考： \n",
"* https://arxiv.org/abs/1601.04589\n",
"* https://github.com/awentzonline/image-analogies"
]
}
],
... | mit |
mattmcd/PyAnalysis | scripts/deep_learning/homl_tf_ch09.ipynb | 1 | 9976 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hands On Machine Learning Chapter 9\n",
"Examples from HOML on using TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"im... | apache-2.0 |
mcocdawc/chemcoord | Tutorial/Gradients.ipynb | 1 | 10106 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Gradients"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n"... | lgpl-3.0 |
birdsarah/bokeh-miscellany | old/resources/Inline.ipynb | 1 | 1691377 | null | gpl-2.0 |
lybicat/netbyte | notebooks/learn-pandas.ipynb | 1 | 12182 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"最近开始学习pandas用来进行数据分析的入门，这里将一些东西总结为一个notebook以方便查看。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 下载数据"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colla... | mit |
ChinaQuants/qlengine | examples/python/rates/curve construction.ipynb | 1 | 8376 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 1. Setup Evaluation Date\n",
"---------------"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": tru... | bsd-3-clause |
LimeeZ/phys292-2015-work | assignments/assignment08/InterpolationEx01.ipynb | 1 | 28380 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Interpolation Exercise 1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline... | mit |
abevieiramota/data-science-cookbook | 2016/network-analysis/(Response) Average Shortest Paths.ipynb | 2 | 3544 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercício 2 - Caminhos Médios\n",
"\n",
"Computar, para os mesmos __gráficos do Exercício 1__, a média dos caminhos mais curtos.\n",
"\n",
"Referência: [Wikipedia](https://en.wikipedia.org/wiki/Average_path_length)"
... | mit |
mdbecker/daa_philly_2015 | DataPhilly_Analysis.ipynb | 1 | 522105 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyzing the Philadelphia Data Science Scene with Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Instructions\n",
"* The latest version of this notebook can always be found and viewed ... | mit |
ds-modules/LINGUIS-110 | FormantsUpdated/Assignment.ipynb | 2 | 27637 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linguistics 110: Vowel Formants\n",
"\n",
"### Professor Susan Lin\n",
"\n",
"In this notebook, we use both data from an outside source and that the class generated to explore the relationships between formants, gende... | mit |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SolveMathProblems/0 - IntroductionToNumericalSolutions.ipynb | 1 | 12495 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numerical models\n",
"\n",
"We start with the numerical solution of a very simple differential\n",
"equation. In fact we choose something simple enough that we already \n",
"know the answer.\n",
"\n",
"\\\\[\... | mit |
danielskol/ml-cipher-cracker | report/Task 2.ipynb | 3 | 6818 | {
"metadata": {
"name": "",
"signature": "sha256:5070299ceb9a74a18bf2a610123db12296554fe49fb5074ce3055d2cda4698ef"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Metropolis implementation"... | mit |
metpy/MetPy | dev/_downloads/9041777e133eed610f5b243c688e89f9/surface_declarative.ipynb | 1 | 3417 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Surface Anal... | bsd-3-clause |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Winter2017_Homework2.ipynb | 1 | 12605 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"met... | mit |
besser82/shogun | doc/ipython-notebooks/converter/Tapkee.ipynb | 4 | 9435 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dimensionality Reduction with the Shogun Machine Learning Toolbox"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### *By Sergey Lisitsyn ([lisitsyn](https://github.com/lisitsyn)) and Fernando J. Ig... | bsd-3-clause |
texaspse/blog | media/f16-scientific-python/week2/Scientific Python Workshop 2.ipynb | 1 | 123445 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First import pandas and numpy"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"... | mit |
tensorflow/docs-l10n | site/ja/r1/tutorials/keras/overfit_and_underfit.ipynb | 1 | 30708 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "fTFj8ft5dlbS"
