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kit-cel/wt | SC468/LDPC_Optimization_AWGN.ipynb | 2 | 159277 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimization of Degree Distributions on the AWGN\n",
"\n",
"This code is provided as supplementary material of the lecture Channel Coding 2 - Advanced Methods.\n",
"\n",
"This code illustrates\n",
"* Us... | gpl-2.0 |
UWSEDS/LectureNotes | PreFall2018/02-Python-and-Data/Lecture-Python-and-Data.ipynb | 1 | 20527 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" .rendered_html {font-size: 140%;}\n",
" .rendered_html h1, h2 {text-align:center;}\n",
"</style>"
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... | bsd-2-clause |
Upward-Spiral-Science/grelliam | code/classification_simulation.ipynb | 1 | 76488 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulated Classifcation\n",
"1. State assumptions\n",
"2. Formally define classification/regression problem\n",
"3. provide algorithm for solving problem (including choosing hyperparameters as appropriate)\n",
"4. s... | apache-2.0 |
hrayatnia/SciPy | ipython gallery/CS1001.py-master/recitation14.ipynb | 2 | 6936 | {
"metadata": {
"name": "recitation14"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CS1001.py\n",
"\n",
"## Extended Introduction to Computer Science with Python, Tel-Aviv University, Spring... | bsd-3-clause |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment2/FullyConnectedNets.ipynb | 1 | 449922 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fully-Connected Neural Nets\n",
"In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a s... | mit |
renekm/CD-atualizado- | MiniProjeto1/Projeto 1 feito.ipynb | 1 | 525255 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"# PROJETO 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='red'>Rene Martinez</font>\n",
"___"
]
},
{
"cell_type": "markdown",
"metada... | gpl-3.0 |
jurgnjn/biokludge | annot/notebooks/Fig2S3_import_Chen2013.ipynb | 2 | 204459 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:46:36.324807Z",
"start_time": "2018-05-18T15:46:30.244709Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/home... | gpl-2.0 |
Ledoux/ShareYourSystem | Pythonlogy/draft/Object/Readme.ipynb | 1 | 2315 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\nView the Object sources on [Github](https://github.com/Ledoux/ShareYourSystem/tree/master/ShareYourSystem/Objects/Notebooker)\n\n",
"cell_type": "markdown",
"prompt_number... | mit |
callaghanmt/phd-flight-tracker | User_Input.ipynb | 1 | 77306 | {
"metadata": {
"name": "",
"signature": "sha256:fb7a0fb7b64f4c53b5d49e0d0728c76fc21b7867bddf09995c66d1b65abeabcb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math as ma\n",
"from haversine... | mit |
teuben/astr288p | notebooks/orbits-02.ipynb | 1 | 3815 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two Dimensional Galactic Orbits - Part 2\n",
"\n",
"We will be using an open source package, called GalPy, that has been used in research.\n",
"\n",
"The code is available via github: https://github.com/jobovy/galpy\n... | mit |
google/or-tools | examples/notebook/linear_solver/integer_programming_example.ipynb | 1 | 5587 | {
"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 |
ktmud/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | 6 | 23066 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generative Adversarial Network\n",
"\n",
"In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n",
"\n",
... | mit |
Hugovdberg/timml | notebooks/timml_notebook1_sol.ipynb | 1 | 365402 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TimML Notebook 1\n",
"## A well in uniform flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider a well in the middle aquifer of a three aquifer system. Aquifer properties are given in Ta... | mit |
StevenCHowell/code_sas_modeling | notebooks/Standard Deviation versus Standard Error.ipynb | 1 | 29032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "81c0606f-dc29-4fae-895c-f5f4c6d8d58d"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Standard Deviation versus Standard Error\n",
"## Steven C. Howell\n",
"### 5 January 2017"
... | gpl-3.0 |
dsevilla/bdge | mongo/sesion4.ipynb | 1 | 292595 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NoSQL (MongoDB) (sesión 4)"
