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tensorflow/probability | tensorflow_probability/python/experimental/nn/examples/vib_dose.ipynb | apache-2.0 | [
"Copyright 2020 The TensorFlow Authors.\nLicensed under the Apache License, Version 2.0 (the \"License\");",
"#@title ##### Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of t... | [
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mne-tools/mne-tools.github.io | dev/_downloads/c6baf7c1a2f53fda44e93271b91f45b8/50_beamformer_lcmv.ipynb | bsd-3-clause | [
"%matplotlib inline",
"Source reconstruction using an LCMV beamformer\nThis tutorial gives an overview of the beamformer method and shows how to\nreconstruct source activity using an LCMV beamformer.",
"# Authors: Britta Westner <britta.wstnr@gmail.com>\n# Eric Larson <larson.eric.d@gmail.com>\n#\n# Li... | [
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4dsolutions/Python5 | Comparing JavaScript with Python.ipynb | mit | [
"Python for Everyone!<br/>Oregon Curriculum Network\nPython and JavaScript\nJavaScript has been moving a lot closer to Python, nowadays supporting classes, with a constructor like __init__ and this for self within that construct (ES has had this for awhile). \nHere in a Jupyter Notebook, we have a way to run both J... | [
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alepoydes/introduction-to-numerical-simulation | practice/What does mean mean mean.ipynb | mit | [
"%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt",
"Вычисление сумм\nВ статистике часто приходится считать выборочное среднее, т.е. по данной выборке значений $x_k$, $k=1..N$, нужно вычислить\n$$\\bar x=\\frac1N\\sum_{k=1}^N x_k.$$\nС точки зрения математики не имеет значения, как считать ... | [
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yuvrajsingh86/DeepLearning_Udacity | sentiment-network/Sentiment_Classification_Projects.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 ... | [
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hmenke/pairinteraction | doc/sphinx/examples_python/vdw_near_surface.ipynb | gpl-3.0 | [
"Dispersion Coefficients Near Surfaces\nIn this tutorial we reproduce the results depicted in Figure 5 from J. Block and S. Scheel \"van der Waals interaction potential between Rydberg atoms near surfaces\" Phys. Rev. A 96, 062509 (2017). We calculate the van der Waals $C_6$-coefficient between two Rubidium Rydberg... | [
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metpy/MetPy | v0.9/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb | bsd-3-clause | [
"%matplotlib inline",
"Simple Sounding\nUse MetPy as straightforward as possible to make a Skew-T LogP plot.",
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport metpy.calc as mpcalc\nfrom metpy.cbook import get_test_data\nfrom metpy.plots import add_metpy_logo, SkewT\nfrom metp... | [
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intel-analytics/BigDL | apps/ray/parameter_server/sharded_parameter_server.ipynb | apache-2.0 | [
"This notebook is adapted from:\nhttps://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb\nSharded Parameter Servers\nGOAL: The goal of this exercise is to use actor handles to implement a sharded parameter server example for distributed asynchronous stochastic gradient descent.\n... | [
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llooker/public-datasets-pipelines | samples/tutorial.ipynb | apache-2.0 | [
"Overview\nAdd a brief description of this tutorial here.",
"%%capture\n\n# Installing the required libraries:\n!pip install matplotlib pandas scikit-learn tensorflow pyarrow tqdm\n!pip install google-cloud-bigquery google-cloud-bigquery-storage\n!pip install flake8 pycodestyle pycodestyle_magic\n\n# Python Built... | [
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stereoboy/Study | Issues/algorithms/Linked Lists.ipynb | mit | [
"Add two numbers represented by linked lists",
"class Node():\n def __init__(self, value, Next=None):\n self.value = value\n self.next = Next\n def __str__(self):\n if self.next == None:\n return str(self.value)\n return str(self.value) + '-' + self.next.__str__()\n\nd... | [
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TimothyADavis/KinMSpy | kinms/docs/KinMSpy_tutorial.ipynb | mit | [
"KinMS galaxy fitting tutorial\nThis tutorial aims at getting you up and running with galaxy kinematic modelling using KinMS! To start you will need to download the KinMSpy code and have it in your python path. \nTo do this you can simply call pip install kinms\nTo get started with kinematic modelling we will compl... | [
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napsternxg/ipython-notebooks | Monte Carlo Integration.ipynb | apache-2.0 | [
"Introduction to Monte Carlo Integration\nInspired from the following posts:\n\nhttp://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab7/GibbsSampler.ipynb\nhttp://twiecki.github.io/blog/2015/11/10/mcmc-sampling/\nhttps://en.wikipedia.org/wiki/Monte_Carlo_integration",
"%matplotlib inline\n\nimport ... | [
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ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1h/aerosol.ipynb | gpl-3.0 | [