},
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"i... | apache-2.0 |
ktaneishi/deepchem | examples/notebooks/Estimators.ipynb | 2 | 17028 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using DeepChem with Tensorflow Data and Estimators\n",
"-----------------------------------------------\n",
"\n",
"When DeepChem was first created, Tensorflow had no standard interface for datasets or models. We created th... | mit |
greenelab/GCB535 | 15_Reproducibility/HW_Reproducible_Research.ipynb | 1 | 16766 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# In-class: Reproducible Computational Research #"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"***NOTE: Make sure your kernel is set to \"R (Sag... | bsd-3-clause |
johntellsall/shotglass | jupyter/postgres-releases.ipynb | 1 | 28372 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"# TODO: update Postgres Git :)"
]
},
{
"cell_type": "code",
"executi... | mit |
TheProgrammingDuck/Europa-Challenge | Site/WebWorldWindDocs.ipynb | 1 | 39987 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Foundations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We decided to use the Django web framework for our site. We believed that it would give us the flexibility we needed to perform the ba... | mit |
metpy/MetPy | v0.12/_downloads/e5685967297554788de3cf5858571b23/Natural_Neighbor_Verification.ipynb | 1 | 13668 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\nNatural Neighb... | bsd-3-clause |
konkam/perceptron_guide | README.ipynb | 1 | 22534 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Guide: quelques étapes pour programmer un perceptron\n",
"\n",
"\n",
"## Préliminaire: charger des images en Python et les mettre sous forme de vecteur\n",
"\n",
"### Les images\n",
"\n",
"Avec votre édite... | gpl-3.0 |
betoesquivel/onforums-application | testdataextractor/TestDataExtractor.ipynb | 2 | 247174 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clustering test data and evaluating clustering technique with it"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output... | mit |
ozorich/phys202-2015-work | assignments/midterm/ProjectEuler52.ipynb | 1 | 5253 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Project Euler: Problem 52"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"https://projecteuler.net/problem=52\n",
"\n",
"It can be seen that the ... | mit |
MonicaGutierrez/PracticalMachineLearningClass | exercises/06-Titanic_cross_validation.ipynb | 1 | 43691 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise 06\n",
"\n",
"## Data preparation and model evaluation exercise with Titanic data\n",
"\n",
"\n",
"\n",
"\n",
"We'll be working with a dataset from Kaggle's Titanic competition: [data](https://git... | mit |
Diyago/Machine-Learning-scripts | DEEP LEARNING/NLP/text analyses/NB-SVM strong linear baseline - classif.ipynb | 1 | 13103 | {
"cells": [
{
"metadata": {
"_cell_guid": "d3b04218-0413-4e6c-8751-5d8a404d73a9",
"_uuid": "0bca9739b82d5d51e1229243e03ea1b6db35c17e"
},
"cell_type": "markdown",
"source": "## Introduction\n\nThis kernel shows how to use NBSVM (Naive Bayes - Support Vector Machine) to crea... | apache-2.0 |
enakai00/jupyter_NikkeiLinux | No5/Figure11 - derivative_animation.ipynb | 1 | 55915 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[4-1]** 動画作成用のモジュールをインポートして、動画を表示可能なモードにセットします。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import mat... | apache-2.0 |
glouppe/scikit-optimize | examples/strategy-comparison.ipynb | 1 | 161729 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing surrogate models\n",
"\n",
"Tim Head, July 2016."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain":... | bsd-3-clause |
NYUDataBootcamp/Projects | UG_S16/Jerry_Allen_Gender_Pay_Gap.ipynb | 1 | 227767 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Gender Pay Gap Inequality in the U.S. and Potential Insights**\n",
"\n",
"A Research Project at NYU's Stern School of Buinsess — May 2016 \n",
"Written by Jerry \"Joa\" Allen (joa218@nyu.edu)\n",
"\n",
"**Abstr... | mit |
zhuanxuhit/deep-learning | embeddings/.ipynb_checkpoints/Skip-Grams-Solution-checkpoint.ipynb | 1 | 925622 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Skip-gram word2vec\n",
