repo_name stringlengths 6 92 | path stringlengths 7 220 | copies stringclasses 78
values | size stringlengths 2 9 | content stringlengths 15 1.05M ⌀ | license stringclasses 15
values |
|---|---|---|---|---|---|
AtmaMani/pyChakras | udemy_ml_bootcamp/Big-Data-and-Spark/Introduction to Spark and Python.ipynb | 4 | 6837 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Spark and Python\n",
"\n",
"Let's learn how to use Spark with Python by using the pyspark library! Make sure to view the video lecture explaining Spark and RDDs before continuing on with this code.\n",
"\n... | mit |
wehlutyk/brainscopypaste | data/notebooks/Model(time=Time.discrete, source=Source.all, past=Past.last_bin, durl=Durl.all, max_distance=2) - variation.ipynb | 1 | 4958792 | null | gpl-3.0 |
ioam/holoviews | examples/reference/elements/bokeh/BoxWhisker.ipynb | 1 | 3106 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
"<dl class=\"dl-horizontal\">\n",
" <dt>Title</dt> <dd> BoxWhisker Element</dd>\n",
" <dt>Dependencies</dt> <dd>Bokeh</dd>\n",
" <dt>Backend... | bsd-3-clause |
mlund/kirkwood-buff | nacl-water/nacl.ipynb | 1 | 94491 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kirkwood-Buff example: NaCl in water\n",
"\n",
"In this example we calculate Kirkwood-Buff integrals in a solute (c) and solvent (w) system and correct for finite size effects as described at http://dx.doi.org/10.1073/pnas.09... | mit |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160502월_1일차_분석 환경, 소개/14.Pandas 고급 인덱싱.ipynb | 1 | 44357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas 고급 인덱싱"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pandas는 numpy 행렬과 같이 comma를 사용한 복수 인덱싱을 지원하기 위해 다음과 같은 특별한 인덱서 속성을 제공한다.\n",
"\n",
"* `ix` : 라벨과 숫자를 동시에 지원하는 복수 인덱싱\n",
"* `... | mit |
jsaudino/75.06_tp1_acs | old/Dai Parte TP.ipynb | 2 | 254061 | {
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np \n",
"import pandas as pd\n",
"\n",
"# plots\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib ... | mit |
google-research/google-research | label_bias/Label_Bias_EqualOpportunity.ipynb | 1 | 41767 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "AN3Ek2S9IIwK"
},
"source": [
"Copyright 2019 Google LLC\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this ... | apache-2.0 |
faizankshaikh/Project | trials/trial5-Copy1.ipynb | 1 | 399004 | {
"cells": [
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import modules\n",
"%matplotlib inline\n",
"import random\n",
"import pylab\n",
"import pandas as pd\n",
"import numpy as np\n",
"import cPic... | mit |
biocore/scikit-bio-presentations | scipy-2015/A Bioinformatics Library for Data Scientists, Students, and Developers.ipynb | 1 | 20269 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"%%html\n",
"<link rel='stylesheet' type='text/css' href='custom.css'/>"
]
},
{
"cell_type": "code",
... | bsd-3-clause |
open2c/bioframe | docs/guide-performance.ipynb | 1 | 233478 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates performance of typical use cases for bioframe on sets of randomly generated intervals."
]
},
{
"cell_ty... | mit |
toth-adam/rl_games | windy_gridworld/Epsilon-greedy_TD_lambda_online_windy_grid_world.ipynb | 1 | 10289 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
... | mit |
h-mayorquin/camp_india_2016 | tutorials/rdes/01_Introducing_rdes.ipynb | 1 | 4171 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basics\n",
"This is the basic usage of rdesigneur"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"moose.showfields function givis details of moose objects."