"ES-DOC CMIP6 Model Properties - Aerosol\nMIP Era: CMIP6\nInstitute: NASA-GISS\nSource ID: GISS-E2-1H\nTopic: Aerosol\nSub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \nProperties: 69 (37 required)\nModel descriptions: Model description details\nInitialized From: -- \nNoteboo... | [
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ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/seaice.ipynb | gpl-3.0 | [
"ES-DOC CMIP6 Model Properties - Seaice\nMIP Era: CMIP6\nInstitute: CNRM-CERFACS\nSource ID: CNRM-CM6-1\nTopic: Seaice\nSub-Topics: Dynamics, Thermodynamics, Radiative Processes. \nProperties: 80 (63 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help ... | [
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google/starthinker | colabs/cm360_segmentology.ipynb | apache-2.0 | [
"CM360 Segmentology\nCM360 funnel analysis using Census data.\nLicense\nCopyright 2020 Google LLC,\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\nhttps://www.apache.org/licenses/LICENSE-2.... | [
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aborgher/Main-useful-functions-for-ML | .ipynb_checkpoints/NLP-checkpoint.ipynb | gpl-3.0 | [
"Table of Contents\n<p><div class=\"lev1 toc-item\"><a href=\"#Correction-with-enchant\" data-toc-modified-id=\"Correction-with-enchant-1\"><span class=\"toc-item-num\">1 </span>Correction with enchant</a></div><div class=\"lev2 toc-item\"><a href=\"#Add-your-own-dictionary\" data-toc-modified-id=\"Add-y... | [
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ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/ukesm1-0-ll/aerosol.ipynb | gpl-3.0 | [
"ES-DOC CMIP6 Model Properties - Aerosol\nMIP Era: CMIP6\nInstitute: NERC\nSource ID: UKESM1-0-LL\nTopic: Aerosol\nSub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \nProperties: 69 (37 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook He... | [
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minh5/cpsc | reports/neiss.ipynb | mit | [
"Table of Contents\n<p><div class=\"lev2 toc-item\"><a href=\"#Are-there-products-we-should-be-aware-of?\" data-toc-modified-id=\"Are-there-products-we-should-be-aware-of?-01\"><span class=\"toc-item-num\">0.1 </span>Are there products we should be aware of?</a></div><div class=\"lev2 toc-item\"><a href=... | [
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JAmarel/Phys202 | Integration/IntegrationEx02.ipynb | mit | [
"Integration Exercise 2\nImports",
"%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom scipy import integrate",
"Indefinite integrals\nHere is a table of definite integrals. Many of these integrals has a number of parameters $a$, $b$, etc.\nFind five of these in... | [
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ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | [
"ES-DOC CMIP6 Model Properties - Ocean\nMIP Era: CMIP6\nInstitute: INM\nSource ID: SANDBOX-2\nTopic: Ocean\nSub-Topics: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \nProperties: 133 (101 required)\nModel descriptions: Model description details\nInitializ... | [
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mmckerns/tutmom | solutions.ipynb | bsd-3-clause | [
"Solutions to exercises\nEXERCISE: Solve the constrained programming problem by any of the means above.\nMinimize: f = -1x[0] + 4x[1]\nSubject to: <br>\n-3x[0] + 1x[1] <= 6 <br>\n1x[0] + 2x[1] <= 4 <br>\nx[1] >= -3 <br>\nwhere: -inf <= x[0] <= inf",
"import cvxopt as cvx\nfrom cvxopt import solvers as cvx_solver... | [
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Krastanov/cutiepy | examples/Lindblad_Master_Equation_Solver_Examples.ipynb | bsd-3-clause | [
"Table of Contents\n\nRabi Oscillations\nSimulating the Full Hamiltonian\nWith Rotating Wave Approximation\nWith $\\gamma_1$ collapse\nWith $\\gamma_2$ collapse\n\n\n\n\nCoherent State in a Harmonic Oscillator",
"from cutiepy import *\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# T... | [
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albahnsen/ML_SecurityInformatics | notebooks/14-KaggleCompetition.ipynb | mit | [
"14 - Kaggle Competition\nFraud Detection\nhttps://inclass.kaggle.com/c/easy-ml-class\nby Alejandro Correa Bahnsen\nversion 0.1, May 2016\nPart of the class Machine Learning for Security Informatics\nThis notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License]\npip install tqdm\n... | [
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GoogleCloudPlatform/vertex-ai-samples | notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb | apache-2.0 | [
"# Copyright 2021 Google LLC\n#\n# 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 ... | [
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zzsza/Datascience_School | 16. 과최적화와 정규화/02. 교차 검증.ipynb | mit | [
"교차 검증\n모형 검증\n예측 모형의 최종 성능을 객관적으로 측정하려면 모수 추정(parameter fitting) 즉 트레이닝(training)에 사용되지 않은 새로운 데이터, 즉 테스트 데이터를 사용해야 한다. 모형의 모수 갯수를 증가시킨다든가 커널 모형, 신경망 모형과 같은 비선형 모형을 사용하게 되면 트레이닝 데이터에 대한 예측 성능을 얼마든지 높일 수 있기 때문이다. 이러한 방법에 의해 과최적화(overfitting)가 일어나면 트레이닝 데이터에 대해서는 예측이 잘되지만 테스트 데이터에 대해서는 예측 성능이 급격히 떨어지는 현상이 발생한다.\n교차 ... | [
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phuongxuanpham/SelfDrivingCar | CarND-Camera-Calibration/camera_calibration.ipynb | gpl-3.0 | [
"%%HTML\n<style> code {background-color : pink !important;} </style>",
"Camera Calibration with OpenCV\nRun the code in the cell below to extract object points and image points for camera calibration.",