]
},
{
"attachments": {
"MongoDB-Logo-5c3a7405a85675366beb3a5ec4c032348c390b3f142f5e6dddf1d78e2df5cb5c.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABtwAAAHdCAYAAACexN3TAAAACXBIWXMAAC4jA... | mit |
nkmk/python-snippets | notebook/opencv_drawing.ipynb | 1 | 5147 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
... | mit |
PubuduSaneth/genome4d | looplist2bed.ipynb | 1 | 12518 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert looplist file to different file formats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Looplist file format](http://dx.doi.org/10.1016/j.cell.2014.11.021) <br>\n",
"\n",
"1. chro... | gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_online_export_edge.ipynb | 1 | 37331 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"#... | apache-2.0 |
QuantumTechDevStudio/RUDNEVGAUSS | archive/Rodion/Gauss/NumPy/old/gaussianCompositionGradientDescent_Minibatch.ipynb | 1 | 59040 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x5ab3e90>]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_resul... | gpl-3.0 |
AtmaMani/pyChakras | python_crash_course/python_cheat_sheet_2.ipynb | 1 | 15703 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "6eb6ae54-534b-4581-9172-4e35fd5b7e94"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Python cheat sheet - iterations\n",
"\n",
"**Table of contents**\n",
" - [Functions](#Fun... | mit |
tbarrongh/cosc-learning-labs | src/notebook/Menu.ipynb | 2 | 1280 | {
"metadata": {
"name": "",
"signature": "sha256:aaa9a3e75d6a07b02f40f4b5f3732fe8b64422386d162dc87bf7030e5f0fc551"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"COSC Learning Lab: Menu"
... | apache-2.0 |
datahac/jup | UX_analytics-D.ipynb | 1 | 62595 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code"... | apache-2.0 |
the-deep-learners/TensorFlow-LiveLessons | notebooks/live_training/natural_language_preprocessing_best_practices_LT.ipynb | 1 | 14243 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Best Practices for Preprocessing Natural Language Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we improve the quality of our Project Gutenberg word vectors by adopting best-... | mit |
paoloRais/lightfm | examples/quickstart/quickstart.ipynb | 1 | 8044 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quickstart\n",
"In this example, we'll build an implicit feedback recommender using the Movielens 100k dataset (http://grouplens.org/datasets/movielens/100k/).\n",
"\n",
"The code behind this example is available as a [Ju... | apache-2.0 |
jo-tez/aima-python | csp.ipynb | 1 | 231531 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CONSTRAINT SATISFACTION PROBLEMS\n",
"\n",
"This IPy notebook acts as supporting material for topics covered in **Chapter 6 Constraint Satisfaction Problems** of the book* Artificial Intelligence: A Modern Approach*. We make ... | mit |
ChristosChristofidis/h2o-3 | h2o-py/demos/imputation.ipynb | 2 | 48105 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import h2o"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text... | apache-2.0 |
aqibsaeed/Human-Activity-Recognition-using-CNN | Activity Detection.ipynb | 1 | 8534 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import tensorflow as tf\n",
"\n",
... | apache-2.0 |
alsrgv/tensorflow | tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb | 2 | 7695 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "TFE Workshop: control flow",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
... | apache-2.0 |
KristianJensen/cameo | examples/visbio_new_interact_feature.ipynb | 1 | 2920248 | null | apache-2.0 |
AstroHackWeek/AstroHackWeek2016 | notebook-tutorial/notebooks/01-Tips-and-tricks.ipynb | 1 | 98605 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Best practices\n",
"\n",
"Let's start with pep8 (https://www.python.org/dev/peps/pep-0008/)\n",
"\n",
"> Imports should be grouped in the following order:\n",
"\n",
"> - standard library imports\n",
"> - r... | mit |
phobson/bokeh | examples/howto/notebook_comms/Basic Usage.ipynb | 1 | 4415 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bokeh.io import push_notebook, show, output_notebook\n",
"from bokeh.layouts import row\n",
