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mne-tools/mne-tools.github.io	stable/_downloads/bcaf3ed1f43ea7377c6c0b00137d728f/custom_inverse_solver.ipynb	bsd-3-clause	[ "%matplotlib inline", "Source localization with a custom inverse solver\nThe objective of this example is to show how to plug a custom inverse solver\nin MNE in order to facilate empirical comparison with the methods MNE already\nimplements (wMNE, dSPM, sLORETA, eLORETA, LCMV, DICS, (TF-)MxNE etc.).\nThis script ...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ijmbarr/causalgraphicalmodels	notebooks/cgm-examples.ipynb	mit	[ "An Introduction to CausalGraphicalModels\nCausalGraphicalModel is a python module for describing and manipulating Causal Graphical Models and Structural Causal Models. Behind the curtain, it is a light wrapper around the python graph library networkx.\nThis notebook is designed to give a quick overview of the func...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Unidata/unidata-python-workshop	notebooks/MetPy_Case_Study/MetPy_Case_Study.ipynb	mit	[ "<a name=\"top\"></a>\n<div style=\"width:1000 px\">\n\n<div style=\"float:right; width:98 px; height:98px;\">\n<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n</div>\n\n<h1>MetPy Case Study</h1>\n\n<div s...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
letsgoexploring/economicData	business-cycle-data/python/.ipynb_checkpoints/business_cycle_data-checkpoint.ipynb	mit	[ "U.S. Business Cycle Data\nThis notebook downloads, manages, and exports several data series for studying business cycles in the US. Four files are created in the csv directory:\nFile name \| Description \|\n----------------------------------...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
dmittov/misc	BikeSharing-Linear.ipynb	apache-2.0	[ "Linear methods\nhttps://www.kaggle.com/c/bike-sharing-demand", "# !pip install -U kaggle\n# register the token in you kaggle profile & save it to ~/.kaggle/kaggle.json\n# !kaggle competitions download -c bike-sharing-demand\n\nimport pandas as pd\nfrom sklearn import linear_model\nfrom scipy import stats\nimpor...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
billzhao1990/CS231n-Spring-2017	assignment2/BatchNormalization.ipynb	mit	[ "Batch Normalization\nOne way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recen...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
phievo/phievo	Examples/immune/Analyse_pMHC.ipynb	lgpl-3.0	[ "Analyse Run\nThis is a template notebook to browse the results of a evolution simulation.\nWARNING: THIS IS THE IMMUNE ADD-ON. THIS NOTEBOOK SHOULD BE MOVED TO BE RUN IN THE SAME DIRECTORY AS run_evolution\nPlease Restart & Run All to make shure you start with a clean notebook.\nEnter the path of the project here ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
JasonSanchez/w261	exams/MIDS-MidTerm.ipynb	mit	[ "MIDS Machine Learning at Scale\nMidTerm Exam\n4:00PM - 6:00PM(CT)\nOctober 19, 2016 \nMidterm\nMIDS Machine Learning at Scale\nPlease insert your contact information here\nInsert you name here : Jason Sanchez\nInsert you email here : jason.sanchez@ischool.berkeley.edu \nInsert your UC Berkeley ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
arne-cl/alt-mulig	python/rstdt-batch-tokenization.ipynb	gpl-3.0	[ "Tokenization of RST-DT files using off-the-shelf tokenizers\n\nCoreNLP: failed\nnltk's TreebankWordTokenizer: failed, but might be adaptable\nlet's try the preprocessing provided by the Educational Testing Service's RST discourse parser,\n cf. rstdt-fixing-tokenization.ipynb", "import os\n\nfrom stanford_corenl...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
GoogleCloudPlatform/practical-ml-vision-book	09_deploying/09e_tflite.ipynb	apache-2.0	[ "from IPython.display import Markdown as md\n\n### change to reflect your notebook\n_nb_loc = \"09_deploying/09e_tflite.ipynb\"\n_nb_title = \"Edge ML with TensorFlow Lite\"\n\n### no need to change any of this\n_nb_safeloc = _nb_loc.replace('/', '%2F')\nmd(\"\"\"\n<table class=\"tfo-notebook-buttons\" align=\"left...	[ "code", "markdown", "code", "markdown", "code", "markdown" ]
AllenDowney/ThinkBayes2	examples/shuttle_soln.ipynb	mit	[ "Think Bayes\nCopyright 2018 Allen B. Downey\nMIT License: https://opensource.org/licenses/MIT", "# Configure Jupyter so figures appear in the notebook\n%matplotlib inline\n\n# Configure Jupyter to display the assigned value after an assignment\n%config InteractiveShell.ast_node_interactivity='last_expr_or_assign...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
xiaodongpang23/anomaly_detection	anomaly_detection.ipynb	mit	[ "import numpy as np\nimport pandas as pd\nimport networkx as nx\nimport json\nimport sys", "Step1: build the initial state of the entire user network, as well as the purchae history of the users\nInput: sample_dataset/batch_log.json", "batchlogfile = 'sample_dataset/batch_log.json'\ndf_batch = pd.read_json(batc...	[ "code", "markdown", "code", "markdown", "code" ]
GAMPTeam/vampyre	demos/sparse/sparse_lin_inverse.ipynb	mit	[ "Sparse Linear Inverse Demo\nIn this demo, we illustrate how to use the vampyre package for a simple sparse linear inverse problem. The problem is to estimate a sparse vector $z$ from linear measurements of the form $y=Az+w$ where $w$ is Gaussian noise and $A$ is a known linear transform -- a basic problem in com...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