]
},
{
"cell_type": "code",
... | mit |
infimath/optimization-method | week02/Problem 3.ipynb | 1 | 10880 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PART A\n",
"\n",
"Consider a diet problem with two different foods (Food 1 and Food 2) and three different nutrients (Nutrient 1, Nutrient 2, and Nutrient 3). The amount of ounces of each nutrient per unit of food is given in t... | mit |
pdonorio/notebookers | notebooks/Slidestest.ipynb | 1 | 1239 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Hello*\n",
"\n",
"# Test"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "st... | mit |
f-fathurrahman/ffr-ElectronicStructure.jl | LF/LeeTuckerman2006/LeeTuckerman2006.ipynb | 1 | 174910 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11-element Array{Float64,1}:\n",
" -4.54545 \n",
" -3.63636 \n",
" -2.72727 \n",
" -1.81818 \n",
" -0.909091\n",
" 0.0 \... | mit |
sampattuzzi/interview-insights | interview_insights.ipynb | 1 | 18317 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np \n",
"import matplotlib.p... | gpl-2.0 |
joefutrelle/scipy-talk | talk4/5 iteration over sequences and dicts.ipynb | 1 | 2057 | {
"metadata": {
"name": "",
"signature": "sha256:2f1f6105bbb3053472b6d9e983455a29d712414dd8cfc0e3323a08e08da97f6e"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# iteration is typically done using for/in ... | mit |
SATHVIKRAJU/Inferential_Statistics | Human_Temp.ipynb | 1 | 28321 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What is the True Normal Human Body Temperature? \n",
"\n",
"#### Background\n",
"\n",
"The mean normal body temperature was held to be 37$^{\\circ}$C or 98.6$^{\\circ}$F for more than 120 years since it was first conc... | mit |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/advance-theme/Request.ipynb | 1 | 12111 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python Request 库入门"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 1 urllib2 和 Request对比\n",
"*Get*请求至`https://api.github.com/`"
]
},
{
"cell_type": "code",
... | mit |
Nikolay-Lysenko/presentations | hard_use_and_abuse/surrogate_modelling_with_neural_networks.ipynb | 1 | 534460 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem of [surrogate modelling](https://en.wikipedia.org/wiki/Surrogate_model) is as follows. There are several control variables tha... | mit |
kwecht/ML-with-Kaggle | notebooks/Linear Regression - DC Bicycle Share.ipynb | 1 | 122272 | {
"metadata": {
"name": "",
"signature": "sha256:5322b4f174f50cf151baabcccaade8c76c8fb1c01e50622fb5dccb9fccd14f9c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Introduction"
]
}... | mit |
CompPhysics/ComputationalPhysics | doc/pub/integrate/ipynb/.ipynb_checkpoints/integrate-checkpoint.ipynb | 1 | 125829 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Computational Physics Lectures: Numerical integration, from Newton-Cotes quadrature to Gaussian quadrature -->\n",
"# Computational Physics Lectures: Numerical integration, from Newton-Cotes quadrature to Gaussian q... | cc0-1.0 |
msanterre/deep_learning | sentiment-rnn/Sentiment_RNN.ipynb | 1 | 49263 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentiment Analysis with an RNN\n",
"\n",
"In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate since we can include inf... | mit |
trustrachel/graphviz | NetworkX Graph Generators.ipynb | 2 | 187965 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import networkx as nx"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
... | apache-2.0 |
KIPAC/StatisticalMethods | tutorials/generative_models.ipynb | 1 | 8095 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial: Generative Models\n",
"\n",
"This exercise is mostly to practice going from a real-world(ish) problem described in words to an actionable model. By model, we mean\n",
"1. a list of quantities comprising your dat... | gpl-2.0 |
lakmalsilva/machineLearning | Triage-MHWEB-Final.ipynb | 1 | 201431 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Issue tracker classification using RNN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing required packages into python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"... | apache-2.0 |
liufuyang/deep_learning_tutorial | course-deeplearning.ai/course1-nn-and-deeplearning/Logistic+Regression+with+a+Neural+Network+mindset+v3.ipynb | 1 | 350759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression with a Neural Network mindset\n",
"\n",
"Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through h... | mit |
abigailStev/whizzy_scripts | fft_comparison.ipynb | 1 | 10406 | {
"metadata": {
"name": "",
"signature": "sha256:8acd9453058711bb14707e6024beda39fdf928b2f04f2e874bbfe1534f73c6c4"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"FFT_comparison.ipynb"
... | mit |
jjonte/udacity-deeplearning-nd | py3/project-4/dlnd_language_translation.ipynb | 1 | 491791 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": 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... | unlicense |
csaladenes/blog | airports/airportia_hu_merger.ipynb | 2 | 57248 | {
"cells": [
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd, json, numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": ... | mit |
lilvinz/deco | gourneyras_decoplan_1.ipynb | 1 | 133288 | {
"metadata": {
"name": "",
"signature": "sha256:eba5f52b7452c39b754ca8dc4531c78c9a4238a74df90c10d4f6869172fb83f7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import deco\n",
"import deco_mix\n",
... | gpl-2.0 |
hainm/scikit-xray-examples | demos/powder_calibration/D_estimate_demo.ipynb | 1 | 17160 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [broken] An **almost** correct diffraction calibration notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
... | bsd-3-clause |
QuLogic/folium | examples/Polyline_text_path.ipynb | 1 | 15572 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5.0+27.g2d457b0.dirty\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__vers... | mit |
kubeflow/kfserving-lts | docs/samples/pipelines/kfs-pipeline-v1alpha2.ipynb | 1 | 6413 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KFServing Pipeline samples\n",
"\n",
"This notebook uses an older version of the KFServing Pipelines component meant for clusters using a KFServing version less than v0.5.0 which only supports the v1alpha2 API."
