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
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Weenkus/Machine-Learning-University-of-Washington | Regression/assignments/Multiple Linear Regression Programming Assignment 1.ipynb | 1 | 52868 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialise the libs"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pa\n",
"import m... | mit |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb | 1 | 188623 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "knOigRU1UJ9Y"
},
"source": [
"# Estimating Mandatory Tour Frequency\n",
"\n",
"This notebook illustrates how to re-estimate a single model component for ActivitySim. This process \n",
"includes run... | bsd-3-clause |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/managed_notebooks/pricing_optimization/pricing-optimization.ipynb | 1 | 27386 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "d1cc1c1fa076"
},
"source": [
"# Pricing Optimization \n",
"## Table of contents\n",
"* [Overview](#section-1)\n",
"* [Dataset](#section-2)\n",
"* [Objective](#section-3)\n",
... | apache-2.0 |
tschinz/iPython_Workspace | 01_Mine/MachineLearning/tensorflow-examples_nb/2_BasicModels/word2vec.ipynb | 3 | 34258 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Word2Vec (Word Embedding)\n",
"\n",
"Implement Word2Vec algorithm to compute vector representations of words.\n",
"This example is using a small chunk of Wikipedia articles to train from.\n",
"\n",
"More info: [Mi... | gpl-2.0 |
TeamLab/lab_study_group | 2017/coursera/code/0120/RNN_Basic.ipynb | 1 | 9703 | {
"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 |
slundberg/shap | notebooks/image_examples/image_classification/Multi-input Gradient Explainer MNIST Example.ipynb | 1 | 262687 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-input Gradient Explainer MNIST Example\n",
"\n",
"Here we demonstrate how to use GradientExplainer when you have multiple inputs to your Keras/TensorFlow model. To keep things simple but also mildly interesting we feed ... | mit |
albi3ro/M4 | Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb | 1 | 347860 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monte Carlo Markov Chain\n",
"\n",
"## Christina Lee\n",
"\n",
"## Category: Numerics\n",
"\n",
"### Monte Carlo Physics Series\n",
"* [Monte Carlo: Calculation of Pi](../Numerics_Prog/Monte-Carlo-Pi.ipynb... | mit |
odelab/odes-plot | Apps/mpl2JSON/mpl2JSON/test2.ipynb | 1 | 2283 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import numpy as np\n",
"from itertools import product\n",
"\n",
"d... | mit |
austinjalexander/sandbox | python/tensorflow/TensorflowCore.ipynb | 2 | 7624 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
... | mit |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/canesm5/atmoschem.ipynb | 1 | 102063 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmoschem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CCCMA \n",
"**Source ID**: CANESM5 \n",
"**To... | gpl-3.0 |
kubeflow/pipelines | components/gcp/dataflow/launch_template/sample.ipynb | 1 | 10020 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Name\n",
"Data preparation by using a template to submit a job to Cloud Dataflow\n",
"\n",
"# Labels\n",
"GCP, Cloud Dataflow, Kubeflow, Pipeline\n",
"\n",
"# Summary\n",
"A Kubeflow Pipeline component to ... | apache-2.0 |
chichilalescu/pyNT | examples/project2.ipynb | 1 | 863202 | {
"metadata": {
"name": "",
"signature": "sha256:691184e201a57c91f214adf3a65a466973b04d399fb9b332dbf6e1bb8fea7d2c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import sympy ... | gpl-3.0 |
eric-svds/flask-with-docker | app/.ipynb_checkpoints/my_notebook-checkpoint.ipynb | 1 | 78477 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sample PCA analysis with Iris dataset\n",
"\n",
"The following are required for this notebook:\n",
"- pip install matplotlib\n",
"- pip install scikit-learn\n",
"\n",
"This notebook plots (and pickles) the Iri... | gpl-2.0 |
bollwyvl/watermark | docs/watermark.ipynb | 2 | 8836 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://sebastianraschka.com) \n",
"\n",
"<hr>\n",
"I would be happy to hear your comments and suggestions. \n",
"Please feel free to drop me a note via\n",
"[twitter](https://twitter.com/rasbt)... | gpl-3.0 |
sbenthall/bigbang | examples/obsolete_notebooks/SummerSchoolCompareWordRankings.ipynb | 1 | 16766 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from bigbang.archive import Archive\n",
"from bigbang.archive import load as load_archive\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_t... | agpl-3.0 |
bsamseth/project-euler | 067/67.ipynb | 1 | 2759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Maximum path sum II\n",
"\n",