buckleylab/Buckley_Lab_SIP_project_protocols	sequence_analysis_walkthrough/QIIME2_Processing_Pipeline.ipynb	mit	[ "Pipeling to Process Raw Sequences into Phyloseq Object with DADA2\n\nPrep for Import to QIIME2 (Combine two index files)\nImport to QIIME2\nDemultiplex\nDenoise and Merge\nPrepare OTU Tables and Rep Sequences (Note: sample names starting with a digit will break this step)\nClassify Seqs\n\n\n100% Appropriated fr...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
maxis42/ML-DA-Coursera-Yandex-MIPT	4 Stats for data analysis/Lectures notebooks/14 regression/stat.regression.ipynb	mit	[ "Линейная регрессия", "import statsmodels\nimport scipy as sc\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nimport statsmodels.stats.api as sms\nfrom statsmodels.graphics.regressionplots import plot_leverage_resid2\nimport matplotlib.pyplot as plt\n\n%pylab inline", "Постановк...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
tpin3694/tpin3694.github.io	machine-learning/accuracy.ipynb	mit	[ "Title: Accuracy\nSlug: accuracy\nSummary: How to evaluate a Python machine learning using accuracy. \nDate: 2017-09-15 12:00\nCategory: Machine Learning\nTags: Model Evaluation\nAuthors: Chris Albon\n<a alt=\"Accuracy\" href=\"https://machinelearningflashcards.com\">\n <img src=\"accuracy/Accuracy_print.png\" ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
AEW2015/PYNQ_PR_Overlay	Pynq-Z1/notebooks/examples/pmod_dac_adc.ipynb	bsd-3-clause	[ "DAC-ADC Pmod Examples using Matplotlib and Widget\n\nContents\nPmod DAC-ADC Feedback\nTracking the IO Error\nError plot with Matplotlib\nXKCD Plot\nWidget controlled plot\n\nPmod DAC-ADC Feedback\nThis example shows how to use the PmodDA4 DAC and the PmodAD2 ADC on the PYNQ-Z1 board, using the baord's two Pmod int...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
csaladenes/csaladenes.github.io	present/mcc2/PythonDataScienceHandbook/05.09-Principal-Component-Analysis.ipynb	mit	[ "<!--BOOK_INFORMATION-->\n<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\nThis notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.\nThe text is released under the CC-BY-NC-ND license, and code is released...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/recommendation_systems/solutions/content_based_preproc.ipynb	apache-2.0	[ "Create Datasets for the Content-based Filter\nThis notebook builds the data you will use for creating our content based model. You'll collect the data via a collection of SQL queries from the publicly available Kurier.at dataset in BigQuery.\nKurier.at is an Austrian newsite. The goal of these labs is to recommend...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
wuafeing/Python3-Tutorial	01 data structures and algorithms/01.07 keep dict in order.ipynb	gpl-3.0	[ "Previous\n1.7 字典排序\n问题\n你想创建一个字典，并且在迭代或序列化这个字典的时候能够控制元素的顺序。\n解决方案\n为了能控制一个字典中元素的顺序，你可以使用 collections 模块中的 OrderedDict 类。在迭代操作的时候它会保持元素被插入时的顺序，示例如下：", "from collections import OrderedDict\n\nd = OrderedDict()\nd[\"foo\"] = 1\nd[\"bar\"] = 2\nd[\"spam\"] = 3\nd[\"grok\"] = 4\n# Outputs \"foo 1\", \"bar 2\", \"spa...	[ "markdown", "code", "markdown", "code", "markdown" ]
diegocavalca/Studies	phd-thesis/Benchmarking 2 - Identificação de Cargas através de Representação Visual de Séries Temporais-Copy1.ipynb	cc0-1.0	[ "Identificação de Cargas através de Representação Visual de Séries Temporais\n\nArtigo: Imaging NILM Time-series\nURL: https://link.springer.com/chapter/10.1007/978-3-030-20257-6_16\nSource-code: https://github.com/LampriniKyrk/Imaging-NILM-time-series\nEstratégia proposta: converter série-temporal em imagens, extr...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tensorflow/text	docs/tutorials/text_similarity.ipynb	apache-2.0	[ "Copyright 2020 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Weenkus/Machine-Learning-University-of-Washington	Regression/examples/week-2-multiple-regression-assignment-1-blank.ipynb	mit	[ "Regression Week 2: Multiple Regression (Interpretation)\nThe goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.\nIn this notebook you will use data on house sales in King County to predict prices using multiple regression. You will:\n* Use SFrame...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
cassiogreco/udacity-data-analyst-nanodegree	P1/P1_Cassio.ipynb	mit	[ "1. What is our independent variable? What is our dependent variable?\nThe independent and dependent variables of the experiment are: \n\nIndependent\n\nWord/Color congruency\n\n\nDependent\n\nTime to name ink\n\n2. What is an appropriate set of hypotheses for this task? What kind of statistical test do you expect ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
diging/tethne-notebooks	2. Working with data from JSTOR Data-for-Research.ipynb	gpl-3.0	[ "%matplotlib inline\n\nfrom pprint import pprint\nimport matplotlib.pyplot as plt", "Introduction to Tethne: Working with data from the Web of Science\nNow that we have the basics down, in this notebook we'll begin working with data from the JSTOR Data-for-Research (DfR) portal.\nThe JSTOR DfR portal gives resear...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