"from bokeh.plotting import figure\n",
"output_notebook()... | bsd-3-clause |
crs4/omero.biobank | notebooks/biobank-basics-10.ipynb | 1 | 14122 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Micro arrays datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
... | gpl-2.0 |
bird-house/twitcher | notebooks/twitcher-client.ipynb | 2 | 5667 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Twitcher Client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# disable ssl warnings\n",
"import urllib3\n",
"urllib3.disable_warnings()"
... | apache-2.0 |
BrownDwarf/ApJdataFrames | notebooks/Hernandez2014.ipynb | 1 | 11741 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`ApJdataFrames` Hernandez2014\n",
"---\n",
"`Title`: A SPECTROSCOPIC CENSUS IN YOUNG STELLAR REGIONS: THE σ ORIONIS CLUSTER \n",
"`Authors`: Jesus Hernandez, Nuria Calvet, Alice Perez, Cesar Briceno, Lorenzo Olguin, Maria ... | mit |
alephcero/adsProject | olds/dataMunging.ipynb | 1 | 36424 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"... | gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_online.ipynb | 1 | 35843 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"#... | apache-2.0 |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/proxy_download_susp_dyndns.ipynb | 1 | 6762 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download from Suspicious Dyndns Hosts\n",
"Detects download of certain file types from hosts with dynamic DNS names (selected list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content... | gpl-3.0 |
musketeer191/job_analytics | .ipynb_checkpoints/jobtitle_skill-checkpoint.ipynb | 1 | 7600 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building JobTitle-Skill matrix\n",
"\n",
"Running LDA on document-skill matrix, where each document is a job post, still does not give good results!!! What is the problem here?\n",
"\n",
"It seems that the job post le... | gpl-3.0 |
afeiguin/comp-phys | 01_03_eqs_of_motion.ipynb | 1 | 2039828 | null | mit |
halexand/NB_Distribution | .ipynb_checkpoints/KL rambling notes on Python-checkpoint.ipynb | 2 | 32618 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this to keep track of useful code bits as I learn Python\n",
"Krista, August 19, 2015"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Shortcut \tAction\n",
"Shift-Enter ... | mit |
endgameinc/youarespecial | BSidesLV -- your model isn't that special -- (1) MLP.ipynb | 1 | 17732 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preliminaries\n",
"\n",
"We're going to build and compare a few malware machine learning models in this series of Jupyter notebooks. Some of them require a GPU. I've used a Titan X GPU for this exercise. If yours isn't as ... | mit |
cmgerber/PLOS_Cloud_Explorer | ipython_notebooks/Batch_data_collection_full.ipynb | 1 | 215256 | {
"metadata": {
"gist_id": "11133500",
"name": "",
"signature": "sha256:43624ba0d4c8e4deab12f858b4b057e06af566efca19ca5aca6bbd97657ec67b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"B... | agpl-3.0 |
gena/paper-osm-2015 | notebooks/TileCatchmentsToGrid.ipynb | 1 | 565841 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generates tiles using a given grid for a set of rasters generated per-catchment"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matpl... | apache-2.0 |
rsignell-usgs/notebook | SOS/NDBC_SOS.ipynb | 2 | 70964 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wind Speed and Gust from NDBC SOS service\n",
"Get CSV data from NDBC SOS service using OWSlib, then read CSV data and plot using Pandas"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collap... | mit |
justincely/lightcurve_pipeline | dev/poisson_hist.ipynb | 1 | 2889 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from lightcurve_pipeline.utils.utils import SETTINGS\n",
"from lightcurve_pipeline.database.database_interface import eng... | bsd-3-clause |
mmaelicke/felis_python1 | felis_python1/lectures/06_Classes.ipynb | 1 | 20135 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the main features in the Python programming language is its **_object oriented_** structure. Thus, beside procedual programming (*script... | mit |
omoju/Fundamentals | CS/Part_4_Graphs_ShortestPath.ipynb | 1 | 3414 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"from IPython.display import HTML\n",
