repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
GoogleCloudPlatform/asl-ml-immersion | notebooks/image_models/labs/4_tpu_training.ipynb | apache-2.0 | import os
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
os.environ["BUCKET"] = BUCKET
"""
Explanation: Transfer Learning on TPUs
In the <a href="3_tf_hub_transfer_learning.ipynb">previous notebook</a>, we learned how to do transfer learning with TensorFlow Hub. In this noteb... |
AllenDowney/ThinkStats2 | solutions/chap11soln.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
analysiscenter/dataset | examples/experiments/zeroing_of_weights/zeroing_of_weights.ipynb | apache-2.0 | import sys
import copy
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from tqdm import tqdm_notebook as tqn
%matplotlib inline
sys.path.append('../../..')
from simple_model import ConvModel
from batchflow.opensets import MNIST
from batchflow import V, B
... |
IS-ENES-Data/submission_forms | dkrz_forms/Templates/CMIP6_submission_form.ipynb | apache-2.0 | # Evaluate this cell to identifiy your form
from dkrz_forms import form_widgets, form_handler, checks
form_infos = form_widgets.show_selection()
# Evaluate this cell to generate your personal form instance
form_info = form_infos[form_widgets.FORMS.value]
sf = form_handler.init_form(form_info)
form = sf.sub.entity_o... |
RoebideBruijn/datascience-intensive-course | project/notebooks/project-milestone-report.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
sns.set_style('white')
"""
Explanation: Milestone report
Instruction
You have proposed a project, collected a data set, cleaned up the data and explored it with descr... |
harmsm/pythonic-science | chapters/00_inductive-python/04_loops.ipynb | unlicense | for i in range(10):
print(i)
for i in range(15):
print(i)
"""
Explanation: Loops
Loops let you execute code over and over.
Basic Loops
Predict
What will this code do?
End of explanation
"""
for i in range(11):
print(i)
"""
Explanation: Summarize
What does the range function do?
Modify
Change the cell ... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/hadgem3-gc31-hm/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hm', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NERC
Source ID: HADGEM3-GC31-HM
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
HeyIamJames/bikeshare | BikeShareStep3.ipynb | mit | station_counts = usage.groupby('station_start')['station_start'].count()
station_rentals_per_day = DataFrame()
station_rentals_per_day['rentals'] = station_counts.values / 366.0
station_rentals_per_day['station'] = station_counts.index
station_rentals_per_day.head()
"""
Explanation: To start with, we'll need to com... |
kwhanalytics/rdtools | docs/degradation_example.ipynb | mit | from datetime import timedelta
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pvlib
%matplotlib inline
#Update the style of plots
import matplotlib
matplotlib.rcParams.update({'font.size': 12,
'figure.figsize': [4.5, 3],
'lines.marker... |
mne-tools/mne-tools.github.io | dev/_downloads/da444a4db06576d438b46fdb32d045cd/topo_compare_conditions.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_evoked_topo
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Co... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
JAmarel/Phys202 | Optimization/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
from scipy.optimize import minimize
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
return -a*x**2 + b*x**4
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
ass... |
huseinzol05/Deep-Learning-Tensorflow | Seq-to-Seq/Basic-Seq2Seq/basic sequence-to-sequence.ipynb | mit | with open('/home/huseinzol05/AI/chat/Assassination Classroom The Graduation (2016) BluRay 720p 900MB Ganool is.srt', 'r') as fopen:
text = fopen.read().split('\n')
text = filter(None, text)
# any first character in a string is digit or '\r' or '<', we remove the string
# but we must start from the back, or later p... |
tensorflow/docs-l10n | site/ja/hub/tutorials/tf2_semantic_approximate_nearest_neighbors.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
opesci/devito | examples/mpi/overview.ipynb | mit | import ipyparallel as ipp
c = ipp.Client(profile='mpi')
"""
Explanation: Prerequisites
This notebook contains examples which are expected to be run with exactly 4 MPI processes; not because they wouldn't work otherwise, but simply because it's what their description assumes. For this, you need to:
Install an MPI dist... |
rajul/tvb-library | tvb/simulator/demos/Monitoring with transformations.ipynb | gpl-2.0 | sim = simulator.Simulator(
model=models.Generic2dOscillator(),
connectivity=connectivity.Connectivity(load_default=True),
coupling=coupling.Linear(),
integrator=integrators.EulerDeterministic(),
monitors=Raw(pre_expr='V;W;V**2;W-V', post_expr=';;sin(mon);exp(mon)'))
sim.configure()
ts, ys = [], []... |
caisq/tensorflow | tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
ML4DS/ML4all | TM3.Topic_Models_with_MLlib/ExB3_TopicModels/.ipynb_checkpoints/TM_Exam2-checkpoint.ipynb | mit | #nltk.download()
mycorpus = nltk.corpus.reuters
"""
Explanation: Master Telefónica Big Data & Analytics
Prueba de Evaluación del Tema 4:
Topic Modelling.
Date: 2016/04/10
Para realizar esta prueba es necesario tener actualizada la máquina virtual con la versión más reciente de MLlib.