milroy/Spark-Meetup	exercises/03_aggregation.ipynb	mit	[ "Simple Aggregation", "import numpy as np\n\ndata = np.arange(1000).reshape(100,10)\nprint data.shape", "Pandas", "import pandas as pd\n\npand_tmp = pd.DataFrame(data, \n columns=['x{0}'.format(i) for i in range(data.shape[1])])\npand_tmp.head()", "What is the row sum?", "pand_tmp.sum(axis=1)"...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pauliacomi/pyGAPS	docs/examples/parsing.ipynb	mit	[ "Parsing examples\nSome examples on parsing to and from supported formats. More info about all\nparsing methods can be found in the manual section.\nDeclare paths\nFirst, let's do all the necessary imports and generate the paths that we'll use\nfor file import and export.", "from pathlib import Path\nimport pygap...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
csampez/analisis-numerico-computo-cientifico	MNO/proyecto_final/MNO_2017/proyectos/equipos/equipo_6/avance_22_05_2017/code/Clase_SVD_Imagen.ipynb	apache-2.0	[ "SVD\nSVD a una imagen para validar resultados con CUDA cusolverDnDgesvd\nEquipo_6 \nIntegrantes:\n\n\nRicardo Lastra\n\n\nAdrián Vázquez\n\n\nAntecedentes:\nLa factorizacion $SVD$ es uno de los modelos de factorizaciones de matrices mas usados hoy en dia por muchas paqueterias computacionales, esta nos ayuda a hac...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
zlpure/CS231n	assignment1/two_layer_net.ipynb	mit	[ "Implementing a Neural Network\nIn this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset.", "# A bit of setup\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom cs231n.classifiers.neural_net import TwoLayerNet\n\n%...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
sastels/Onboarding	4 - Sorting.ipynb	mit	[ "Sorting\nThe easiest way to sort is with the sorted(list) function, which takes a list and returns a new list with those elements in sorted order. The original list is not changed.", "a = [5, 1, 4, 3]\nprint sorted(a)\nprint a", "It's most common to pass a list into the sorted() function, but in fact it can ta...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Kaggle/learntools	notebooks/machine_learning/raw/tut_titanic.ipynb	apache-2.0	[ "In the final exercise of the Intro to Machine Learning course, you learned how to make a submission to a Kaggle competition. But some of the work was already completed for you, since you were provided a notebook with partially completed code. \nIn this tutorial, you'll explore a full workflow that you can use to...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
palandatarxcom/sklearn_tutorial_cn	notebooks/02.1-Machine-Learning-Intro.ipynb	bsd-3-clause	[ "这个分析笔记由Jake Vanderplas编辑汇总。源代码和license文件在GitHub。中文翻译由派兰数据在派兰大数据分析平台上完成。源代码在GitHub上。\nScikit-learn简介: 基于Python的机器学习\n在本节中会介绍Scikit-learn的基本原理，它是一个集成了很多机器学习工具并被广泛使用的包，用Python实现。详情请参考http://scikit-learn.org 。\n概述\n主要目标：介绍机器学习的中心思想以及它们是怎样通过Scikit-learn集成进Python的。\n\n机器学习的定义\nScikit-learn中的数据表示\nScikit-learn的API的介绍\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
szitenberg/ReproPhyloVagrant	notebooks/Tutorials/Basic/3.8 Building a supermatrix.ipynb	mit	[ "This section shows how to build a supermatrix by providing minimal requirements for gene content per taxon (OTU). This approach is more suited for small scale analysis, because it relies on manual decisions, whereas large scale suprematrices are better constructed with the parameter space and data explorations too...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ML4DS/ML4all	R2.kNN_Regression/regression_knn_student.ipynb	mit	[ "The k-nearest neighbors (kNN) regression algorithm\nAuthor: Jerónimo Arenas García (jarenas@tsc.uc3m.es)\n Jesús Cid Sueiro (jcid@tsc.uc3m.es)\n\nNotebook version: 2.2 (Sep 08, 2017)\n\nChanges: v.1.0 - First version\nChanges: v.1.1 - Stock dataset included.\nChanges: v.2.0 - Notebook for UTAD course. Adver...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
metpy/MetPy	v1.1/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb	bsd-3-clause	[ "%matplotlib inline", "GINI Water Vapor Imagery\nUse MetPy's support for GINI files to read in a water vapor satellite image and plot the\ndata using CartoPy.", "import cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport xarray as xr\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io impor...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pmorissette/bt	examples/PTE.ipynb	mit	[ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport ffn\n\n#using this import until pip is updated to have the version of bt with the targetVol algo\n# you will need to change this be wherever your local version of bt is located.\nimport sys\nsys.path.insert(0, \"C:\\\\Users\\JPL09A\...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era	Keras_CNN_newsgroups_text_classification.ipynb	gpl-3.0	[ "20 newsgroups classification\nHere we use the 20 newsgroups text dataset by Ken Lang, which is a dataset of 20,000 messages from 20 different newsgroups. One thousand messages from each newsgroup were sampled randomly and classified by newsgroup.\nThe standard GloVe (Global Vectors for Word Representation) word ve...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