]
},
{... | apache-2.0 |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/ukesm1-0-mmh/seaice.ipynb | 1 | 99809 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Seaice \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NERC \n",
"**Source ID**: UKESM1-0-MMH \n",
"**T... | gpl-3.0 |
kastnerkyle/kastnerkyle.github.io-nikola | blogsite/posts/robust-matrix-decomposition.ipynb | 1 | 532859 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrix factorization is a very interesting area of machine learning research. Formulating a problem as a 2D matrix $X$ to be decomposed into multiple matrices, which combine to return an approximation of $X$, can lead to state of the a... | bsd-3-clause |
open2c/bioframe | docs/tutorials/tutorial_assign_motifs_to_peaks.ipynb | 1 | 136313 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to: assign TF Motifs to ChIP-seq peaks\n",
"\n",
"This tutorial demonstrates one way to assign CTCF motifs to CTCF ChIP-seq peaks using bioframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metad... | mit |
imaginebog/kmc_proc | notebooks/process_fs_groups-40.ipynb | 1 | 5814 | {
"metadata": {
"name": "",
"signature": "sha256:05b219d769a01689118e7df51e8e83d9edff759772a07a31e4e5d8646dbd128d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas"
],
"language": "py... | mit |
uliang/First-steps-with-the-Python-language | Day 1 - Unit 1.1.ipynb | 1 | 32050 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function, division"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Your first steps with Python\n",... | mit |
flohorovicic/pynoddy | docs/notebooks/.ipynb_checkpoints/SALib-sensitivity-analysis-checkpoint.ipynb | 3 | 274819 | {
"metadata": {
"name": "",
"signature": "sha256:a7283ef7a962917a52e91b39c602ba27b58dff4ef4781afab8a5d06701c37c76"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Global Sensitivity Analysis with SALib m... | gpl-2.0 |
hall1467/wikidata_usage_tracking | jupyter_notebooks/.ipynb_checkpoints/entity_views-checkpoint.ipynb | 1 | 71714 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics for Entity Page Views"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "s... | mit |
mkhuthir/learnPython | jupyter/Basics/Fibonacci_Sequence.ipynb | 1 | 2308 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recursive Fibonacci sequence!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"def fibo(n): \n",
" if n == 0:\n",
" return 0\n",
" elif ... | mit |
saashimi/code_guild | interactive-coding-challenges/stacks_queues/sort_stack/sort_stack_challenge.ipynb | 2 | 5023 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<small><i>This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>"
]
},
{
"cell_type": "markdown"... | mit |
gastonstat/stat259 | tutorials/cyclones.ipynb | 1 | 12175 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read in data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"myfile = open('cyclone.dat', 'r')\n",
"content = myfile.readlines(... | mit |
luwei0917/awsemmd_script | notebook/Optimization/structure_prediction_analysis_old_set.ipynb | 1 | 1193793 | null | mit |
rhiever/scipy_2015_sklearn_tutorial | notebooks/03.2 Methods - Unsupervised Preprocessing.ipynb | 19 | 6337 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example from Image Processing"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as... | cc0-1.0 |
mne-tools/mne-tools.github.io | 0.18/_downloads/a35e576fa66929a73782579dc334f91a/plot_time_frequency_mixed_norm_inverse.ipynb | 1 | 8096 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Compute MxNE... | bsd-3-clause |
phanrahan/magmathon | notebooks/tutorial/icestick/TFF.ipynb | 1 | 4961 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Toggle Flip-Flop\n",
"\n",
"In this example we create a toggle flip-flop (TFF) from a d-flip-flop (DFF) and an xor gate. In `Magma`, finite state machines can be constructed by composing combinational logic with register pri... | mit |
chaitra8/ml_lab_ecsc_306 | labwork/lab2/sci-learn/non_linear_regression.ipynb | 19 | 3403 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n====================... | apache-2.0 |
RaspberryJamBe/ipython-notebooks | notebooks/nl-be/101 - Intro - Python leren kennen en IPython gebruiken.ipynb | 1 | 16768 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Hm, zullen we maar beginnen?\n",
"Deze applicatie heet IPython en je kan er Python code mee uitvoeren (Python is een soort programmeertaal; een manier om aan een computer uit te leggen wat die computer voor jou moet doen).\n",