"By starting at the top of the triangle below and moving to adjacent numbers on the row below, the maximum total from top to bottom is 23.\n",
"\n",
"3\n",
"\n",
"7 4 \n",
"\... | mit |
antoniomezzacapo/qiskit-tutorial | community/aqua/chemistry/LiH_with_qubit_tapering_and_uccsd.ipynb | 1 | 11326 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import common packages\n",
"from collections import OrderedDict\n",
"import itertools\n",
"import logging\n",
"\n",
"import numpy as np\n",
"import scipy\n",
"\n",
... | apache-2.0 |
datitran/Krimskrams | Kaggle/Mercedes-Benz Greener Manufacturing/naive_modeling_different_seeds.ipynb | 1 | 120210 | {
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime\n",
"import xgboost as xgb\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\... | mit |
blab/antibody-response-pulse | bcell-array/code/.ipynb_checkpoints/VBMG_infection_OAS-Equilibrium-checkpoint.ipynb | 1 | 516408 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antibody Response Pulse\n",
"https://github.com/blab/antibody-response-pulse\n",
"\n",
"### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination\n",
"### Adaptive immun... | gpl-2.0 |
nkmk/python-snippets | notebook/numpy_nan_remove.ipynb | 1 | 4690 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
... | mit |
hoerldavid/nis-automation | simple_overview.ipynb | 1 | 4623 | {
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"###################\n",
"# set up the environment, nis, and image saving path\n",
"#####################\n",
"import os\n",
"from nis_util import *\n",
... | mit |
ondrolexa/sg2 | 14_Simultaneous_deformation.ipynb | 1 | 153287 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simultaneous deformation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive n... | mit |
QuantStack/quantstack-talks | 2019-05-22-pydata-frankfurt/notebooks/bqplot.ipynb | 5 | 70092 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bqplot https://github.com/bloomberg/bqplot\n",
"\n",
"## A Jupyter - d3.js bridge\n",
"\n",
"bqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.\n",
"\n",
"- Ap... | bsd-3-clause |
google/skywater-pdk-libs-sky130_bag3_pr | workspace_setup/tutorial_files/3_analogbase.ipynb | 1 | 38666 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AnalogBase\n",
"In this module, you will learn the basics of `AnalogBase`, and how to design a source-follower layout generator using AnalogBase.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
... | apache-2.0 |
IST256/learn-python | content/lessons/11-WebAPIs/HW-WebAPIs.ipynb | 1 | 6275 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework: TLDL: The Gist of a Song\n",
"\n",
"## The Problem\n",
"\n",
"TLDL: Too Long didn't listen. In this assignment you are tasked with producing the gist of any song through its lyrics. The parts you need for th... | mit |
jbgalvanize/jbgalvanize.github.io | posts/Notation.ipynb | 1 | 6623 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Notation in Statistics\n",
"\n",
"Understanding mathematics and statistics when you are new to an idea or topic is difficult enough, but we (yes, myself included) need to be careful as mathematicians/statisticians to introd... | mit |
K3D-tools/K3D-jupyter | examples/mesh_custom.ipynb | 1 | 2673 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import k3d\n",
"import numpy as np\n",
"\n",
"N = 100\n",
"\n",
"theta = np.linspace(0, 2.0 * np.pi, N)\n",
"phi = np.linspace(0, 2.0 * np.pi, N)\n",
"theta, phi = n... | mit |
kubernetes-client/python | examples/notebooks/intro_notebook.ipynb | 1 | 8225 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Managing kubernetes objects using common resource operations with the python client\n",
"----------------------------------------------------------------------------------------------... | apache-2.0 |
chongxi/spiketag | notebooks/weighted_feature.ipynb | 1 | 158788 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-06-29T12:50:47.110000",
"start_time": "2016-06-29T12:50:47.042000"
},
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload"
]
},
{
"cell_ty... | bsd-3-clause |
jasonost/clinicaltrials | nlp/MTIextractPrep.ipynb | 1 | 2532 | {
"metadata": {
"name": "",
"signature": "sha256:f150b3bf6ab58e8c2ff76126d6fe2860e164b58f5a45f7f4dd0858b437dd926a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import codecs, string, random, math, cPickl... | mit |
Ryan-J-Smith/income-explorer | notebooks/MainFile-Tracts.ipynb | 1 | 15397 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Main File - Tract Level\n",
"\n",
"This file is used for getting and acquiring census data at the tract level."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
... | mit |
tzjin/tf_chatbot | reddit_proc/DataProc.ipynb | 1 | 17304 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sqlite3 as sql\n",