vravishankar/Jupyter-Books	Classes+and+Objects.ipynb	mit	[ "Object Oriented Programming\nAccording to Wikipedia, \"Object-oriented programming (OOP) is a programming paradigm based on the concept of 'objects', which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods.\"\nClasses & Objects\nA class ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jseabold/statsmodels	examples/notebooks/formulas.ipynb	bsd-3-clause	[ "Formulas: Fitting models using R-style formulas\nSince version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
prisae/blog-notebooks	MX_BarrancasDelCobre.ipynb	cc0-1.0	[ "Barrancas Del Cobre\nMaps for https://mexico.werthmuller.org/besucherreisen/barrancasdelcobre.\nYou can find more explanatory examples in Travel.ipynb, also in this directory.", "import travelmaps2 as tm\ntm.setup(dpi=200)\n\nfig_x = tm.plt.figure(figsize=(tm.cm2in([11, 6])))\n\n# Locations\nMDF = [19.433333, -...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
mne-tools/mne-tools.github.io	0.22/_downloads/f781cba191074d5f4243e5933c1e870d/plot_find_ref_artifacts.ipynb	bsd-3-clause	[ "%matplotlib inline", "Find MEG reference channel artifacts\nUse ICA decompositions of MEG reference channels to remove intermittent noise.\nMany MEG systems have an array of reference channels which are used to detect\nexternal magnetic noise. However, standard techniques that use reference\nchannels to remove n...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
DEIB-GECO/PyGMQL	examples/notebooks/02a_Mixing_Local_Remote_Processing_SIMPLE.ipynb	apache-2.0	[ "Interfacing with an external GMQL service: Aggregating the Chip-Seq signal of histone marks on promotorial regions\nIn this first application, genes' promoters are extracted from a local dataset and a large set of Chip-Seq experiments is selected from a remote repository. Then, for every promoter and for every Chi...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ajgpitch/qutip-notebooks	examples/qip-noisy-device-simulator.ipynb	lgpl-3.0	[ "Noisy quantum device simulation with QuTiP\nAuthor: Boxi Li (etamin1201@gmail.com)\nThis is the introduction notebook to the deliverable of one of the Google Summer of Code 2019 project (GSoC2019) \"Noise Models in QIP Module\", under the organization NumFocus. The final product of the project is a framework of no...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
awjuliani/DeepRL-Agents	Simple-Policy.ipynb	mit	[ "Simple Reinforcement Learning in Tensorflow Part 1:\nThe Multi-armed bandit\nThis tutorial contains a simple example of how to build a policy-gradient based agent that can solve the multi-armed bandit problem. For more information, see this Medium post.\nFor more Reinforcement Learning algorithms, including DQN an...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
agile-geoscience/striplog	docs/tutorial/10_Extract_curves_into_striplogs.ipynb	apache-2.0	[ "Extract curves into striplogs\nSometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).\nWe'll start by making some fake CSV text — we'll make 5 formations called A, B, C, D and E:", "data = \"\"\"Comp Formation,Depth\nA,100...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Roc-J/Python_data_science	Data_Mining/Local_outlier_factor.ipynb	apache-2.0	[ "局部异常因子方法发现异常点\n局部异常因子(Local Outlier Factor,LOF)也是一种异常检测算法，它对数据实例的局部密度和邻居进行比较，判断这个数据是否属于相似的密度的区域，它适合从那些簇个数未知，簇的密度和大小各不相同的数据中筛选出异常点。 \n从k近邻算法启发来", "from collections import defaultdict\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ninstance = np.matrix([[0,0],[0,1],[1,1],[1,0],[5,0]])\n\nx = np.squeeze(np...	[ "markdown", "code", "markdown", "code", "markdown" ]
napjon/ds-nd	p2-introds/nyc_subway/project.ipynb	mit	[ "Overview\nNYC Subway contains regular number of ridership across different conditions. It also contains time series. In this analysis, I investigate whether there is difference between raining vs not raining, and other statistical method to build the model, predicting number of ridership.", "import pandas as pd\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
rubensfernando/mba-analytics-big-data	Python/2016-07-22/aula2-parte1-funcoes.ipynb	mit	[ "Funções\n\nAté agora, vimos diversos tipos de dados, atribuições, comparações e estruturas de controle.\nA ideia da função é dividir para conquistar, onde:\nUm problema é dividido em diversos subproblemas\nAs soluções dos subproblemas são combinadas numa solução do problema maior.\n\n\n\nEsses subproblemas têm o n...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jsharpna/DavisSML	lectures/lecture5/lecture5.ipynb	mit	[ "The Lasso\nStatML: Lecture 5\nProf. James Sharpnack\n\nSome content and images are from \"The Elements of Statistical Learning\" by Hastie, Tibshirani, Friedman\nReading ESL Chapter 3\n\nRecall Convex Optimization\nDef A function $f : \\mathbb R^p \\to \\mathbb R$ is convex if for any $0 \\le \\alpha \\le 1$, $x_0...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
sdpython/ensae_teaching_cs	_doc/notebooks/td2a_ml/seasonal_timeseries.ipynb	mit	[ "Timeseries\nCe notebook présente quelques étapes simples pour une série temporelle. La plupart utilise le module statsmodels.tsa.", "from jyquickhelper import add_notebook_menu\nadd_notebook_menu()\n\n%matplotlib inline", "Données\nLes données sont artificielles mais simulent ce que pourraient être le chiffre ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