"import numpy as np\nimport cv2\nimport glob\nimport matplotlib.pyplot as plt\n%matplotlib qt\n\n# prepare ob... | [
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paris-saclay-cds/python-workshop | Day_2_Software_engineering_best_practices/solutions/03_code_style.ipynb | bsd-3-clause | [
"import itertools\n\nimport six\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# preprocessing\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\n# classifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklea... | [
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luctrudeau/Teaching | IntroductionIOAsync/IntroductionIOAsync.ipynb | lgpl-3.0 | [
"Introduction à l'I/O Asynchrone\nLe Socket de Berkeley\nIl est difficile d'imaginer le nombre d'instanciations d'objets de type Socket depuis leur introduction en 1983 à l'université Berkeley.\nLe Socket est l'interface de programmation la plus populaire pour faire de la réseautique.\nElle est si populaire, que to... | [
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ThunderShiviah/code_guild | interactive-coding-challenges/graphs_trees/check_balance/check_balance_challenge.ipynb | mit | [
"<small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>\nChallenge Notebook\nProblem: Check if a binary tree is balanced.\n\nConstraints\nTest Cases\nAlgorithm\nCode\nUnit Test\nSolution Notebook\n\nConstraints\n\nIs a balanced tree one where the heights of two sub t... | [
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trangel/Data-Science | deep_learning_ai/Tensorflow+Tutorial+dropout.ipynb | gpl-3.0 | [
"TensorFlow Tutorial\nWelcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe,... | [
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anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/04.00 Order Management.ipynb | mit | [
"<!--NAVIGATION-->\n< Account Information | Contents | Trade Management >\nOrder Management\nOrders\nCreating Orders\ncreate_order(self, account_id, **params)",
"from datetime import datetime, timedelta\nimport pandas as pd\nimport oandapy\nimport configparser\n\nconfig = configparser.ConfigParser()\nconfig.read(... | [
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justanr/notebooks | hexagonal/refactoring_and_interfaces.ipynb | mit | [
"I've been thinking a lot about software achitecure lately. Not just thinking, because I wouldn't come up with these ideas on my own, but consuming a lot about it -- books, talks, slide decks, blog posts. And while thinking about all this, I've been hacking away at some projects in my spare time. And I noticed some... | [
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nicolas998/Analisis_Datos | 02_Medidas_Localizacion.ipynb | gpl-3.0 | [
"Medidas de Localización\nMedidas típicas, que son paramétricas:\n- Media: $\\mu = \\frac{\\sum_{i=1}^{N} x_i}{N} $\n- Desviación $\\sigma = \\sqrt{ \\frac{1}{N} \\sum_{i=1}^{N} (x_i -\\mu)^2}$\n- Asimetría.\n- etc...\nExisten otras medidas que son no paramétricas, estas pueden ser:\n- Mediana.\n- Quintiles.\n- Dec... | [
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HUDataScience/StatisticalMethods2016 | notebooks/Basic_Python.ipynb | apache-2.0 | [
"Basic of Python.\nThe library we are going to use are the following:\n\nnumpy",
"for i in range(10):\n y = i*40 +i**2 + 2\n print(y)\n \ni = i+2\n\nrange(10)\n\nfor i in [\"a\",\"b\",\"tt\"]:\n print i\n\nx = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],[2, 1, 2, 3, 4, 5, 6, 7, 8, 9],[0,5, 2, 3, 4, 5, 6, 7, 8, 9]]\... | [
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afeiguin/comp-phys | 14_02_multilayer-networks.ipynb | mit | [
"How a regression network is traditionally trained\nThis network is trained using a data set $D = ({{\\bf x}^{(n)}, {\\bf t}^{(n)}})$ by adjusting ${\\bf w}$ so as to minimize an error function, e.g.,\n$$\nE_D({\\bf w}) = \\sum_n\\sum_i (y_i({\\bf x}^{(n)};{\\bf w}) - t_i^{(n)})^2\n$$\nThis objective function is a ... | [
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banbh/little-pythoner | python/Main.ipynb | apache-2.0 | [
"On this page you'll find a series of exercises. We'll be using Python for all the code, but not really. You barely need to know any Python at all. In fact here is all you need to know (at least about Python).\nAll You Need to Know\nNumbers: 0, 1, 2, 3, ... (i.e., no negative numbers or decimals)\nStrings: thing... | [
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ChadFulton/statsmodels | examples/notebooks/generic_mle.ipynb | bsd-3-clause | [
"Maximum Likelihood Estimation (Generic models)\nThis tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples: \n\nProbit model for binary dependent variables\nNegative binomial model for count data\n\nThe GenericLikelihoodModel class eases the process by provid... | [
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aattaran/Machine-Learning-with-Python | titanic/titanic_survival_exploration[1].ipynb | bsd-3-clause | [
"Machine Learning Engineer Nanodegree\nIntroduction and Foundations\nProject: Titanic Survival Exploration\nIn 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Tit... | [
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NathanYee/ThinkBayes2 | code/report03.ipynb | gpl-2.0 | [
