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mne-tools/mne-tools.github.io | 0.15/_downloads/plot_eeg_erp.ipynb | 1 | 12882 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n\nEEG processing and... | bsd-3-clause |
quantopian/research_public | notebooks/lectures/Random_Variables/answers/notebook.ipynb | 2 | 191504 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exercises: Random Variables - Answer Key\n",
"By Christopher van Hoecke, Max Margenot, and Delaney Mackenzie\n",
"\n",
"## Lecture Link : \n",
"https://www.quantopian.co... | apache-2.0 |
heyfaraday/CMB_test | outdated/0.2.3/4.12.16.ipynb | 1 | 966804 | {
"cells": [
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from math import sin, cos, tan, pi, sqrt, factorial, fabs, acos\n",
"\n",
"from numpy.fft import fft\n",
"from numpy import comp... | mit |
chengsoonong/crowdastro | notebooks/24_simple_pipeline.ipynb | 1 | 17131 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Pipeline\n",
"\n",
"This notebook contains the training pipeline for the simple (one-host) data set."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load training data"
]
},
... | mit |
rsignell-usgs/notebook | UGRID/FVCOM_hslice.ipynb | 1 | 83737 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FVCOM horizontal slice at fixed depth"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import num... | mit |
nvictus/svgpath2mpl | examples/arcs.ipynb | 1 | 53320 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "chronic-individual",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id"... | bsd-3-clause |
wasit7/PythonDay | notebook/00 index.ipynb | 4 | 1584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcome to PythonDay Tutorial\n",
"====\n",
"\n",
"This tutorial will give you fundametal concepts in PYthon programming and also bring you to the Python world, where coding is fun :)\n",
"\n",
"Wasit\n",
"\n",
... | bsd-3-clause |
ergosimulation/mpslib | scikit-mps/examples/ex_mpslib_mask.ipynb | 2 | 92995 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MPSlib: Using masks"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mpslib as mps\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n"... | lgpl-3.0 |
sgkang/DamGeophysics | notebook/ReadFiles_all dataOld.ipynb | 1 | 614488 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
"\n",
" python setup.... | mit |
climberwb/pycon-pandas-tutorial | Solutions-4.ipynb | 1 | 128449 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs... | mit |
andrewzwicky/puzzles | advent_of_code/2021/15.ipynb | 1 | 10316 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from s... | mit |
hackpert/CBSECSPracticals | Question19.ipynb | 2 | 1155 | {
"metadata": {
"name": "",
"signature": "sha256:fb1a4f9027148bb2fcb16846378de195799c87dfec58431fbe4fb3c8e5e7b156"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a class to implement stack in pytho... | mit |
hail-is/hail | hail/python/hail/docs/tutorials/06-joins.ipynb | 3 | 13091 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table Joins Tutorial\n",
"\n",
"This tutorial walks through some ways to join Hail tables. We'll use a simple movie dataset to illustrate. The movie dataset comes in multiple parts. Here are a few questions we might naturall... | mit |
streety/biof509 | Wk09-Advanced-ML-tasks-Deep-Learning.ipynb | 2 | 77388 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 8 - Advanced Machine Learning\n",
"\n",
"During the course we have covered a variety of different tasks and algorithms. These were chosen for their broad applicability and ease of use with many important techniques and a... | mit |
OxES/OxKeplerSC | examples/Example_cbv_1.ipynb | 3 | 92926 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pyla... | gpl-3.0 |
KIPAC/StatisticalMethods | notes/bayes_law.ipynb | 1 | 43096 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Notes: Bayes' Law\n",
"\n",
"In which we will\n",
"* see how the concept of a generative model and our background in the calculus of probability allow us to infer pr... | gpl-2.0 |
Obus/scikit-learn | my_playground/Untitled0.ipynb | 1 | 12095 | {
"metadata": {
"name": "",
"signature": "sha256:5d47726a7d60bd7f7fa9fa43c2812e28f382cb78670f060f09a17663dad6ef68"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn\n",
"from sklearn impo... | bsd-3-clause |
azubiolo/itstep | it_step/ml_from_scratch/4_least-squares_continued/least-squares_empty.ipynb | 1 | 16603 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Ordinary Least Squares -- Part II\n",
"## Course recap\n",
"This lab consists in implementing the **Ordinary Least Squares** (OLS) algorithm, which is **a linear regression with... | mit |
greenelab/continuous_analysis | Shippable_Plotting.ipynb | 1 | 134544 | {
"cells": [
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import datetime\n",
"#%matplotlib inline\n",
"\n",
"out... | bsd-3-clause |
ercius/openNCEM | ncempy/notebooks/loading_data_examples.ipynb | 1 | 3621486 | null | gpl-3.0 |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_linear_model_patterns.ipynb | 1 | 4496 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Linear classifier ... | bsd-3-clause |
jwjohnson314/data-801 | notebooks/more_data_structures.ipynb | 1 | 42320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More on data structures\n",
"\n",
"#### Iterable vs. Iterators\n",
"\n",
"Lists are examples of iterable data structures, which means that you can iterate over the actual objects in these data structures."