... | cc0-1.0 |
mprego/NBA | Regression/Regression.ipynb | 2 | 143387 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NBA Regression Model\n",
"##Predicts game scores and outcomes through regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Collect Data"
]
},
{
"cell_type": "code",
"executio... | mit |
leewujung/ooi_sonar | notebooks/before201709/Check single EK60 RAW file .ipynb | 1 | 448985 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import glob, os, sys\n",
"import datetime as dt\n",
"from matplotlib.dates import date2num, num2date\n",
"from calendar import monthrange\n",
"import h5py\n",
"import matplotli... | apache-2.0 |
tranlyvu/kaggle | San Francisco Crime Classification/notebook/Untitled.ipynb | 2 | 20480 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Author : [Vu Tran](https://github.com/tranlyvu). Other info is on [github](https://github.com/tranlyvu/kaggle/tree/master/San%20Francisco%20Crime%20Classification)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"sourc... | apache-2.0 |
zzsza/Datascience_School | 03. 파이썬 프로그래밍/03. Python 시작하기.ipynb | 1 | 90350 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "5c453f03661344b9a4f1172ec5b37f85"
},
"source": [
"# Python 시작하기"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "f5aa5408d6d04a57aea07bf0c08c4db6"
},
"source": [
"이 노트북은 처음으로 P... | mit |
simkovic/simkovic.github.io | _ipynb/No Way Anova - The Challenge.ipynb | 1 | 3644 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There has been lot of discussion lately about the problems hampering statistical analysis (significance, $p$-values, replicability, $p$-hacking) and scientific communication (publication system, disfunctional incentives) and their pote... | mit |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/ml_lasso_rf_grid_search_correction.ipynb | 1 | 192882 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hyperparam\u00e8tres, LassoRandomForestRregressor et grid_search (correction)\n",
"\n",
"Le notebook explore l'optimisation des hyper parama\u00e8tres du mod\u00e8le [LassoRandomForestRegressor](http:/... | mit |
michaelaye/iuvs | notebooks/2015-09-10 Interpolating HK temps.ipynb | 1 | 375500 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/klay6683/miniconda3/lib/python3.4/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPytho... | isc |
bradleypallen/keras-quora-question-pairs | quora-question-pairs-data-prep.ipynb | 1 | 8749 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Quora question pairs: data preparation"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Import packag... | mit |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/.ipynb_checkpoints/2 - Numpy Exercises-checkpoint.ipynb | 1 | 17908 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
"___\n",
"<center>*Copyright Pierian Data 2017*</center>\n",
"<cent... | apache-2.0 |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_topo_compare_conditions.ipynb | 1 | 3781 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Compare evoked res... | bsd-3-clause |
mnschmit/LMU-Syntax-nat-rlicher-Sprachen | 09-notebook-solution.ipynb | 1 | 196833 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Übungsblatt 9\n",
"## Präsenzaufgaben\n",
"### Aufgabe 1 Unifikation I"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"sour... | apache-2.0 |
tensorflow/docs-l10n | site/ja/guide/tf_numpy.ipynb | 1 | 39158 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ZjN_IJ8mhJ-4"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"i... | apache-2.0 |
ekergy/jupyter_notebooks | curso/6-ML.ipynb | 1 | 573892 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning #\n",
"\n",
"**Lo que ha sido siempre la Estadística de toda la vida, que durante los 80 se llamó Inteligencia Artificial**\n",
"\n",
"* Una serie de técnicas de análisis y manipulación de datos que s... | gpl-3.0 |
Rauf-Kurbanov/au_dl_course | seminar_1/homework_task1.ipynb | 1 | 7457 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hint: Use dtype... | gpl-3.0 |
egentry/lamat-2016-solutions | day5/orbits.ipynb | 1 | 128920 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/egentry/anaconda/lib/python3.4/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.k... | mit |
WendyMawanli/liupengyuan.github.io | chapter2/homework/computer/4-12/201611680777(4.12).ipynb | 27 | 2128 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n=int(input('请输入整数个数'))\n",
"i=0\n",
"total=0\n",
"for i in range(n):\n",
" x=int(input())\n",
" total=total+x\n",
" i+=1\n",