"import IPython.core.display as di # Example: di.display_html('<h3>%s:</h3>' % st... | gpl-3.0 |
philippgrafendorfe/stackedautoencoders | MNIST_SAE.ipynb | 1 | 2602711 | null | mit |
amirziai/learning | spark/Median for RDDs, Datasets, and Dataframes.ipynb | 1 | 14150 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Median for RDDs, Datasets, and Dataframes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting `spark` up and running"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metada... | mit |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/10-Quantopian-Platform/02-Basic-Algorithm-Methods.ipynb | 2 | 14693 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Algorithm Methods\n",
"\n",
"Let's algorithmically test our earlier optimized tech portfolio strategy with Quantopian!\n",
"\n",
"#### THIS CODE ONLY WORKS ON QUANTOPIAN. EACH CELL CORRESPONDS WITH A PART OF THE... | apache-2.0 |
jasonding1354/ScalaFAQ | type_system/phantom_type.ipynb | 1 | 13852 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 虚类型\n",
"我们将定义好的没有任何实例的类型称为虚类型。\n",
"\n",
"虚类型一般作为一个标志而存在，表明我们不会使用该类型的任何实例，它是用来解决设计问题而存在的。\n",
"\n",
"**对于定义必须按照某一特定顺序执行的工作流而言，虚类型作用很大。**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source... | mit |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/pmod/pmod_adc.ipynb | 4 | 15012 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Pmod ADC Reading Waveforms\n",
"\n",
"This demonstration shows how to use the Pmod ADC (AD2). \n",
"\n",
"The Pmod ADC, and an analog signal generator are required for ... | bsd-3-clause |
ivannz/study_notes | year_15_16/fall_2015/game theoretic foundations of ml/labs/SVM-lab.ipynb | 1 | 11833 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Применение машины опорных векторов к выявлению фальшивых купюр"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Подключим необходимые библиотеки."
]
},
{
"cell_type": "code",
"execution_... | mit |
myuuuuun/various | STP/SDE.ipynb | 2 | 2339541 | null | mit |
deeplycloudy/dressanalysis | Dress color analysis.ipynb | 1 | 481646 | {
"metadata": {
"name": "",
"signature": "sha256:e934d7b83a14b0b65f03314b6d2880b9ddc9747752981842cbd0508323e97189"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import display, Image\... | mit |
xpharry/Udacity-DLFoudation | your-first-network/.ipynb_checkpoints/dlnd-your-first-neural-network-checkpoint.ipynb | 1 | 238056 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Your first neural network\n",
"\n",
"In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural networ... | mit |
escientists/cervical-cancer | notebooks/keras attempt.ipynb | 1 | 239115 | {
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions\n",
"from keras.preprocessing import image\n",
"import numpy as np"
]
},
... | apache-2.0 |
ktmud/deep-learning | student-admissions/StudentAdmissions.ipynb | 9 | 11422 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Student Admissions with Neural Networks\n",
"In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:\n",
"- GRE Scores (Test)\n",
"- GPA Scores (Grades)\n",
... | mit |
yubhrraj/PredictAddict | Random_Forest_Model/RF.ipynb | 1 | 99606 | {
"cells": [
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"stud... | mit |
kit-cel/wt | nt1/vorlesung/extra/dsss.ipynb | 2 | 96432 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Content and Objectives"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Show spreading in time and frequency domain\n",
"- BPSk symbols are being pulse-shaped by rectangular w. and... | gpl-2.0 |
spacedrabbit/PythonBootcamp | GUI/Widget Basics & Events.ipynb | 1 | 4819 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
... | mit |
ioam/geoviews | examples/gallery/matplotlib/filled_contours.ipynb | 1 | 1887 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import geoviews as gv\n",
"import geoviews.feature as gf\n",
"import cartopy.crs as ccrs\n",
"\n",
"gv.extension('matplotlib')\n",
"\n",
"gv.... | bsd-3-clause |
Jay-Jay-D/LeanSTP | Jupyter/KitchenSinkQuantBookTemplate.ipynb | 4 | 12193 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Welcome to The QuantConnect Research Page\n",