bbalasub1/glmnet_python	docs/glmnet_vignette.ipynb	gpl-3.0	[ "Glmnet Vignette (for python)\nJuly 12, 2017\nAuthors\nTrevor Hastie, B. J. Balakumar\nIntroduction\nGlmnet is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
waltervh/BornAgain-tutorial	old/python/tutorial.ipynb	gpl-3.0	[ "Introduction to Python\nUseful links\n\nBornAgain: http://bornagainproject.org\nBornAgain tutorial: https://github.com/scgmlz/BornAgain-tutorial\nPython official tutorial: https://docs.python.org/3/tutorial/\nAnaconda Python: https://www.continuum.io/\nPyCharm IDE: https://www.jetbrains.com/pycharm/\n\nNote that B...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
PYPIT/PYPIT	doc/nb/LRIS_blue_notes.ipynb	gpl-3.0	[ "Notes on the LRIS Blue reduction", "# imports\nsys.path.append(os.path.abspath('/Users/xavier/local/Python/PYPIT/src'))\nimport arload as pyp_arload\nimport ario as pyp_ario", "Detectors\nNote: LRISb has employed different detectors. We may need to\nmake PYPIT backwards compatible.\nFITS file", "fil = '/Us...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
madsenmj/ml-introduction-course	Class03/Class03.ipynb	apache-2.0	[ "Class 03\nBig Data Cleaning: Data Transformations\nAlthough machine learning is the exciting part of this course, most data scientists spend the vast majority of their time doing data clearning and data wrangling. Some put the figure at as high as 90% of their time! There is a good reason for this: most of the dat...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Hvass-Labs/TensorFlow-Tutorials	13B_Visual_Analysis_MNIST.ipynb	mit	[ "TensorFlow Tutorial #13-B\nVisual Analysis (MNIST)\nby Magnus Erik Hvass Pedersen\n/ GitHub / Videos on YouTube\nIntroduction\nTutorial #13 showed how to find input images that maximized the response of individual neurons inside the Inception model, so as to find the images that the neuron liked to see. But becaus...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
y2ee201/Deep-Learning-Nanodegree	sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Lesson 5).ipynb	mit	[ "Sentiment Classification & How To \"Frame Problems\" for a Neural Network\nby Andrew Trask\n\nTwitter: @iamtrask\nBlog: http://iamtrask.github.io\n\nWhat You Should Already Know\n\nneural networks, forward and back-propagation\nstochastic gradient descent\nmean squared error\nand train/test splits\n\nWhere to Get ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Startupsci/data-science-notebooks	titanic-data-science-solutions.ipynb	mit	[ "Titanic Data Science Solutions\nThis notebook is companion to the book Data Science Solutions. The notebook walks us through a typical workflow for solving data science competitions at sites like Kaggle.\nThere are several excellent notebooks to study data science competition entries. However many will skip some o...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mannyfin/IRAS	Type C calibrations/TypeC calcs corrected.ipynb	bsd-3-clause	[ "The situation\nType C thermocouples are not NIST calibrated to below 273.15 K. For my research specific scenario, I need to cool my sample (Molybdenum) to cryogenic temperatures and also anneal to very high ~2000 K. There is no thermocouple with these properties. \nThe solution\nWe know that Type K thermocouples a...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pxcandeias/py-notebooks	FRF_plots.ipynb	mit	[ "<a id='top'></a>\nFrequency Response Functions (FRFs) plots\nThis notebook is about frequency response functions (FRFs) and the various ways they can be plotted.\nTable of contents\nPreamble\nDynamic system setup\nFrequency response function\nNyquist plot\nBode plot\nNichols plot\nOdds and ends\nPreamble\nWe will ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
lithiumdenis/MLSchool	2. Бостон.ipynb	mit	[ "import pandas as pd\nimport numpy as np", "Загрузим данные", "from sklearn.datasets import load_boston\n\nbunch = load_boston()\n\nprint(bunch.DESCR)\n\nX, y = pd.DataFrame(data=bunch.data, columns=bunch.feature_names.astype(str)), bunch.target\n\nX.head()", "Зафиксируем генератор случайных чисел для воспрои...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ES-DOC/esdoc-jupyterhub	notebooks/bnu/cmip6/models/sandbox-3/land.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Land\nMIP Era: CMIP6\nInstitute: BNU\nSource ID: SANDBOX-3\nTopic: Land\nSub-Topics: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \nProperties: 154 (96 required)\nModel descriptions: Model description details\nInitialized From: -- \nNo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
DS-100/sp17-materials	sp17/disc/disc11/disc11_solution.ipynb	gpl-3.0	[ "Discussion 11: Logistic Regression and Gradient Descent", "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import patches, cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nfrom mpl_toolkits.mplot3d import Axes3D\n%matplotlib inline\n\nfrom IPython.dis...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