"pr... | mit |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_interpolate_bad_channels.ipynb | 1 | 2308 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n====================... | bsd-3-clause |
rickiepark/tfk-notebooks | hackers-guide-to-neural-networks/sigmoid_graph.ipynb | 1 | 24690 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: MacOSX\n",
"Populating the interactive namespace from numpy and matplotlib\n"
... | mit |
folivetti/BIGDATA | Spark/Lab04.ipynb | 2 | 13834 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"# **Lab 5b - k-Means para Quantização de Atributos**\n",
"\n",
"#### Os algoritmos de agrupamento de dados, além de serem utilizados em análise explorató... | mit |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/.ipynb_checkpoints/histograms.ipynb-checkpoint.ipynb | 1 | 9324 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### New to Plotly?\n",
"Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).\n",
"... | mit |
saashimi/code_guild | interactive-coding-challenges/sorting_searching/merge_sort/merge_sort_solution.ipynb | 2 | 5278 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<small><i>This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>"
]
},
{
"cell_type": "markdown"... | mit |
CarlosGrohmann/scripts_papers | garopaba_als_sfm_tls/v2019/garopaba_als_sfm_tls_jupyter_MonteCarlo.ipynb | 1 | 39702 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"##############################################################################\n",
"# Script used in the paper:\n",
"# Dune migration and volume change from airborne LiDAR, terrestrial LiD... | mit |
openp2pdesign/Makers-Inquiry---Analysis | Q003.ipynb | 1 | 60991 | {
"metadata": {
"name": "",
"signature": "sha256:3e707aae2994ecc9936ce827c21ff4309280b2430103c041980d17bbdbaa5f7d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Q003 - Qual \u00e9 la tua ... | gpl-3.0 |
santanche/java2learn | notebooks/pt/c02oo-java/s02lab/s02acoisa/s01resposta/acoisa-01-basico.ipynb | 1 | 8667 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Coisa\n",
"\n",
"Leia os slides abaixo para entender o contexto da aplicação proposta, que envolve a apresentação de um ser virtual chamado `A Coisa`:\n",
"\n",
"[](lab-a-coisa.pdf)"
]
... | gpl-2.0 |
mysticPrince/hcpf | algos/plot_graphs.ipynb | 1 | 106796 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
... | mit |
albahnsen/ML_RiskManagement | exercises/05-creditscoring_cross_validation.ipynb | 1 | 10178 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise 05\n",
"\n",
"## Data preparation and model evaluation exercise with credit scoring\n",
"\n",
"Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or ... | mit |
mattssilva/UW-Machine-Learning-Specialization | Week 2/.ipynb_checkpoints/Predicting house prices-checkpoint.ipynb | 1 | 72731 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fire up graphlab create"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import graphlab\n",
"import numpy as np"
]
},
{
... | mit |
griffinfoster/fundamentals_of_interferometry | 2_Mathematical_Groundwork/2_y_exercises.ipynb | 1 | 35805 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"***\n",
"\n",
"* [Outline](../0_Introduction/0_introduction.ipynb)\n",
"* [Glossary](../0_Introduction/1_glossary.ipynb)\n",
"* [2. Mathematical Groundwork](2_0_introducti... | gpl-2.0 |
emannuelOC/ml-nlp | models/.ipynb_checkpoints/word2vec-imdb-checkpoint.ipynb | 1 | 12034 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# word2vec IMDB data\n",
"\n",
"Training word2vec embeddings on the IMDB database and experimenting.\n",
"\n",
"Referência: Tutorial Kagggle [\"Bag of Words meets Bags of Popcorn\"](https://www.kaggle.com/c/word2vec-nlp... | mit |
Destaneon/python-fundamentals | cheat-sheets/Web-Scraping.ipynb | 2 | 7839 | {
"metadata": {
"name": "",
"signature": "sha256:0ceb0aadf19731319000a3ee3b25e11883f365a935421095a95a40259ddd428d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes, we really need to use some data... | apache-2.0 |
GeosoftInc/gxpy | examples/jupyter_notebooks/Tutorials/Coordinate Systems.ipynb | 1 | 12727 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) 2017 [Geosoft Inc.](geosoft.com)\n",
"\n",
"https://github.com/GeosoftInc/gxpy\n",
"\n",
"[BSD 2-clause License](https://github.com/GeosoftInc/gxpy/blob/master/LICENSE)"
]
},
{
"cell_type": "mark... | bsd-2-clause |
Almaz-KG/MachineLearning | coursera/intro-ml/week-4/practice/w4-practice-01--salary-regression.ipynb | 1 | 22737 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.feature_extraction import DictVectorizer\n",