]
},
... | mit |
lexieheinle/jour407homework | StormData/nebraska-storm-analysis.ipynb | 1 | 37987 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import agate\n",
"text = agate.Text()\n",
"number = agate.Number()\n",
"tester = agate.TypeTester(force={\n",
" 'WFO': text,\n",
" 'DAM... | mit |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/labs/sample_babyweight.ipynb | 1 | 84983 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating a Sampled Dataset\n",
"\n",
"**Learning Objectives**\n",
"\n",
"1. Setup up the environment\n",
"1. Sample the natality dataset to create train, eval, test sets\n",
"1. Preprocess the data in Pandas d... | apache-2.0 |
elliekinz/Disease-ontology | wiki_pubmed_fuzzy/.ipynb_checkpoints/Wiki-PubMed-Fuzzy-checkpoint.ipynb | 1 | 15501 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('./../')\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"meta... | apache-2.0 |
jamesfolberth/NGC_STEM_camp_AWS | notebook_solutions/ML_morning_JTN/02_Logistic_Regression_and_Text_Models.ipynb | 2 | 60250 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 3: Logistic Regression and Text Models\n",
"***\n",
"\n",
"<img src=\"figs/logregwordcloud.png\",width=1000,height=50>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem ... | bsd-3-clause |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Discrete_Fourier_Transform.ipynb | 2 | 148643 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "cod... | gpl-3.0 |
aje/POT | notebooks/plot_otda_classes.ipynb | 1 | 227907 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# OT for domain adaptation\n",
"\n",
"\n",
"Th... | mit |
pmorissette/bt | examples/Strategy_Combination.ipynb | 1 | 214276 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook creates a parent strategy(combined) with 2 child strategies(Equal Weight, Inv Vol).\n",
"\n",
"Alternatively, it creates the 2 child strategies, runs the backtest, combines the results, and creates a parent strate... | mit |
WomensCodingCircle/CodingCirclePython | Lesson08_Dictionaries/Dictionary.ipynb | 2 | 12320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dictionaries\n",
"\n",
"A dictionary is datatype that contains a series of key-value pairs. It is similar to a list except for that the indices of the values can be strings, tuples, etc. not just integers. It is also differen... | mit |
qutip/qutip-notebooks | docs/guide/Eseries.ipynb | 2 | 40174 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Eseries Class\n",
"\n",
"###Contents\n",
"- [Exponential-Series Representation of Quantum Objects](#exponential)\n",
"- [Applications of Exponential-Series](#applications)"
]
},
{
"cell_type": "code",
"exe... | lgpl-3.0 |
xesscorp/myhdlpeek | examples/peeker_simple_mux.ipynb | 1 | 21415 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#A-Simple-Multiplexer-Example\" data-toc-modified-id=\"A-Simple-Multiplexer-Example-1\... | mit |
AbhiK24/Introductory_Machine_Learning | Data_Visualisation/Deep Learning - TSNE.ipynb | 1 | 429657 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/user/anaconda/envs/tensorflow/lib/python2.7/site-packages/matplotlib/font_manager... | mit |
Michaelt293/ANZSMS-Programming-workshop | Python_tutorial.ipynb | 1 | 38981 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#ANZSMS 2015 Python Programming Workshop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Print Function "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [... | cc0-1.0 |
edwardd1/phys202-2015-work | assignments/assignment06/LaTeXEx01.ipynb | 1 | 13209 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# LaTeX Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The images of the equations on this page were taken from the Wikipedia pages referenced ... | mit |
CartoDB/cartoframes | docs/examples/data_visualization/legends/color_category_legend.ipynb | 1 | 58159 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Color category legend\n",