probml/pyprobml	deprecated/simulated_annealing_2d_demo.ipynb	mit	[ "<a href=\"https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/simulated_annealing_2d_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\nSimulated annealing on a 2d surface\nCode is based on\nhttps://krischer....	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mne-tools/mne-tools.github.io	0.20/_downloads/bf3ad991f7c7776e245520709f49cb04/plot_cwt_sensor_connectivity.ipynb	bsd-3-clause	[ "%matplotlib inline", "Compute seed-based time-frequency connectivity in sensor space\nComputes the connectivity between a seed-gradiometer close to the visual cortex\nand all other gradiometers. The connectivity is computed in the time-frequency\ndomain using Morlet wavelets and the debiased squared weighted pha...	[ "code", "markdown", "code", "markdown", "code" ]
jorgedominguezchavez/dlnd_first_neural_network	Your_first_neural_network.ipynb	mit	[ "Your first neural network\nIn 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 network up to you (for the most part). After you've submitted this project, feel free to explore the data ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
WillenZh/deep-learning-project	tutorials/autoencoder/Convolutional_Autoencoder.ipynb	mit	[ "Convolutional Autoencoder\nSticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.", "%matplotlib inline\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom tensorflow.examples.tutorials.mnis...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
google-research/language	language/multiberts/coref.ipynb	apache-2.0	[ "Application: Gender Bias in Coreference Systems\nThis notebook walks through the analysis in Section 4 of the paper. We'll look at accuracy and bias correlation metrics on the Winogender dataset of Rudinger et al. 2018, and show how the multibootstrap can be used in two different ways:\n\nA paired analysis of an i...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
akloster/porekit-python	examples/squiggle_classifier_1/Read_Until_Efficiency.ipynb	isc	[ "Theoretical Efficiency of Read Until Enrichment\nThe \"Read Until\" feature of the Oxford Nanopore sequencing technology means a program can see the data coming in at each pore and, dependend on that data, reject the molecule inside a certain pore.\nThe actual performance of such a method depends on a lot of facto...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
david4096/bioapi-examples	python_notebooks/1kg_sequence_annotation_service.ipynb	apache-2.0	[ "GA4GH 1000 Genomes Sequence Annotations Example\nThis example illustrates how to access the sequence annotations for a given set of ....\nInitialize Client\nIn this step we create a client object which will be used to communicate with the server. It is initialized using the URL.", "from ga4gh.client import clien...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
psychemedia/ou-robotics-vrep	robotVM/notebooks/Demo - Square 2 - Variables.ipynb	apache-2.0	[ "Traverse a Square - Part 2 - Variables\nIn this notebook, we will introduce one of the most powerful ideas in programming: the variable.\nA variable is a container that we can reference by name that is associated with a particular value. The value is assigned to the variable using the the = operator, which we migh...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ES-DOC/esdoc-jupyterhub	notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/aerosol.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Aerosol\nMIP Era: CMIP6\nInstitute: MESSY-CONSORTIUM\nSource ID: EMAC-2-53-AERCHEM\nTopic: Aerosol\nSub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \nProperties: 69 (37 required)\nModel descriptions: Model description details\nInitialized From:...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Kulbear/deep-learning-nano-foundation	DLND-tv-script-generation/dlnd_tv_script_generation.ipynb	mit	[ "TV Script Generation\nIn this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.\nGet the Data\nThe data is already provided for you. ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
JorisBolsens/PYNQ	Pynq-Z1/notebooks/examples/video_filters.ipynb	bsd-3-clause	[ "Software Grayscale and Sobel filters on HDMI input\nThis example notebook will demonstrate two image filters using a snapshot from the HDMI input: <br>\n1. First, a frame is read from HDMI input\n2. That image is saved and displayed in the notebook\n3. Some simple Python pixel-level image processing is done (Gray ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ES-DOC/esdoc-jupyterhub	notebooks/inm/cmip6/models/inm-cm4-8/seaice.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Seaice\nMIP Era: CMIP6\nInstitute: INM\nSource ID: INM-CM4-8\nTopic: Seaice\nSub-Topics: Dynamics, Thermodynamics, Radiative Processes. \nProperties: 80 (63 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help page\nNote...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
sailuh/perceive	Parsers/SecLists/Reply-Parse.ipynb	gpl-2.0	[ "Seclists reply parse\nExample: http://seclists.org/fulldisclosure/2017/Jan/0\nWith each reply, we'll attempt to parse out the following:\n* raw reply text, without html tags\n * the reply text with any signatures stripped out\n* an analysis of what html tags are in the message\n* a listing of which domains are re...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ocefpaf/secoora	notebooks/timeSeries/ssv/00-velocity_secoora.ipynb	mit	[ "SECOORA sea surface temperature time-series notebook\nProduce weekly maps and tables for the SECOORA. Based on IOOS system-test notebook.", "import time\nstart_time = time.time()\n\nimport os\n\n%load_ext watermark\n%watermark --githash --machine --python --packages iris,pyoos,owslib\n\nstyle = os.path.join(os....	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