"Report03 - Nathan Yee\nThis notebook contains report03 for computational baysian statistics fall 2016\nMIT License: https://opensource.org/licenses/MIT",
"from __future__ import print_function, division\n\n% matplotlib inline\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport math\nimport numpy as np\... | [
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GoogleCloudPlatform/asl-ml-immersion | notebooks/image_models/solutions/2_mnist_models.ipynb | apache-2.0 | [
"MNIST Image Classification with TensorFlow on Cloud AI Platform\nThis notebook demonstrates how to implement different image models on MNIST using the tf.keras API.\nLearning Objectives\n\nUnderstand how to build a Dense Neural Network (DNN) for image classification\nUnderstand how to use dropout (DNN) for image c... | [
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jeiros/Jupyter_notebooks | python/markov_analysis/PyEMMA-API.ipynb | mit | [
"Testing the pyEMMA API",
"import pyemma\npyemma.__version__",
"Now we import a few general packages that we need to start with. The following imports basic numerics and algebra routines (numpy) and plotting routines (matplotlib), and makes sure that all plots are shown inside the notebook rather than in a sepa... | [
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mdiaz236/DeepLearningFoundations | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | [
"Generative Adversarial Network\nIn 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!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp... | [
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ad960009/dist-keras | examples/example_1_analysis.ipynb | gpl-3.0 | [
"Model Development and Evaluation\nJoeri Hermans (Technical Student, IT-DB-SAS, CERN) \nDepartement of Knowledge Engineering \nMaastricht University, The Netherlands\nThis notebook is dedicated to the development and evaluation of a Keras model based on a large preprocessed dataset.",
"%matplotlib... | [
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fionapigott/Data-Science-45min-Intros | neural-networks-101/Neural Networks - Part 1.ipynb | unlicense | [
"Neural Networks - Part 1\n2016-06-17, Josh Montague\nMotivation, a little history, a naive implementation, and a discussion of neural networks.\nLogistic regression\nRecap of the structural pillars of logistic regression for classification (previous RST).\n<img src=\"img/NN-1.jpeg\"> \nLet's see an example wher... | [
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PepSalehi/tuthpc | Untitled5.ipynb | bsd-3-clause | [
"MPI and cluster computing\nWhat is large-scale and cluster computing?\n\n\nMPI: the message passing interface (mpi4py and ...)\n\n\nGPU: graphics processing-based parallelism (pyopencl and pycuda)\n\n\nCloud computing (cloud, mrjob, and Apache's mesos/spark/hadoop/zookeeper/...)\n\n\nWe'll focus on mpi4py, as it's... | [
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JonasHarnau/apc | apc/vignettes/vignette_misspecification.ipynb | gpl-3.0 | [
"Misspecification Tests for Log-Normal and Over-Dispersed Poisson Chain-Ladder Models\nWe replicate the empirical applications in Harnau (2018) in Section 5.\nThe work on this vignette was supported by the European Research Council, grant AdG 694262.\nFirst, we import the package",
"import apc\n\n# Turn off futur... | [
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AaronCWong/phys202-2015-work | days/day20/MoviePy.ipynb | mit | [
"Making Animations using MoviePy\nThis notebook shows how to make animations using MoviePy and Matplotlib. Here are links to the MoviePy documentation and a short tutorial:\n\nhttp://zulko.github.io/moviepy/\nhttp://zulko.github.io/blog/2014/11/29/data-animations-with-python-and-moviepy/\n\nLet's start by importing... | [
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LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day5/MultiwavelengthPhotometry.ipynb | mit | [
"Matched wavelength photometry\nVersion 0.1\nFor today's problem, we will perform matched-aperture photometry in 3 bands on multiple galaxies within a rich galaxy cluster. Ultimately, we will be looking for trends in galaxy colors and other properties as a function of cluster radius.\nNote - we will use astropy for... | [
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gregoryg/cdh-projects | notebooks/jupyter/datascience/K Means Cluster Visualization.ipynb | apache-2.0 | [
"Visualizing clusters in Python\nI am wanting to see the results of clustering methods such as K-Means; this is my playground.\nInitial examples are taken from K Means Clustering in Python",
"import matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom sklearn.cluster import KMeans\nimport sklearn.metrics ... | [
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evanmiltenburg/python-for-text-analysis | Assignments/ASSIGNMENT-1.ipynb | apache-2.0 | [
"Assignment 1: Calculation, Strings, Boolean Expressions and Conditions\nDeadline: Friday, September 9, 2021 before 3pm (submit via Canvas: Block I/Assignment 1) \nThis assignment is not graded, but it is mandatory to submit a version that shows you have given it a serious try. We will check your assignments to mon... | [
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phoebe-project/phoebe2-docs | 2.1/tutorials/spots.ipynb | gpl-3.0 | [