"The `color-category` type draws a categorical legend on your map that is based on your data's geometry using either the `color` (default) or `strokeColor` property of your visualization.\n",
"\n",
... | bsd-3-clause |
ChristinaLK/xd | misc/plotting.ipynb | 1 | 10837 | {
"metadata": {
"name": "",
"signature": "sha256:67bc813e70642aa32e2996e280d8a18755558387cb9df4e1882009281b950285"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"... | mit |
UWSEDS/LectureNotes | week_4/Exceptions.ipynb | 1 | 16840 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... | bsd-2-clause |
chicago-justice-project/article-tagging | lib/notebooks/senteval_budgeting.ipynb | 1 | 217157 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import json\n",
"sys.path.append('..')\n",
"import tagnews\n",
"import matplotlib.pyplot as plt\n",
"import datetime as dt\n",
"import numpy as np\n",
"... | mit |
satishgoda/learning | python/jupyter/usecases/jupyter_notebook_usecases.ipynb | 1 | 102615 | {
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from IPython.core.display import Image, HTML"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": tr... | mit |
wathen/PhD | MHD/FEniCS/ShiftCurlCurl/CppGradient/Efficient/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | 1 | 3648 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from dolfin import *\n",
"mesh = UnitSquareMesh(int(2),int(2))\n",
"order = 1\n",
"Magnetic = FunctionSpace(mesh, \... | mit |
DoWhatILove/turtle | programming/python/notebooks/matplotlib/barchart_do.ipynb | 2 | 2942 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"n_groups = 5\n",
"\n",
"means_men = (20, 35, 30, 35, 27)\n",
"std... | mit |
folivetti/BIGDATA | Spark/Lab2_Spark_PySpark.ipynb | 1 | 37351 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# **Spark + Python = PySpark**\n",
"\n",
"#### Esse notebook introduz os conceitos básicos do Spark através de sua interface com a linguagem Python. Como aplicação... | mit |
gregorjerse/rt2 | 2015_2016/lab13/Extending values on vertices-template.ipynb | 1 | 6569 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extending values on vertices to a discrete gradient vector field\n",
"During extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires qui... | gpl-3.0 |
annajur/cbp | GALI.ipynb | 1 | 1231011 | null | mit |
vicolab/ml-pyxis | examples/torch-dataset.ipynb | 1 | 8070 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyTorch dataset interface"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example we will look at how a `pyxis` LMDB can be used with PyTorch's `torch.utils.data.Dataset` and `torch.utils.dat... | mit |
cliburn/sta-663-2017 | notebook/09_Machine_Learning.ipynb | 1 | 1360513 | null | mit |
erinspace/share_tutorials | SHARE_Curation_Associates_Overview.ipynb | 1 | 21508 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Jupyter Notebooks and the SHARE API\n",
"----\n",
"\n",
"Learn About\n",
"- Jupyter Notebooks and Python\n",
"- Making API Calls\n",
"- Using the SHARE S... | apache-2.0 |
xiph/rav1e | doc/regress_log-bitrate_wrt_log-quantizer.ipynb | 1 | 35672 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rate-control Empirical Analysis"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"<script>\n",
" function code_toggle() {\n",
" if (code_shown){\n",
" $('div.input').hide('500');\n",
... | bsd-2-clause |
jGaboardi/AAG_16 | .ipynb_checkpoints/AAG_16 copy 2-checkpoint.ipynb | 1 | 1660794 | null | lgpl-3.0 |
emiliom/stuff | MMW_API_landproperties_demo.ipynb | 1 | 15760 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model My Watershed (MMW) API Demo\n",
"[Emilio Mayorga](https://github.com/emiliom/), University of Washington, Seattle. 2018-5-10,17. Demo put together using as a starting point instructions from Azavea from October 2017."