"import pandas as pd\n",
"import codecs, re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collap... | mit |
kyleam/seaborn | doc/tutorial/regression.ipynb | 2 | 18992 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _regression_tutorial:\n",
"\n",
".. currentmodule:: seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing linear relationships"
]
},
{
"cell_type": "raw",
"metadata":... | bsd-3-clause |
tpin3694/tpin3694.github.io | statistics/t-tests.ipynb | 1 | 4712 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: T-Tests \n",
"Slug: t-tests \n",
"Summary: T-tests in Python. \n",
"Date: 2016-02-08 12:00 \n",
"Category: Statistics \n",
"Tags: Basics\n",
"Authors: Chris Albon "
]
},
{
"cell_type": "... | mit |
QuantumTechDevStudio/RUDNEVGAUSS | invariance_testing/testing.ipynb | 2 | 2013 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `n... | gpl-3.0 |
zimmermant/vpython_optics_bench | optics_bench_test.ipynb | 1 | 32579 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from vpython import *\n",
"\n",
"#This iteration projects rays from a sphere projecting to the surface of a larger spheroid.\n",
"\n",
"spikeball = sph... | gpl-3.0 |
yyl/data-mining-repo | hw2.ipynb | 1 | 3832 | {
"metadata": {
"name": "",
"signature": "sha256:3d6753004134b9ca5debc5342da747912c824af07a15837bf20227fa3e0b53ae"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"import pandas as pd\... | gpl-2.0 |
fedor1113/LineCodes | Decoder.ipynb | 1 | 29315 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decode line codes in png graphs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assumptions (format):\n",
"* The clock is given and it is a red line on the top.\n",
"* The signal line is b... | mit |
ituethoslab/navcom-2017 | Intro to git and GitHub.ipynb | 1 | 1904 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro to *git* and GitHub\n",
"\n",
"*git* is a version control system, especially for software code, and other things too such as data.\n",
"\n",
"GitHub is an online service, which provides *git* service. GitHub has... | gpl-3.0 |
kkwteh/cdips_hpi_forecast | ZHVI_Exploration.ipynb | 1 | 248103 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using environment in /Users/emunsing/Documents/Coding/github/cdips_hpi_forecast/env\n",
"Python version 3.5.2 (default, Oct 31 2016... | mit |
gis4dis/poster | jupyter-notebooks/AD Experimentation.ipynb | 1 | 23402 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../src/')\n",
"sys.path.insert(0, '../')\n",
"\n",
"import django\n",
"django.setup()"
]
},
{
"cell_type": "code",
"execution_c... | bsd-3-clause |
NeuroDataDesign/seelviz | Jupyter/.ipynb_checkpoints/Python Notebook Example-checkpoint.ipynb | 1 | 2771 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"os.chdir('/Users/Tony/Documents/Git Folder/seelviz/Jupyter/DownsampleGraphML')\n",
"\n",
"from argparse import ArgumentParser\n",
"from col... | apache-2.0 |
jni/notebooks | python-bioformats test run.ipynb | 1 | 77849 | {
"metadata": {
"name": "",
"signature": "sha256:11eb3cb1b5aab1e64af410417f84434f836ebb76370e4a49a5a33c794f344b59"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recently, the CellProfiler team at the Bro... | bsd-3-clause |
robertodias/study | python/movie_recom/model_2.ipynb | 3 | 26840 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"ratings_list = [i.strip().split(\"::\") for i in open('ml-1m/ratings.dat', 'r').readlines()]\n",
"users_list = [i.strip().spl... | mit |
brttstl/taco-mb25 | p1/.ipynb_checkpoints/p1-checkpoint.ipynb | 1 | 10479 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import timeit"
]
},
{
"cell_type": "cod... | apache-2.0 |
JasonTam/ndsb2015 | CNN_Features.ipynb | 2 | 17839 | {
"metadata": {
"name": "",
"signature": "sha256:64bdf6f877b93773016482a8d42438231ccf9cd01c8ee878fddab689997cf9bb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import h5py\n... | mit |
Phelimb/cbg | example-scripts/search.ipynb | 1 | 9004 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def search(seq, threshold=1):\n",
" url=\"http://api.bigsiseq.com/search?threshold=%f&seq=%s\" % (float(threshold),seq)\n",
" results = requests.get(url).js... | mit |
renecnielsen/twitter-diy | ipynb/02 - Descriptive Statistics-RCN-Copy1.ipynb | 2 | 1544735 | null | mit |
ioam/holoviews | doc/Homepage.ipynb | 1 | 6660 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible. Instead of specifying every step for each plot, HoloVie... | bsd-3-clause |
jay-johnson/sci-pype | examples/example-core-demo.ipynb | 1 | 2521 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python Core Demo\n",
"\n",
"How to use the python core from a Jupyter notebook. It also shows how to debug the JSON application configs which are used to connect to external database(s) and redis server(s).\n"