dsiufl/2015-Fall-Hadoop	instructor-notes/3-pyspark-wordcount.ipynb	mit	[ "Spark version of wordcount examples\nPrepare the pyspark environment.", "import findspark\nimport os\nfindspark.init('/home/ubuntu/shortcourse/spark-1.5.1-bin-hadoop2.6')\n\nfrom pyspark import SparkContext, SparkConf\nconf = SparkConf().setAppName(\"test\").setMaster(\"local[2]\")\nsc = SparkContext(conf=conf)"...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
agile-geoscience/welly	docs/_userguide/Projects.ipynb	apache-2.0	[ "Projects\nWells are one of the fundamental objects in welly.\nWell objects include collections of Curve objects. Multiple Well objects can be stored in a Project.\nOn this page, we take a closer look at the Project class. It lets us handle groups of wells. It is really just a list of Well objects, with a few extra...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mne-tools/mne-tools.github.io	dev/_downloads/00e78bba5d10188fcf003ef05e32a6f7/decoding_time_generalization_conditions.ipynb	bsd-3-clause	[ "%matplotlib inline", "Decoding sensor space data with generalization across time and conditions\nThis example runs the analysis described in :footcite:KingDehaene2014. It\nillustrates how one can\nfit a linear classifier to identify a discriminatory topography at a given time\ninstant and subsequently assess whe...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
h-mayorquin/time_series_basic	presentations/2016-01-21(Wall-Street-Letter-Latency-Prediction).ipynb	bsd-3-clause	[ "Prediction of text with Nexa Letter Latency.\nThis notbook is for seeing how much the delay between the code vector and the code is related to the accuaracy of the prediciton.", "import numpy as np\nimport h5py\nfrom sklearn import svm, cross_validation\nfrom sklearn.naive_bayes import MultinomialNB", "Load th...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/feateng/feateng.ipynb	apache-2.0	[ "<h1> Feature Engineering </h1>\n\nIn this notebook, you will learn how to incorporate feature engineering into your pipeline.\n<ul>\n<li> Working with feature columns </li>\n<li> Adding feature crosses in TensorFlow </li>\n<li> Reading data from BigQuery </li>\n<li> Creating datasets using Dataflow </li>\n<li> Usi...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
crdietrich/sparklines	Pandas Sparklines Demo.ipynb	mit	[ "Sparklines in Pandas\nSparklines are small unlabeled plots, used to visually convey an idea in a small space. This script creates sparklines in a Pandas DataFrame which can then be displayed inline in a Jupyter Notebook or output to an HTML file. It does not annotate the figure, other columns of the DataFrame ca...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mit-eicu/eicu-code	notebooks/medication.ipynb	mit	[ "medication\nThe medications table reflects the active medication orders for patients. These are orders but do not necessarily reflect administration to the patient. For example, while existence of data in the infusionDrug table confirms a patient received a continuous infusion, existence of the same data in this t...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
opengeostat/pygslib	pygslib/Ipython_templates/backtr_raw.ipynb	mit	[ "Testing the back normalscore transformation", "#general imports\nimport matplotlib.pyplot as plt \nimport pygslib \nfrom matplotlib.patches import Ellipse\nimport numpy as np\nimport pandas as pd\n\n#make the plots inline\n%matplotlib inline ", "Getting the data ready for work\nIf the data is in GSLIB fo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
henrysky/astroNN	demo_tutorial/VAE/variational_autoencoder_demo.ipynb	mit	[ "Variational Autoencoder demo with 1D data\nHere is astroNN, please take a look if you are interested in astronomy or how neural network applied in astronomy\n* Henry Leung - Astronomy student, University of Toronto - henrysky\n* Project advisor: Jo Bovy - Professor, Department of Astronomy and Astrophysics, Univer...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
turbomanage/training-data-analyst	blogs/goes16/maria/hurricanes2017.ipynb	apache-2.0	[ "2017 Hurricane Tracks\nDemonstrates how to plot all the North American hurricane tracks in 2017, starting from the BigQuery public dataset.", "%bash\napt-get update\napt-get -y install python-mpltoolkits.basemap \n\nfrom mpl_toolkits.basemap import Basemap\nimport google.datalab.bigquery as bq\nimport matplotlib...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
probml/pyprobml	notebooks/misc/linreg_divorce_numpyro.ipynb	mit	[ "<a href=\"https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/linreg_divorce_numpyro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\nRobust linear regression\nWe illustrate linear using the \"waffle divorce\" ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
saketkc/notebooks	python/coursera-BayesianML/04_mcmc_assignment.ipynb	bsd-2-clause	[ "<a href=\"https://colab.research.google.com/github/saketkc/notebooks/blob/master/python/coursera-BayesianML/04_mcmc_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\nFirst things first\nClick File -> Save a copy in Drive and cli...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