]... | cc0-1.0 |
CRPropa/CRPropa3 | doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb | 1 | 11591 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D MHD models\n",
"This notebook explains how to use cubic results of 3D MHD models on a uniform grid in CRPropa.\n",
"\n",
"## Supplied data\n",
"\n",
"The fields need to be supplied in a raw binary file that con... | gpl-3.0 |
phockett/ePSproc | notebooks/ePSproc_test_BLM_numerics_260721.ipynb | 1 | 2688513 | null | gpl-3.0 |
KIPAC/StatisticalMethods | tutorials/old/GPRegression.ipynb | 1 | 25060 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Week 8 Tutorial\n",
"\n",
"## Gaussian Process Regression\n",
"\n",
"In this example, we return to the \"straight line\" problem, generate some mock data, and in... | gpl-2.0 |
SimonBiggs/electronfactors | paper/Method -- Equivalent ellipse example.ipynb | 1 | 78704 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import yaml\n",
"import numpy as np\n",
"\n",
"import descartes as des\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
... | agpl-3.0 |
NlGG/Projects | OLS_ML_GMM.ipynb | 1 | 7468 | {
"cells": [
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"... | mit |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/win_usb_device_plugged.ipynb | 1 | 2750 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# USB Device Plugged\n",
"Detects plugged USB devices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: USB Device Plugged\n",
" id: 1a4bd6e3-4c6e-... | gpl-3.0 |
nitin-cherian/LifeLongLearning | Python/Python Morsels/multimax/my_try/multimax.ipynb | 1 | 8189 | {
"cells": [
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def multimax(iterable, key=None):\n",
" \"\"\"\n",
" Function that takes an iterable and returns all\n",
" maximum values found in the iterable\n",... | mit |
braysia/covertrace | doc/jupyter_examples/ktr_modeling.ipynb | 1 | 1314572 | null | mit |
akimbekov/Stock_prediction_using_ML_and_Deep_learning | .ipynb_checkpoints/Project-checkpoint.ipynb | 1 | 785102 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outline is based on this sentence:\n",
"\n",
"For example, a project that performs various binary classification methods on a social science dataset you may want to focus on data munging, method selection, method evaluation, fe... | mit |
VascoVisser/euler | notebooks/0018.ipynb | 1 | 1669 | {
"metadata": {
"name": "",
"signature": "sha256:70c7fe68ef82e54771b30318e600ee3dcb86b946a4deee90c0ce62008164efb3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"raw_triangle=\"\"\"75\n",
"95 64\n",
... | mit |
davidrpugh/Pyreto | examples/clauset-shalizi-newman-2009.ipynb | 1 | 37799 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"au... | mit |
google/or-tools | examples/notebook/constraint_solver/vrp_with_time_limit.ipynb | 1 | 7103 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may... | apache-2.0 |
0ppen/introhacking | Code From Book.ipynb | 1 | 31118 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code from Book"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Getting Started"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
... | mit |
diminishedprime/.org | programmey_stuff/chord_thingy/Chord Thingy.ipynb | 1 | 24402 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def modBase1(num, base):\n",
... | mit |
equalitie/BotHound | ipython/Clustering.ipynb | 2 | 942285 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clustering\n",
"===\n",
"*Bothound project*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialization"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 82,
======... | agpl-3.0 |
JustasB/MitralSuite | Davison2003.ipynb | 1 | 99015 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### Define Model Setup Steps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source"... | mit |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_megsim_data_single_trial.ipynb | 1 | 2192 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# MEGSIM singl... | bsd-3-clause |
ML4DS/ML4all | U2.SpectralClustering/.ipynb_checkpoints/SpecClustering_student-checkpoint.ipynb | 1 | 914180 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "2b7256b9-a1c7-4314-a936-3f298d6d53f3"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Spectral Clustering Algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {
"slide... | mit |
idc9/law-net | vertex_metrics_experiment/procedural_v_substantive_scotus.ipynb | 1 | 379458 | {
"cells": [
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