"Binary with Spots\nSetup\nIMPORTANT NOTE: if using spots on contact systems or single stars, make sure to use 2.1.15 or later as the 2.1.15 release fixed a bug affecting spots in these systems.\nLet's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't us... | [
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rohanisaac/spectra | notebooks/find_background.ipynb | gpl-3.0 | [
"Intelligent background subtraction algorithm using wavelets.\nAlso contains implementations of other wavelets and peak-finding code\nRuns 2017/1/9\nFind and Subtract background",
"%matplotlib inline\nfrom __future__ import division\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport sys\nfrom... | [
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profxj/xastropy | xastropy/casbah/CASBAH_galaxy_database.ipynb | bsd-3-clause | [
"Building the CASBAH Galaxy Database (v1.0)\nSDSS\nTargeting\nI am unclear on how to sensibly extract targeting information from the\nSDSS. But this may well be an issue for various analyses.\nExtracting Galaxy data\nThe script build_sdss loops through the listed fields with SDSS\ncoverage and calls the grab_sdss_... | [
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tensorflow/docs-l10n | site/zh-cn/tutorials/load_data/text.ipynb | apache-2.0 | [
"Copyright 2018 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 ... | [
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fastai/course-v3 | nbs/dl1/lesson3-planet.ipynb | apache-2.0 | [
"Multi-label prediction with Planet Amazon dataset",
"%reload_ext autoreload\n%autoreload 2\n%matplotlib inline\n\nfrom fastai.vision import *",
"Getting the data\nThe planet dataset isn't available on the fastai dataset page due to copyright restrictions. You can download it from Kaggle however. Let's see how ... | [
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ernestyalumni/servetheloop | packetDef/podCommands.ipynb | mit | [
"server/udp/podCommands.js - from node.js /JavaScript to Python (object)",
"# find out where we are on the file directory\nimport os, sys\n\nprint( os.getcwd())\nprint( os.listdir(os.getcwd()))",
"The reactGS folder \"mimics\" the actual react-groundstation github repository, only copying the file directory str... | [
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mne-tools/mne-tools.github.io | 0.21/_downloads/6035dcef33422511928bd2247a3d092d/plot_source_power_spectrum_opm.ipynb | bsd-3-clause | [
"%matplotlib inline",
"Compute source power spectral density (PSD) of VectorView and OPM data\nHere we compute the resting state from raw for data recorded using\na Neuromag VectorView system and a custom OPM system.\nThe pipeline is meant to mostly follow the Brainstorm [1]\nOMEGA resting tutorial pipeline <b... | [
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napjon/ds-nd | p1-statistics/project.ipynb | mit | [
"Overview\nIn a Stroop task, participants are presented with a list of words, with each word displayed in a color of ink. The participant’s task is to say out loud the color of the ink in which the word is printed. The task has two conditions: a congruent words condition, and an incongruent words condition. In the ... | [
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zlpure/CS231n | assignment1/features.ipynb | mit | [
"Image features exercise\nComplete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the assignments page on the course website.\nWe have seen that we can achieve reasonable performance on an image clas... | [
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kecnry/autofig | docs/tutorials/3d.ipynb | gpl-3.0 | [
"Plotting in 3D with autofig",
"import autofig\nimport numpy as np\n\n#autofig.inline()\n\nt = np.linspace(0,10,31)\nx = np.random.rand(31)\ny = np.random.rand(31)\nz = np.random.rand(31)",
"By default, autofig uses the z dimension just to assign z-order (so that positive z appears \"on top\")",
"autofig.rese... | [
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goddoe/CADL | session-1/session-1.ipynb | apache-2.0 | [
"Session 1 - Introduction to Tensorflow\n<p class=\"lead\">\nAssignment: Creating a Dataset/Computing with Tensorflow\n</p>\n\n<p class=\"lead\">\nParag K. Mital<br />\n<a href=\"https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\">Creative Applications of Deep Learning w/ T... | [
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quantumlib/Cirq | docs/tutorials/shor.ipynb | apache-2.0 | [
"Copyright 2020 The Cirq Developers",
"#@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 law ... | [
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amueller/scipy-2017-sklearn | notebooks/21.Unsupervised_learning-Non-linear_dimensionality_reduction.ipynb | cc0-1.0 | [
"%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np",
"Manifold Learning\nOne weakness of PCA is that it cannot detect non-linear features. A set\nof algorithms known as Manifold Learning have been developed to address\nthis deficiency. A canonical dataset used in Manifold learning is the\n... | [
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ShubhamDebnath/Coursera-Machine-Learning | Course 5/Dinosaurus Island Character level language model final v3.ipynb | mit | [