"\n",
"In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this... | mit |
decisionstats/pythonfordatascience | regression.ipynb | 1 | 26810 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imp... | apache-2.0 |
probml/pyprobml | notebooks/misc/pixel_cnn_vq_vae.ipynb | 1 | 2454065 | null | mit |
frainfreeze/studying | home/python/learningPython5thED/Learning python 5th ed..ipynb | 1 | 28780 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test your knowledge: Part II exercises\n",
"## 1. The basics\n",
"Run each of the following expressions, and try to\n",
"explain what’s happening in each case. Note that the semicolon in some of these\n",
"is being us... | mit |
jaybo/Python-Notebooks | TEMCA/RectToPolar.ipynb | 1 | 1539 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys, os\n",
"import cv2\n",
"import numpy as np\n",
"\n",
"cap = cv2.VideoCapture(0)"
]
},
{
"cell_type": "code",
"execution_count... | mit |
iancze/ScottiePippen | notebooks/Weighted Means.ipynb | 1 | 2723 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#... | mit |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2015.ipynb | 1 | 44123 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1A.e - TD not\u00e9, 5 d\u00e9cembre 2014\n",
"\n",
"Parcours de chemins dans un graphe acyclique (arbre)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata":... | mit |
muatik/my-coding-challenges | python/10daysOfStatistics/Day_4_Binomial_Distribution.ipynb | 1 | 4058 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10-days-of-statistics\n",
"### Day 4: Binomial Distribution I\n",
"https://www.hackerrank.com/challenges/s10-binomial-distribution-1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this c... | mit |
jbwhit/WSP-312-Tips-and-Tricks | notebooks/07-Some_basics.ipynb | 1 | 12537 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import absolute_import, division, print_function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Github"
]
},
{... | mit |
hchauvet/beampy | doc-src/auto_tutorials/positioning_system.ipynb | 1 | 10921 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\nBeampy Positioning s... | gpl-3.0 |
hktxt/MachineLearning | intro_to_pandas.ipynb | 1 | 76373 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "intro_to_pandas.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [
"JndnmDMp66FL",
"YHIWvc9Ms-Ll",
"TJffr5_Jwqvd"
],
"toc_visible": true,
"include_colab_... | gpl-3.0 |
QuantStack/quantstack-talks | 2019-06-26-GeoPython/notebooks/1.ipywidgets.ipynb | 1 | 5750 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repository: https://github.com/jupyter-widgets/ipywidgets\n",
"# Installation: \n",
"`conda install -c conda-forge ipywidgets`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple slider... | bsd-3-clause |
zzeleznick/cs194-final-proj | main/index.ipynb | 1 | 11420642 | null | mit |
zhaoyan1117/gaussian-classifier | src/p4.ipynb | 1 | 180747 | {
"metadata": {
"name": "",
"signature": "sha256:acd18dbd6dfe8df45adf27942f9afec429d73f57117dd1e3340db8de287f0c75"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Import library"
]
... | bsd-2-clause |
Ric01/Uso-Google-Finance-Python3 | Leer Precio Acciones Python 3.ipynb | 1 | 31262 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial: Uso de la libreria de Google Finance en Python para leer datos de acciones"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Paso 1: Importar las librerias necesarias"
]
},
{
"c... | gpl-3.0 |
crystalzhaizhai/cs207_yi_zhai | lectures/L6/L6.ipynb | 2 | 8989 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 6: Wednesday, September 20th 2017\n",
"## Towards Intermediate Python\n",
"Topics:\n",
"* Recap: How does this stuff really work?\n",
"* Nested environments\n",
"* Closures\n",
"* Decorators"
]
... | mit |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_classification_batch.ipynb | 1 | 39206 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"#... | apache-2.0 |
akseshina/dl_course | seminar_1/classwork.ipynb | 2 | 160726 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import xlrd"
]
},
{
"cell_type": "markd... | gpl-3.0 |
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