]
},
{
... | apache-2.0 |
blepfo/drp_fall_2016 | Simple_NN.ipynb | 1 | 65374 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np # For matrix/vector operations\n",
"import gzip ... | gpl-3.0 |
ternaus/kaggle_wallmart | Fill with regressor.ipynb | 1 | 203341 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data has many nan values. Normally people will fill it with most common value for a classification variable or mean of some subclass for a continuous variable, but I will try to fill missed values using machine learning, treating them ... | mit |
jorisvandenbossche/DS-python-data-analysis | notebooks/case4_air_quality_processing.ipynb | 1 | 18261 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p><font size=\"6\"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>\n",
"\n",
"> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanho... | bsd-3-clause |
nilutz/Connectfour | notebooks/connectfour-withMinmax.ipynb | 1 | 20160 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cl... | mit |
zipeiyang/liupengyuan.github.io | chapter2/homework/computer/middle/201611580308.ipynb | 16 | 3674 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * \n",
" * * \n",
" * * * \n",
" * * * * \n",
" * * * * * \n"
]
}
... | mit |
hfoffani/deep-learning | face_generation/dlnd_face_generation.ipynb | 1 | 1295062 | null | mit |
Molns/pyurdme | examples/yeast_polarization/2D_periodic_test.ipynb | 5 | 354880 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pyurdme\n",
"from matplotlib import cm as CM\n",
"from IPython.display import display, clear_output\n",
"imp... | gpl-3.0 |
LFPy/LFPy | examples/LFPy-example-02.ipynb | 2 | 76490 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 2: Extracellular response of synaptic input\n",
"This is an example of **``LFPy``... | gpl-3.0 |
nkmk/python-snippets | notebook/urllib_parse_query_string.ipynb | 1 | 13121 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib.parse"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url... | mit |
amirziai/learning | reinforcement-learning/Options.ipynb | 1 | 2163 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Standard RL assumes flat states and action spaces with no hierarchy\n",
"* Represent sequences of **primitive actions** to achieve subgoa... | mit |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/essentials/greedy-algorithm-examples.ipynb | 2 | 4937 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
... | gpl-3.0 |
LorenzoBi/courses | TSAADS/tutorial 9/.ipynb_checkpoints/Untitled-checkpoint.ipynb | 1 | 108472 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import fsolve\n",
"%matplotlib inline\n",
"\n",
"def sigmoid(x):\n",... | mit |
teoguso/sol_1116 | cumulant-to-pdf.ipynb | 1 | 7125 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Best report ever\n",
"\n",
"Everything you see here is either markdown, LaTex, Python or BASH."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow... | mit |
ljinke/MachineLearning | exercises/import and package.ipynb | 1 | 4216 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['',\n",
" '/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python27.zip',\n",
" '/usr/local/Ce... | mit |
NLP-Deeplearning-Club/Classic-ML-Methods-Algo | ipynbs/appendix/ensemble/voting.ipynb | 1 | 5057 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 投票\n",
"\n",
"投票是最简单最基本的集成方式,核心思想也很朴素:大家伙投票决定结果.\n",
"\n",
"其原理是结合了多个不同的机器学习分类器,并且采用多数表决(硬投票)或者平均预测概率(软投票)的方式来预测分类标签.这样的分类器可以用于一组同样表现良好的模型,以便平衡它们各自的弱点.\n",
"\n",
"## ***使用sklearn做投票***\n",
"\n",
"sklea... | mit |
tensorflow/docs-l10n | site/en-snapshot/addons/tutorials/optimizers_cyclicallearningrate.ipynb | 2 | 14858 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Tce3stUlHN0L"
},
"source": [
"##### Copyright 2021 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"i... | apache-2.0 |
sudhanshuptl/Machine-Learning | Kaggle/Titanic/.ipynb_checkpoints/Analysing Data-checkpoint.ipynb | 1 | 237043 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysing Data"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib... | gpl-2.0 |
mne-tools/mne-tools.github.io | dev/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb | 1 | 11850 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Non-parame... | bsd-3-clause |
simonelanucara/script | sentinelsat.ipynb | 1 | 110449 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"from shapely.geometry import MultiPolygon, Polygon\n",
"import sentinelsat\n",
"from sentinelsat import SentinelAPI\n",
"import folium\n",
"import ... | gpl-3.0 |
tarmstrong/nbdiff | scripts/example-notebooks/diff/5/after.ipynb | 1 | 180773 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How Long Does It Take To Review an IPython Pull Request?"