"Character level language model - Dinosaurus land\nWelcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to... | [
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keras-team/keras-io | guides/ipynb/keras_tuner/custom_tuner.ipynb | apache-2.0 | [
"Tune hyperparameters in your custom training loop\nAuthors: Tom O'Malley, Haifeng Jin<br>\nDate created: 2019/10/28<br>\nLast modified: 2022/01/12<br>\nDescription: Use HyperModel.fit() to tune training hyperparameters (such as batch size).",
"!pip install keras-tuner -q",
"Introduction\nThe HyperModel class i... | [
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NGSchool2016/ngschool2016-materials | jupyter/ndolgikh/.ipynb_checkpoints/NGSchool_python-checkpoint.ipynb | gpl-3.0 | [
"Set the matplotlib magic to notebook enable inline plots",
"%pylab inline",
"Calculate the Nonredundant Read Fraction (NRF)\nSAM format example:\nSRR585264.8766235 0 1 4 15 35M * 0 0 CTTAAACAATTATTCCCCCTGCAAACATTTTCAAT GGGGGGGGGGGGGGGGGGGGGGFGGGGGGGGGGGG ... | [
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dtrimarco/blog | posts/product_data_071317.ipynb | mit | [
"Pandas for Product Analysis Part 1: Apply and Transform\nPython's pandas package is one of the most powerful tools for data analysis in the Python ecosystem. Built on top of NumPy, it makes working with tabular data quite effective and adds an astounding amount of functionality to your toolkit. Despite its strengt... | [
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authman/DAT210x | Module5/Module5 - Lab5.ipynb | mit | [
"DAT210x - Programming with Python for DS\nModule5- Lab5",
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nmatplotlib.style.use('ggplot') # Look Pretty",
"A Convenience Function",
"def plotDecisionBoundary(model, X, y):\n fig = plt.figure()\n ax = fig.add_... | [
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NREL/bifacial_radiance | docs/tutorials/4 - Medium Level Example - Debugging your Scene with Custom Objects (Fixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject).ipynb | bsd-3-clause | [
"4 - Medium Level Example - Debugging your Scene with Custom Objects\nFixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject\nThis journal has examples of various things, some which hav ebeen covered before and some in more depth:\n<ul>\n <li> Running a fixed_tilt simulation beginning to end. </li>\n ... | [
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thehackerwithin/berkeley | code_examples/python_mayavi/mayavi_intermediate.ipynb | bsd-3-clause | [
"%matplotlib qt\nimport numpy as np\nfrom mayavi import mlab\n\nfrom scipy.integrate import odeint",
"Lorenz Attractor - 3D line and point plotting demo\nLorenz attractor is a 3D differential equation that we will use to demonstrate mayavi's 3D plotting ability. We will look at some ways to make plotting lots of ... | [
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johnnyliu27/openmc | examples/jupyter/mgxs-part-iii.ipynb | mit | [
"This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n\nCalcu... | [
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mssalvador/Fifa2018 | Teknisk Tirsdag Tutorial (Supervised Learning).ipynb | apache-2.0 | [
"Teknisk Tirsdag: Supervised Learning\nI denne opgave skal vi bruge Logistisk Regression til at forudsige hvilke danske fodboldspillere der egentlig kunne spille for en storklub.",
"# Run the datacleaning notebook to get all the variables\n%run 'Teknisk Tirsdag - Data Cleaning.ipynb'",
"Efter at have hentet vor... | [
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amkatrutsa/MIPT-Opt | Spring2021/intro_gd.ipynb | mit | [
"Введение в численные методы оптимизации (Ю. Е. Нестеров Введение в выпуклую оптимизацию, гл. 1 $\\S$ 1.1)\n\nОбзор материала весеннего семестра\nПостановка задачи\nОбщая схема решения\nСравнение методов оптимизации\nМетоды одномерной минимизации\n\nОбзор материала весеннего семестра\nТакже на странице курса.\n\nМе... | [
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LedaLima/incubator-spot | spot-oa/oa/proxy/ipynb_templates/Advanced_Mode_master.ipynb | apache-2.0 | [
"Apache Spot's Ipython Advanced Mode\nProxy\nThis guide provides examples about how to request data, show data with some cool libraries like pandas and more.\nImport Libraries\nThe next cell will import the necessary libraries to execute the functions. Do not remove",
"import datetime\nimport pandas as pd\nimport... | [
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alfkjartan/nvgimu | notebooks/.ipynb_checkpoints/Get started-checkpoint.ipynb | gpl-3.0 | [
"Getting started with the analysis of nvg data\nThis notebook assumes that data exists in a database in the hdf5 format. For instructions how to set up the database with data see [../readme.md].\nImport modules",
"import numpy as np\nimport matplotlib.pyplot as plt\nimport nvg.ximu.ximudata as ximudata\n%matplotl... | [
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fionapigott/Data-Science-45min-Intros | count-min-101/CountMinSketch.ipynb | unlicense | [
"Basic Idea of Count Min sketch\nWe map the input value to multiple points in a relatively small output space. Therefore, the count associated with a given input will be applied to multiple counts in the output space. Even though collisions will occur, the minimum count associated with a given input will have some ... | [