karlstroetmann/Formal-Languages	Ply/Conflicts-Resolved.ipynb	gpl-2.0	[ "from IPython.core.display import HTML\nwith open (\"../style.css\", \"r\") as file:\n css = file.read()\nHTML(css)", "Resolving Conflicts Using Precedence Declarations\nThis file shows how shift/reduce and reduce/reduce conflicts can be resolved using operator precedence declarations.\nThe following grammar i...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
willettk/insight	notebooks/neural_networks_and_deep_learning.ipynb	apache-2.0	[ "Work with http://neuralnetworksanddeeplearning.com/", "%matplotlib inline\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport random", "Chapter 1", "def sigmoid(z):\n \n return 1./(1. + np.exp(-z))\n\ndef sigmoid_vector(w,x,b):\n \n return 1./(1. + np.exp(-1 * np.sum(w * x) - b))\n\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
nagordon/mechpy	tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb	mit	[ "Mechpy Tutorials\na mechanical engineering toolbox\nsource code - https://github.com/nagordon/mechpy\ndocumentation - https://nagordon.github.io/mechpy/web/ \n\nNeal Gordon\n2017-02-20 \n\nComposite Plate Mechanics with Python\nreference: hyer page 584. 617\nThe motivation behind this talk is to explore the capa...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
pligor/predicting-future-product-prices	04_time_series_prediction/24_price_history_seq2seq-full_dataset_testing.ipynb	agpl-3.0	[ "# -- coding: UTF-8 --\n#%load_ext autoreload\n%reload_ext autoreload\n%autoreload 2\n\nfrom __future__ import division\nimport tensorflow as tf\nfrom os import path, remove\nimport numpy as np\nimport pandas as pd\nimport csv\nfrom sklearn.model_selection import StratifiedShuffleSplit\nfrom time import time\nfro...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tuanavu/coursera-university-of-washington	machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank.ipynb	mit	[ "Implementing logistic regression from scratch\nThe goal of this notebook is to implement your own logistic regression classifier. You will:\n\nExtract features from Amazon product reviews.\nConvert an SFrame into a NumPy array.\nImplement the link function for logistic regression.\nWrite a function to compute the ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
gtesei/DeepExperiments	MNIST_for_beginners_noNN_noCONV_0.12.0-rc1.ipynb	apache-2.0	[ "MNIST For ML Beginners\nA very simple MNIST classifier. See extensive documentation at http://tensorflow.org/tutorials/mnist/beginners/index.md", "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os.path\n\nimport argparse\nimport sys\nimpor...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
jrg365/gpytorch	examples/01_Exact_GPs/Spectral_Delta_GP_Regression.ipynb	mit	[ "Spectral GP Learning with Deltas\nIn this paper, we demonstrate another approach to spectral learning with GPs, learning a spectral density as a simple mixture of deltas. This has been explored, for example, as early as Lázaro-Gredilla et al., 2010.\nCompared to learning Gaussian mixtures as in the SM kernel, this...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
balarsen/pymc_learning	Counting/Poisson and exponential.ipynb	bsd-3-clause	[ "Go from exponential to Poisson\nAlso look to: Adams RP, Murray I, MacKay DJC. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proceedings of the 26th Annual International Conference on Machine Learning; Montreal, Quebec, Canada. 1553376: ACM; 2009. p. 9-16.\nSome...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
bt3gl/Machine-Learning-Resources	ml_notebooks/synthetic_features_and_outliers.ipynb	gpl-2.0	[ "Copyright 2017 Google LLC.", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to i...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ishank26/nn_from_scratch	.ipynb_checkpoints/mlnn-checkpoint.ipynb	gpl-3.0	[ "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport sklearn as skl\nimport sklearn.datasets\nimport sklearn.linear_model\n%matplotlib inline\n\n# Generate data\nX, y = sklearn.datasets.make_moons(300, noise=0.22)\nplt.figure(figsize=(7, 5))\nplt.scatter(X[:, 0], X[:, 1], s=15, c=y, cmap=...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
vadim-ivlev/STUDY	handson-data-science-python/DataScience-Python3/.ipynb_checkpoints/MeanMedianMode-checkpoint.ipynb	mit	[ "Mean, Median, Mode, and introducing NumPy\nMean vs. Median\nLet's create some fake income data, centered around 27,000 with a normal distribution and standard deviation of 15,000, with 10,000 data points. (We'll discuss those terms more later, if you're not familiar with them.)\nThen, compute the mean (average) - ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
NYUDataBootcamp/Projects	UG_F16/RodriguezBallve-Spain's_Labor_Market.ipynb	mit	[ "Exploring Spain's Broken Labor Market\nAuthor Bosco Rodríguez Ballvé\nDate Fall 2016\nClass Data Bootcamp @ NYU Stern \nInstructors Coleman, Lyon\nAbstract\nA successful economy in the 21st century, in which the mix of products and services is changing constantly, requires a dynamic labor market as a mechanism to ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

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