]
},
{
"cell_type": "markdown"
"metadata": {},
... | mit |
moizumi99/CVBookExercise | Chapter-9/CV Book Chapter 9 Exercise 2.ipynb | 1 | 42563 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from PIL import Image\n",
"from numpy import *\n",
"from pylab import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"coll... | unlicense |
jGaboardi/Facility_Location | CPLEX_v_Gurobi/Cplex_v_Gurobi_pMedian_constant_matrix.ipynb | 2 | 1247933 | null | lgpl-3.0 |
boffi/boffi.github.io | dati_2015/ha04/03_Rayleigh.ipynb | 1 | 31401 | {
"metadata": {
"name": "",
"signature": "sha256:f9f1154d7009d1308dad26248b6ea0022790e92c78d209d8e5b426a6578c5ea4"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import *\n",
"init_printin... | mit |
chengsoonong/didbits | Estimation/SVM_kernels.ipynb | 1 | 161707 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine with Kernels\n",
"\n",
"This follows the example from ```scikit-learn``` ([here](http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html) and [here](http://scikit-learn.org/stable/auto_examples/... | apache-2.0 |
BinPy/BinPy | BinPy/examples/notebook/ic/Series_7400/IC7440.ipynb | 5 | 9466 | {
"metadata": {
"name": "",
"signature": "sha256:3c782c6bcbb2318dc454ffa40838d60827668d71564deef73fcdb6eb2d96cc78"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Usage of IC 7400"
]
... | bsd-3-clause |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/lowess.ipynb | 4 | 148041 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LOWESS Smoother\n",
"\n",
"This notebook introduces the LOWESS smoother in the `nonparametric` package. LOWESS performs weighted local linear fits.\n",
"\n",
"We generated some non-linear data and perform a LOWESS fit... | bsd-3-clause |
mohanprasath/Course-Work | pyspark/Untitled1.ipynb | 1 | 3029 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-02-06T08:50:52.105424Z",
"start_time": "2019-02-06T08:50:32.081072Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Intitializing Scala interpreter ..."
... | gpl-3.0 |
jasonding1354/pyDataScienceToolkits_Base | Scikit-learn/.ipynb_checkpoints/(3)linear_regression-checkpoint.ipynb | 2 | 145651 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 内容概要\n",
"- 如何使用pandas读入数据\n",
"- 如何使用seaborn进行数据的可视化\n",
"- scikit-learn的线性回归模型和使用方法\n",
"- 线性回归模型的评估测度\n",
"- 特征选择的方法"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"... | mit |
JeffAbrahamson/MLWeek | practicum/06_ANN/RBM.ipynb | 1 | 59497 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Restricted Boltzman Machines\n",
"\n",
"Un RBM est un algorithme non-linéaire et non-supervisé qui est basé sur un modèle probabiliste. Son but est d'apprendre des critères. Souvent, on suit avec un classifieur comme SVM ou... | gpl-3.0 |
atlury/deep-opencl | cs480/23 Linear Dimensionality Reduction.ipynb | 1 | 256372 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\newcommand{\\xv}{\\mathbf{x}}\n",
"\\newcommand{\\Xv}{\\mathbf{X}}\n",
"\\newcommand{\\yv}{\\mathbf{y}}\n",
"\\newcommand{\\Yv}{\\mathbf{Y}}\n",
"\\newcommand{\\zv}{\\mathbf{z}}\n",
"\\newcommand{\\av}{\\mathbf{a... | lgpl-3.0 |
Sitin/neuromatriarchy | neuromatriarchy.ipynb | 1 | 1420602 | null | artistic-2.0 |
astarostin/ALTA-2016-Challenge | alta2016.ipynb | 1 | 27886 | {
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
... | gpl-3.0 |
iurilarosa/thesis | codici/.ipynb_checkpoints/Verifiche HWI-checkpoint.ipynb | 1 | 578 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import scipi"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"n... | gpl-3.0 |
dali-ml/dali-cython | notebooks/LSTM.ipynb | 2 | 22576 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],... | mit |
mne-tools/mne-tools.github.io | 0.23/_downloads/afbdbca3a62dcdd6fc01894cd11b97b4/20_reading_eeg_data.ipynb | 1 | 9475 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Importing ... | bsd-3-clause |
fcollonval/coursera_data_visualization | WritingAboutData.ipynb | 1 | 5340 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression Modeling in Practice\n",