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tensorflow/docs-l10n | site/zh-cn/lattice/tutorials/shape_constraints_for_ethics.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 ... | [
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Jackie789/JupyterNotebooks | CorrectingForAssumptions.ipynb | gpl-3.0 | [
"Engineering Existing Data to Follow Multivariate Linear Regression Assumptions\nJackie Zuker\nAssumptions:\n\nLinear relationship - Features should have a linear relationship with the outcome\nMultivariate normality - The error from the model should be normally distributed\nHomoscedasticity - The distribution of e... | [
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pycroscopy/pycroscopy | jupyter_notebooks/image_registration.ipynb | mit | [
"<font size = \"5\"> Image Registration </font>\n<hr style=\"height:1px;border-top:4px solid #FF8200\" />\n\nby \nGerd Duscher and Matthew. F. Chisholm\nMaterials Science & Engineering<br>\nJoint Institute of Advanced Materials<br>\nThe University of Tennessee, Knoxville\nRegistration of a Stack of Images\nWe us th... | [
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ImAlexisSaez/deep-learning-specialization-coursera | course_1/week_3/assignment_1/planar_data_classification_with_one_hidden_layer_v1.ipynb | mit | [
"Planar data classification with one hidden layer\nWelcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \nYou will learn how to:\n- Implemen... | [
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"mar... |
dtamayo/rebound | ipython_examples/HyperbolicOrbits.ipynb | gpl-3.0 | [
"Loading Hyperbolic Orbits into REBOUND\nImagine we have a table of orbital elements for comets (kindly provided by Toni Engelhardt).",
"from io import StringIO\nimport numpy as np\nimport rebound\nepoch_of_elements = 53371.0 # [MJD, days]\nc = StringIO(u\"\"\"\n# id e q[AU] i[deg] Omega[de... | [
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getsmarter/bda | module_4/M4_NB3_NetworkClustering.ipynb | mit | [
"<div align=\"right\">Python 3.6 Jupyter Notebook</div>\n\nFinding connected components using clustering\n<br><div class=\"alert alert-warning\">\n<b>Note that this notebook contains advanced exercises applicable only to students who wish to deepen their understanding and qualify for bonus marks on the course.</b>... | [
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mjbommar/cscs-530-w2016 | samples/cscs530-w2015-midterm-sample1.ipynb | bsd-2-clause | [
"Midterm\nGoal\nI will explore whether a network-theory driven approach shown to improve the efficiency of an agricultural extension program is sensitive to the models and parameters originally used.\nJustification\nSocial networks have been shown to be important vehicles for the transmission of new agricultural me... | [
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OceanPARCELS/parcels | parcels/examples/tutorial_sampling.ipynb | mit | [
"Field sampling tutorial\nThe particle trajectories allow us to study fields like temperature, plastic concentration or chlorophyll from a Lagrangian perspective. \nIn this tutorial we will go through how particles can sample Fields, using temperature as an example. Along the way we will get to know the parcels cla... | [
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Luke035/dlnd-lessons | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | [
"Generative Adversarial Network\nIn 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!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp... | [
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"mar... |
opengridcc/opengrid | notebooks/Multi-variable Linear Regression Demo.ipynb | apache-2.0 | [
"Multi-variable linear regression\nThe multivariable linear regression analysis is used to create a model of a single variable, typically an energy consumption. We call this the dependent variable. The model is constructed as a linear combination of explanatory variables, like weather measurements or occupation. M... | [
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] |
woobe/h2o_tutorials | introduction_to_machine_learning/py_04b_classification_ensembles.ipynb | mit | [
"Machine Learning with H2O - Tutorial 4b: Classification Models (Ensembles)\n<hr>\n\nObjective:\n\nThis tutorial explains how to create stacked ensembles of classification models for better out-of-bag performance.\n\n<hr>\n\nTitanic Dataset:\n\nSource: https://www.kaggle.com/c/titanic/data\n\n<hr>\n\nSteps:\n\nBuil... | [
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GoogleCloudPlatform/asl-ml-immersion | notebooks/feature_engineering/solutions/5_tftransform_taxifare.ipynb | apache-2.0 | [
"TfTransform #\nLearning Objectives\n1. Preproccess data and engineer new features using TfTransform \n1. Create and deploy Apache Beam pipeline \n1. Use processed data to train taxifare model locally then serve a prediction\nOverview\nWhile Pandas is fine for experimenting, for operationalization of your workflow ... | [
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BoasWhip/Black | Notebook/M269 Unit 4 Notes -- Search.ipynb | mit | [