"#### Refer to this page for documentation https://www.quantconnect.com/docs#Introduction-to-Jupyter\... | apache-2.0 |
ParuninPavel/lenta4_hack | dl_module/Dataset_preprocess.ipynb | 1 | 1424312 | null | mit |
zacwentzell/BIA-660-C-Spring2017 | In-class Lectures/February 02/inclass_lecture_3.ipynb | 1 | 8982 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"... | mit |
johnbachman/emcee | docs/_static/notebooks/quickstart.ipynb | 2 | 28454 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = \"retina\"\n",
... | mit |
airbnb/knowledge-repo | tests/test_posts/one_plus_one.ipynb | 1 | 8516 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"---\n",
"title: \"My bright idea!\"\n",
"authors:\n",
" - resident_innovator\n",
"created_at: 2017-01-01 00:00:00\n",
"updated_at: 2017-01-25 00:00:00\n",
"tags:\n",
" - proofs\n",
" - novel\n",
"tldr: |\... | apache-2.0 |
atitus/PHY3110 | notes/03-conservation-laws/.ipynb_checkpoints/barbell-checkpoint.ipynb | 1 | 6070 | {
"metadata": {
"name": "",
"signature": "sha256:883b5d98b5eb193382fb6e953e2578a443e5f1524493c0f1bad15021d29d4104"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Barbell Angular Momentum"
... | mit |
decisionstats/pythonfordatascience | matplotlib+cars.ipynb | 1 | 28731 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source"... | apache-2.0 |
rssalessio/PythonVRFT | examples/notebook_example_2.ipynb | 1 | 171001 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## VRFT with measurement noise (no instrumental variables)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Copyright (c) [2021] Alessio Russo [alessior@kth.se].... | gpl-3.0 |
scottquiring/Udacity_Deeplearning | reinforcement/Q-learning-cart.ipynb | 1 | 9788836 | null | mit |
sdpython/pyquickhelper | _unittests/ut_helpgen/data/completion_profiling.ipynb | 1 | 1402712 | null | mit |
ekostat/ekostat_calculator | notebooks/.ipynb_checkpoints/get_data_from_sharkweb-checkpoint.ipynb | 1 | 60353 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get data from SHARKweb."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pathlib\n",
"import urllib"
]
},
{
"cell_t... | mit |
scios/fricas_kernel | screenshots/test2.ipynb | 2 | 135994 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n",
"Value = \"Wednesday August 5, 2009 at 19:05:50 \"\n"
]
},
"metadata": {},
"output_type": "display_data"
... | bsd-2-clause |
turi-code/tutorials | dss-2016/churn_prediction/churn-tutorial.ipynb | 2 | 52796 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Forecasting customer churn \n",
"\n",
"Churn prediction is the task of identifying users that are likely to stop using a service, product or website. In this notebook, you will learn how to:\n",
"\n",
"#### Train & co... | apache-2.0 |
FranciscoBraga/AprendendoPython | while/Trabalhando com While no Python.ipynb | 1 | 3738 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
... | apache-2.0 |
canercandan/dim | notebooks/experiments_tsp_problem_with_uniform_operators.ipynb | 1 | 567491 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Test avec des op\u00e9rateurs de permutation uniforme sur le probl\u00e8me du TSP"
]
},
{
"cell_ty... | lgpl-2.1 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_linear_regression_raw.ipynb | 1 | 3411 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n====================... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_info.ipynb | 1 | 8511 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n\nThe :class:`Info <... | bsd-3-clause |
mkudija/Map-Tools | (2)_plot_trips.ipynb | 1 | 206656 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dat... | mit |
arsenovic/galgebra | doc/galgebra_guide.ipynb | 1 | 156568 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is Geometric Algebra?\n",
"==========================\n",
"\n",
"<script type=\"text/x-mathjax-config\">\n",
"MathJax.Hub.Config({TeX: { equationNumbers: { autoNumber: \"AMS\" } }});\n",
"</script>\n",
"\n"... | bsd-3-clause |
computational-class/cjc2016 | code/04.PythonCrawler_selenium.ipynb | 1 | 53032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"***\n",
"***\n",
"# 数据抓取\n",
" > # 使用Selenium操纵浏览器\n",
"\n",
"***\n",
"***\n",
"\n",
"王成军 \n",
"\n",
"wangchengjun@nju.edu.cn\n... | mit |