"from sklearn.linear_model import Ri... | apache-2.0 |
abevieiramota/data-science-cookbook | 2017/04-knn-exercicio/knn_bruno_mourao.ipynb | 2 | 59335 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import datasets\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import collections"
]
},
{
... | mit |
dotsdl/msmbuilder | examples/implied-timescales.ipynb | 12 | 2897 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as pp\n",
"from msmbuilder.example_datasets import fetch_alanine_dipeptide\n",
... | lgpl-2.1 |
phoebe-project/phoebe2-docs | 2.2/examples/legacy_spots.ipynb | 1 | 47086 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing Spots in PHOEBE 2 vs PHOEBE Legacy\n",
"============================\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's ... | gpl-3.0 |
INM-6/Python-Module-of-the-Week | session20_NEST/jupyter_notebooks/1_first_steps.ipynb | 1 | 10583 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# PyNEST - First Steps"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"**Modeling networks of... | mit |
pdwyys20/deep-learning | UdacityNotes/3_CNN/cnn/ConvLayer.ipynb | 1 | 2529 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outpu... | mit |
junkoda/mockgallib | notebook/growth.ipynb | 1 | 13629 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Growth module\n",
"\n",
"Provides linear growth factor $D(a)$.\n",
"\n",
"Uses `omega_m` in the `cosmology` module; set it in advance, e.g, `mock.cosmology_set(0.31)`.\n",
"\n",
"Function | Result\n",
"--... | gpl-3.0 |
flaviostutz/datascience-snippets | kaggle-lung-cancer-approach2/03-nodule-segmentation-3d-train.ipynb | 1 | 12210 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Train 3d nodule detector with LUNA16 dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editabl... | mit |
mne-tools/mne-tools.github.io | 0.23/_downloads/61268d5dc873438a743241ad21a989fd/decoding_rsa_sgskip.ipynb | 2 | 10001 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Representa... | bsd-3-clause |
RMRanasinghe/KDDCup-2014 | src/SGDGridSearchCV date reduced.ipynb | 1 | 8275 | {
"metadata": {
"name": "SGDGridSearchCV date reduced"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.linear_model import SGDClassifi... | gpl-2.0 |
agile-geoscience/striplog | docs/tutorial/12_Calculate_sand_proportion.ipynb | 1 | 72277 | {"cells": [{"cell_type": "markdown", "id": "swiss-stomach", "metadata": {}, "source": ["# Calculate sand proportion\n", "\n", "We'd like to compute a running-window sand log, given some striplog.\n", "\n", "These are some sand beds:"]}, {"cell_type": "code", "execution_count": 7, "id": "fatty-blackberry", "metadata": {... | apache-2.0 |
buzmakov/tomography_scripts | tomo/yaivan/rc_test.ipynb | 1 | 45599 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
... | mit |
lesonkorenac/dataquest-projects | 2. Data Analysis and Visualization/Analyzing Thanksgiving Dinner/Thanksgiving survey.ipynb | 1 | 125923 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_... | mit |
MD2Korg/CerebralCortex | jupyter_demo/mprov_example.ipynb | 1 | 6751 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import required libs"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" w... | bsd-2-clause |
niazangels/CADL | session-3/session-3.ipynb | 1 | 9093578 | null | apache-2.0 |
JackDi/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | 1 | 35276 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Optimization Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"me... | mit |
mne-tools/mne-tools.github.io | 0.18/_downloads/583215f6772d7b8b68be563fd3c069e8/plot_decimate_head_surface.ipynb | 1 | 1926 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Decimating s... | bsd-3-clause |
danxhuber/isoclassify | examples/grid.ipynb | 1 | 848581 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# NB: the recommended way to run isoclassify is through the command-line interface (CLI)\n",
"# using examples such as given in examples/examples.csv (see README)\n",
"# below is an exampl... | mit |
GoogleCloudPlatform/asl-ml-immersion | notebooks/recommendation_systems/solutions/3_als_bqml_hybrid.ipynb | 1 | 17664 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hybrid Recommendations with the Movie Lens Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Note:__ It is recommended that you complete the companion __als_bqml.ipynb__ notebook before conti... | apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.