"# Assignment: Writing about your data\n",
"\n",
"Here is my first assignment of the [Regression Modeling in Practice online course](https://www.coursera.org/learn/regression-modeling-p... | mit |
jdfekete/progressivis | notebooks/PsBoardLocalFiles.ipynb | 1 | 3610 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from progressivis_nb_widgets.nbwidgets import PsBoard, Scatterplot\n",
"import pandas as pd\n",
"from progressivis.core import Scheduler, Every\n",
"from progressivis.table import T... | bsd-2-clause |
ES-DOC/esdoc-jupyterhub | notebooks/uhh/cmip6/models/sandbox-2/ocnbgchem.ipynb | 1 | 79366 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocnbgchem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: UHH \n",
"**Source ID**: SANDBOX-2 \n",
"**To... | gpl-3.0 |
luwei0917/awsemmd_script | notebook/linear_regression_sep20_2.ipynb | 1 | 229586 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from datetime import datet... | mit |
ingmarschuster/rkhs_demo | RKHS_in_Machine_learning.ipynb | 1 | 316817 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "151ace28-1df8-4b52-baf0-4f792e91d5ff"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"$\\newcommand{\\Reals}{\\mathbb{R}}\n",
"\\newcommand{\\Nats}{\\mathbb{N}}\n",
"\\newcommand{\\... | gpl-3.0 |
charlesll/Examples | PySolExExample.ipynb | 1 | 96528 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Charles Le Losq\n",
"Friday, 22 May 2015\n",
"Modified the 16 June 2015.\n",
"\n",
"Geophysical Laboratory,\n",
"Carnegie Institution for Science\n",
"\n",
"Example of use of pysolex, the library using the s... | gpl-2.0 |
sunilmallya/dl-twitch-series | E3_finetuning_randall_not_randall.ipynb | 1 | 148805 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a model to detect if Randall is in the image or not!\n",
"\n",
"Randall or Not"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
" \n",
"- Randall : s3://ranman-s... | apache-2.0 |
idekerlab/graph-services | notebooks/DEMO.ipynb | 1 | 10163 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# cxMate Service DEMO\n",
"By Ayato Shimada, Mitsuhiro Eto\n",
"\n",
"This DEMO shows\n",
"1. detect communities using an __igraph's community detection algorithm__\n",
"2. __paint communities (nodes and edges)__ in... | mit |
indranilsinharoy/PyZDDE | Examples/IPNotebooks/01 Notes on ipzCaptureWindow functions.ipynb | 2 | 1048183 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using `ipzCaptureWindow` and `ipzCaptureWindow2` for embedding graphic analysis windows into notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/indr... | mit |
mohanprasath/Course-Work | machine_learning/Machine Learning Concepts - Code and Implementations.ipynb | 1 | 1180208 | null | gpl-3.0 |
goedman/RobGoedmansNotebooks.jl | notebooks/SheehanOlver/12.ipynb | 1 | 8563 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 12: PLU Decomposition\n",
"\n",
"For MATH3976 students: in the assignment, we will be using a bitstype. The following creates a new type of precisely 128 bits, that is a subtype of `AbstractFloat`:"
]
},
{
... | mit |
sgkang/AGU2014MovingDimensionsinEM | examples/Hydro1Dinv_1_realistic.ipynb | 1 | 582413 | {
"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"
]
},
{
"name": "stderr",
... | mit |
ivannz/study_notes | year_14_15/spring_2015/netwrok_analysis/notebooks/labs/struct_sim.ipynb | 1 | 8473 | {
"metadata": {
"name": "",
"signature": "sha256:79e4643379428e41b4ae254d74267decd5a93e7c64fb2f846ab3b8f3465b771a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Structural Similarity"
... | mit |
google/jax-md | notebooks/customizing_potentials_cookbook.ipynb | 1 | 68516 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Custom Potentials.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
... | apache-2.0 |
sophie63/FlyLFM | Notebooks/.ipynb_checkpoints/100106-checkpoint.ipynb | 1 | 3569944 | null | bsd-2-clause |
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