evanbiederstedt/RRBSfun | genomic_regions_distance/Pairs_Genomic_Regions_12August2016_NormalBCD19pCD27mcell23_44.ipynb | 2 | 538131 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
... | mit |
hall1467/wikidata_usage_tracking | jupyter_notebooks/misalignment/dissonance-201509.ipynb | 2 | 685275 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"library(data.table)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## ... | mit |
jmwerner/TXT2PYNB | Examples/Example_1.ipynb | 1 | 1776 | {
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"language": "python",
"input": "a = 5\nd = 5000\ne = 50928734",
"outputs": [],
... | mit |
pm4py/pm4py-core | notebooks/2_event_data_filtering.ipynb | 1 | 37210 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Event Data Filtering\n",
"*by: Sebastiaan J. van Zelst*"
]
},
{
"cell_type": "markdown",
... | gpl-3.0 |
yigong/AY250 | hw7/.ipynb_checkpoints/test-checkpoint.ipynb | 1 | 154381 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"import pdb\n",
"import numpy a... | mit |
pioneers/topgear | ipython-in-depth/examples/IPython Kernel/Terminal Usage.ipynb | 1 | 6380 | {
"metadata": {
"name": "",
"signature": "sha256:993106eecfd7abe1920e1dbe670c4518189c26e7b29dcc541835f7dcf6fffbb2"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"A few things that work bes... | apache-2.0 |
Knewton/lentil | nb/data_explorations.ipynb | 2 | 29140 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"\n",
"from collections import defaultdict\n",
"import pickle\n",
"import os\n",
"import sys\n",
"\n",
... | apache-2.0 |
aerosara/thesis | notebooks_archive_10112014/Haloize with odelay.ipynb | 1 | 75153 | {
"metadata": {
"name": "",
"signature": "sha256:2a308f0838082efc652326fea7adcc33cfa2864194380a12855957f6945c0302"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"CRTBP Derivatives, Stoppin... | mit |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_04/Begin/Tick Marks.ipynb | 2 | 1692 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Tick Marks, Labels, and Grids</h1>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n... | bsd-3-clause |
jpallas/beakerx | doc/python/ChartingAPI.ipynb | 1 | 20656 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python API to BeakerX Interactive Plotting\n",
"\n",
"You can access Beaker's native interactive plotting library from Python.\n",
"\n",
"## Plot with simple properties\n",
"\n",
"Python plots has syntax very ... | apache-2.0 |
LimeeZ/phys292-2015-work | assignments/assignment11/OptimizationEx01.ipynb | 1 | 33649 | {
"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 |
code-shoily/data-playground | পাইথন ও ডাটাঃ নামপাই - ১.ipynb | 1 | 29024 | {
"metadata": {
"name": "\u09aa\u09be\u0987\u09a5\u09a8 \u0993 \u09a1\u09be\u099f\u09be\u0983 \u09a8\u09be\u09ae\u09aa\u09be\u0987 - \u09e7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u... | mit |
ecervera/mindstorms-nb | task/quadrat.ipynb | 1 | 4098 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercici: fer un quadrat\n",
"\n",
"<img src=\"img/bart-simpson-chalkboard.jpg\" align=\"right\" width=250>\n",
"A partir de les instruccions dels moviments bàsics, heu de fer un programa per a que el robot avance i gire ... | mit |
JamesSample/icpw | correct_toc_elev.ipynb | 1 | 11562 | {
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import imp\n",
"from sqlalchemy import create_engine"
]
},
{... | mit |
TomDecroos/matplotsoccer | experimental-notebooks/pieter.ipynb | 1 | 261974 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"import sys\n",
"sys.path.append(\"../src/\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadat... | mit |
WeatherSuperMan/Udacity-Self-driving-Car-Nanodegree | Term_1/Project_5_Vehicle_Detection/Project_5_Final_Submission_codes.ipynb | 1 | 1859987 | null | mit |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/c_neuralnetwork.ipynb | 1 | 10656 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4f3CKqFUqL2-",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Learning Objectives:**\n",... | apache-2.0 |
nproctor/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | 1 | 102545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Display Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader":... | mit |
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