"4. Searching\n4.1 Searching Lists\nAlgorithm: Selection\nFinding the median of a collection of numbers is selection problem with $k=(n+1)/2$ if $n$ is odd (and, if $n$ is even, the median is the mean of the $k$th and $(k+1)$th smallest items, where $k=n/2$).\nInitial Insight\nChoose a value from $S$, to be used as... | [
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CartoDB/cartoframes | docs/examples/data_visualization/layers/add_multiple_layers.ipynb | bsd-3-clause | [
"Add Multiple Layers\nIn this example, three Layers are added to a Map. Notice the draw order and default symbology for each.\nFor more information, run help(Layer)",
"from cartoframes.auth import set_default_credentials\nfrom cartoframes.viz import Map, Layer\n\nset_default_credentials('cartoframes')\n\nMap([\n ... | [
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kenjisato/intro-macro | doc/python/Optimal Growth (Euler).ipynb | mit | [
"Computing the Optimal Grwoth Model by the Euler Equation",
"%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt",
"Model\nLet's consider the optimal growth model,\n\\begin{align}\n &\\max\\int_{0}^{\\infty}e^{-\\rho t}u(c(t))dt \\\n &\\text{subject to} \\\n &\\qquad\\dot{k}(t)=f(k(... | [
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ccasotto/rmtk | rmtk/vulnerability/derivation_fragility/equivalent_linearization/lin_miranda_2008/lin_miranda_2008.ipynb | agpl-3.0 | [
"Lin and Miranda (2008)\nThis method, described in Lin and Miranda (2008), estimates the maximum inelastic displacement of an existing structure based on the maximum elastic displacement response of its equivalent linear system without the need of iterations, based on the strength ratio. The equivalent linear syste... | [
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GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/solutions/1_explore_data.ipynb | apache-2.0 | [
"Explore and create ML datasets\nLearning Objectives\n* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n* Visualize your dataset using the Seaborn library\n* Inspect and clean-up the dataset for future ML model training\n* Create a benchmark to judge future ML model performance off of\nOverview\... | [
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"mar... |
fangohr/oommf-python | new/notebooks/fmr_standard_problem.ipynb | bsd-2-clause | [
"FMR standard problem\nAuthor: Marijan Beg\nDate: 11 May 2016\nProblem specification\nWe choose a cuboidal thin film permalloy sample measuring $120 \\times 120 \\times 10 \\,\\text{nm}^{3}$. The choice of a cuboid is important as it ensures that the finite difference method employed by OOMMF does not introduce err... | [
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gangadhara691/gangadhara691.github.io | P3 wrangle_data/DataWrangling_ganga.ipynb | mit | [
"P3 - Data Wrangling with MongoDB\nOpenStreetMap Project Data Wrangling with MongoDB\nGangadhara Naga Sai<a name=\"top\"></a>\nData used -<a href=https://mapzen.com/metro-extracts/> MapZen Weekly OpenStreetMaps Metro Extracts</a>\nMap Areas:\n These two maps are selected since ,right now i am living at Hoodi,Beng... | [
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"mar... |
taliamo/Final_Project | organ_pitch/Scripts/.ipynb_checkpoints/upload_env_data-checkpoint.ipynb | mit | [
"T. Martz-Oberlander, 2015-11-12, CO2 and Speed of Sound\nFormatting ENVIRONMENTAL CONDITIONS pipe organ data for Python operations\nNOTE: Here, pitch and frequency are used interchangeably to signify the speed of sound from organ pipes.\nThe entire script looks for mathematical relationships between CO2 concentrat... | [
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eds-uga/csci1360e-su16 | lectures/L9.ipynb | mit | [
"Lecture 9: Introduction to Functions\nCSCI 1360E: Foundations for Informatics and Analytics\nOverview and Objectives\nIn this lecture, we'll introduce the concept of functions, critical abstractions in nearly every modern programming language. Functions are important for abstracting and categorizing large codebase... | [
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ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | [
"ES-DOC CMIP6 Model Properties - Land\nMIP Era: CMIP6\nInstitute: TEST-INSTITUTE-3\nSource ID: SANDBOX-1\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 F... | [
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"mar... |
CodeNeuro/notebooks | worker/notebooks/thunder/tutorials/factorization.ipynb | mit | [
"Factorization\nFactorization algorithms are useful for data-driven decomposition of spatial and temporal data, for example, to recover spatial patterns with similar temporal profiles. Here, we show how to use some of the factorization algorithms in Thunder and visualize the results.\nSetup plotting",
"%matplotli... | [
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"mar... |
wheeler-microfluidics/teensy-minimal-rpc | teensy_minimal_rpc/notebooks/dma-examples/Example - [BROKEN] Periodic multi-channel ADC multiple samples using DMA and PIT.ipynb | gpl-3.0 | [
"NB Cannot use PIT to trigger periodic DMA due to hardware bug\nSee here.\nTry using PDB instead??",
"import pandas as pd\n\ndef get_pdb_divide_params(frequency, F_BUS=int(48e6)):\n mult_factor = np.array([1, 10, 20, 40])\n prescaler = np.arange(8)\n\n clock_divide = (pd.DataFrame([[i, m, p, m * (1 << p)... | [
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