... | mit |
logmonster/blog | wordpress/move_around_mongo_n_es_1/import2mongo.ipynb | 2 | 10672 | {
"cells": [
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.4.0\n",
"something wrong with the data line > 5\n",
"something wrong with the data line > 11\n"... | gpl-3.0 |
karlstroetmann/Formal-Languages | Python/FSM-2-Dot.ipynb | 2 | 6766 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.display import HTML\n",
"with open('../style.css', 'r') as file:\n",
" css = file.read()\n",
"HTML(css)"
]
},
{
"cell_type": "markdown",
"metadata"... | gpl-2.0 |
crystalzhaizhai/cs207_yi_zhai | lectures/L4/Exercise_2_final.ipynb | 1 | 3668 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"This exercise will walk you through the creation of a dictionary.\n",
"1. First, load the data in the `names.txt` file. This file contains common names of some species. Load this into a list.\n",
"2. Do... | mit |
endJunction/CppDevTools | compile_time_analysis/compile_time_analysis.ipynb | 1 | 197452 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import matplotlib\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
... | gpl-3.0 |
google/earthengine-community | guides/ipynb/image_math.ipynb | 1 | 9047 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Copyright 2021 The Earth Engine Community Authors { display-mode: \"form\" }\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may n... | apache-2.0 |
PAIR-code/facets | colab_facets.ipynb | 1 | 4658 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Facets Dive and Overview Colab Example",
"version": "0.3.2",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
... | apache-2.0 |
Xero-Hige/Notebooks | Concurrentes/2017-1C/Ejercicio Seniales.ipynb | 1 | 7964 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#include <stdio.h>\n",
"#include <signal.h>\n",
"#include <unistd.h>\n",
"\n",
"int main (void){\n",
" printf(\"Proceso %d\\n\",getpid());\n",
... | gpl-3.0 |
rickiepark/tfk-notebooks | first-contact-with-tensorflow/chapter5_convolution_neural_network.ipynb | 1 | 11084 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"simple_neural_network 예제에서 MNIST 데이터를 이미 다운 받았으므로 다시 다운 받지 않습니다."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_ty... | mit |
tritemio/FRETBursts | notebooks/Example - Customize the us-ALEX histogram.ipynb | 1 | 13387 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example - Customize the μs-ALEX histogram\n",
"\n",
"*This notebook is part of smFRET burst analysis software [FRETBursts](http://opensmfs.github.io/FRETBursts/).*"
]
},
{
"cell_type": "markdown",
"metadata": {},... | gpl-2.0 |
StingraySoftware/notebooks | Simulator/Concepts/Inverse Transform Sampling.ipynb | 1 | 26164 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inverse Transform Sampling\n",
"\n",
"This notebook will conceptualize how inverse transform sampling works"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
... | mit |
gvanderheide/discreteMarkovChain | docs/multirandomwalk.ipynb | 1 | 5105 | {
"metadata": {
"name": "",
"signature": "sha256:505ba4c2550b28b16b276cf231b060ef5e3f50cdf4a960b88f87e937c819b046"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
... | mit |
spacedrabbit/PythonBootcamp | Advanced Python Objects - Test.ipynb | 1 | 5568 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advanced Python Objects Test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Numbers\n",
"\n",
"**Problem 1: Convert 1024 to binary and hexadecimal representation:**"
]
},
... | mit |
gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | 6_ARTVIEW-dev-interface.ipynb | 2 | 994 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## DEVELOPMENT OF ARTVIEW, A MODULAR GRAPHICAL USER INTERFACE FOR RADAR DATA\n",
"##### 1:30 PM"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
... | bsd-2-clause |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/sysmon_ghostpack_safetykatz.ipynb | 1 | 2658 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Detection of SafetyKatz\n",
"Detects possible SafetyKatz Behaviour"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Detection of SafetyKatz\n",
"... | gpl-3.0 |
pramitchoudhary/Experiments | notebook_gallery/other_experiments/explore-models/modelinterpretation/titanic_sbrl_evaluation.ipynb | 1 | 22385 | {
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"code_folding": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
... | unlicense |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Seq2Seq_solutions.ipynb | 1 | 258483 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
... | cc0-1.0 |
ssdoz2sk/hour_of_code_python_crawler_2016_5 | notebook2/Simple2 - Elearning.ipynb | 1 | 13383 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample2 - Elearning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirement"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"o... | gpl-2.0 |
aboSamoor/compsocial | freebase/Professions.ipynb | 1 | 21658 | {
"metadata": {
"name": "",
"signature": "sha256:4a78c085b0025c5fdae2f162d4c72617ad2188fbe15cdaaeedb6c7388dc0822c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from time import time,sleep\n",
"from... | gpl-3.0 |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/tutorials/04-Intro_pyspark_sparkSQL.ipynb | 1 | 17982 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **Introduction to Spark SQL via PySpark**\n",
"----------------------------------------------------------------------------\n",
"## Goals:\n",
"* Get familiarized with the basics of Spark SQL and PySpark\n",
"* Learn ... | gpl-3.0 |
opengeostat/pygslib | pygslib/Ipython_templates/broken/vtk_tools.ipynb | 2 | 161065 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# VTK tools\n",
"\n",
"Pygslib use VTK:\n",
"\n",
" - as data format and data converting tool\n",
" - to plot in 3D\n",
" - as a library with some basic computational geometry functions, for example to know if a... | mit |
bmanubay/open-forcefield-tools | examples/substructure_linking.ipynb | 1 | 47783 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linking molecule fragments via reaction SMIRKS\n",
"\n",
"To test/develop SMIRFF force fields, it is extremely helpful to be able to generate molecules containing assorted combinations of various molecular fragments or substr... | mit |
predictscan3/scan3 | analysis_nbs/Normalise Binary Fields.ipynb | 1 | 5382 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from os.path import join, basename, dirname\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sys import path\n",
"from os import ... | mit |
fs714/ipyexample | chan/01_Centre_Expand_Handler_Alpha_14.ipynb | 2 | 7764 | {
"cells": [
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from IPython.core.debugger import Pdb; pdb = Pdb()\n",
"\n",
"\n",
"def get_dow... | apache-2.0 |
kubeflow/kfp-tekton | samples/katib/early-stopping.ipynb | 1 | 12750 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kubeflow Pipelines with Katib component\n",
"\n",
"In this notebook you will:\n",
"- Create Katib Experiment using random algorithm.\n",
"- Use median stopping rule as an early stopping algorithm.\n",
"- Use Kuber... | apache-2.0 |
fionapigott/Data-Science-45min-Intros | python-decorators-101/python-decorators-101.ipynb | 6 | 42484 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python decorators\n",
"\n",
"Josh Montague, 2015-12\n",
"\n",
"*Built on OS X, with IPython 3.0 on Python 2.7*\n",
"\n",
"In this session, we'll dig into some details of Python functions. The end goal will be ... | unlicense |
herruzojm/udacity-deep-learning | language-translation/dlnd_language_translation.ipynb | 1 | 607501 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Language Translation\n",
"In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and F... | mit |
dianafprieto/SS_2017 | .ipynb_checkpoints/05_NB_VTKPython_Scalar-checkpoint.ipynb | 1 | 5018 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"imgs/header.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualization techniques for scalar fields in VTK + Python"
]
},
{
"cell_type": "markdown",
"metadata": {},... | mit |
jskDr/jamespy_py3 | wireless/infotheory_nb/info-theory-Copy2.ipynb | 2 | 2459 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sage.all import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
... | mit |
probml/pyprobml | notebooks/book2/07/laplace_approx_beta_binom_jax.ipynb | 1 | 734 | {
"cells": [
{
"cell_type": "markdown",
"id": "6cff39e7",
"metadata": {},
"source": [
"Source of this notebook is here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/04/laplace_approx_beta_binom_jax.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"displa... | mit |
smsaladi/murraylab_tools | examples/Echo Setup Usage Examples.ipynb | 1 | 41841 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Welcome to the Echo package"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"echo\" subpackage of murraylab_tools is designed to automate the setup of reactions (mostly TX-TL reactions) with the... | mit |
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