Para la actualización, debe segui... |
lekshmideepu/nest-simulator | doc/userdoc/model_details/aeif_models_implementation.ipynb | gpl-2.0 | # Install assimulo package in the current Jupyter kernel
import sys
!{sys.executable} -m pip install assimulo
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 6)
"""
Explanation: NEST implementation of the aeif models
Hans E... |
leriomaggio/python-in-a-notebook | 07 More Functions.ipynb | mit | def thank_you(name):
# This function prints a two-line personalized thank you message.
print("\nYou are doing good work, %s!" % name)
print("Thank you very much for your efforts on this project.")
thank_you('Adriana')
thank_you('Billy')
thank_you('Caroline')
"""
Explanation: More Functions
Earlier we ... |
kuo77122/deep-learning-nd | Lesson15-TFLearn/Sentiment Analysis with TFLearn.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
pycroscopy/pycroscopy | jupyter_notebooks/AFM_simulations/IntroductionToSimulations/IntroToAFMSimulations.ipynb | mit | from __future__ import division, print_function, absolute_import, unicode_literals
import os
import numpy
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import Image
path = os.getcwd()
fig1 = path + '/Fig1.jpg'
Image(filename=fig1)
"""
Explanation: Introduction to dynamic AFM simulations
Cont... |
rflamary/POT | notebooks/plot_gromov_barycenter.ipynb | mit | # Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import numpy as np
import scipy as sp
import scipy.ndimage as spi
import matplotlib.pylab as pl
from sklearn import manifold
from sklearn.decomposition import PCA
import ot
"""
Explanation: Gromov... |
nfaggian/notebooks | Introductions/.ipynb_checkpoints/Tutorial - 1.0 - Introduction-checkpoint.ipynb | apache-2.0 | print("Hello World")
"""
Explanation: Welcome to the interactive Python session.
This notebook series is intended as a very high-level introduction to python and tools, for a more comprehensive set of material refer to the following reference:
https://wiki.python.org/moin/BeginnersGuide/Programmers
Also, consider work... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/example_pano.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from skimage import io, transform
from skimage.color import rgb2gray
from skdemo import imshow_all
ic = io.ImageCollection('../images/pano/DFM_*')
"""
Explanation: Note: This example has been significantly expanded and enhanced. The new, recommended version is locate... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from IPython.display import display, Latex
"""
Explanation: Detrending, Stylized Facts and the Business Cycle
In an influential article, Harvey and Jaeger (1993) described the use of unobserved comp... |
opencobra/cobrapy | documentation_builder/gapfilling.ipynb | gpl-2.0 | from cobra.io import load_model
from cobra.flux_analysis import gapfill
model = load_model("iYS1720")
"""
Explanation: Gapfillling
Model gap filling is the task of figuring out which reactions have to be added to a model to make it feasible. Several such algorithms have been reported e.g. Kumar et al. 2009 and Reed et... |
therealAJ/python-sandbox | data-science/learning/ud2/Part 1 Exercise Solutions/Ecommerce Purchases Exercise .ipynb | gpl-3.0 | import pandas as pd
ecom = pd.read_csv('Ecommerce Purchases')
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>
Ecommerce Purchases Exercise
In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the dire... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_evoked_delayed_ssp.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Create... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/rome/update_from_v339_to_v341.ipynb | gpl-3.0 | import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '339'
NEW_VERSION = '341'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(... |
walkon302/CDIPS_Recommender | notebook_versions/Recommendor_Method_Nathans_v1.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
%matplotlib inline
"""
Explanation: Recommendation Method 1: Most similar items to user's previous views
Algorithm
Offline:
1. For each item, calculate features on trained neural network $ f_j $
2. For ... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/vresm-1-0/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'vresm-1-0', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: VRESM-1-0
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
ad960009/dist-keras | examples/mnist_analysis.ipynb | gpl-3.0 | !(date +%d\ %B\ %G)
"""
Explanation: MNIST Analysis with Distributed Keras
Joeri Hermans (Technical Student, IT-DB-SAS, CERN)
Departement of Knowledge Engineering
Maastricht University, The Netherlands
End of explanation
"""
%matplotlib inline
import numpy as np
from keras.optimizers import *
fro... |
myuuuuun/NumericalCalculation | chapter2/Chapter2.ipynb | mit | #!/usr/bin/python
#-*- encoding: utf-8 -*-
"""
Copyright (c) 2015 @myuuuuun
https://github.com/myuuuuun/NumericalCalculation
This software is released under the MIT License.
"""
%matplotlib inline
from __future__ import division, print_function
import math
import numpy as np
import functools
import sys
import types
im... |
tensorflow/docs-l10n | site/ja/agents/tutorials/10_checkpointer_policysaver_tutorial.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
xlbaojun/Note-jupyter | 05其他/pandas文档-zh-master/检索 ,查询数据.ipynb | gpl-2.0 | import numpy as np
import pandas as pd
"""
Explanation: 检索，查询数据
这一节学习如何检索pandas数据。
End of explanation
"""
dates = pd.date_range('1/1/2000', periods=8)
dates
df = pd.DataFrame(np.random.randn(8,4), index=dates, columns=list('ABCD'))
df
panel = pd.Panel({'one':df, 'two':df-df.mean()})
panel
"""
Explanation: Python... |
mjlong/openmc | docs/source/pythonapi/examples/post-processing.ipynb | mit | from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
import openmc
from openmc.statepoint import StatePoint
%matplotlib inline
"""
Explanation: This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and p... |
ImAlexisSaez/deep-learning-specialization-coursera | course_1/week_4/assignment_1/building_your_deep_neural_network_step_by_step_v3.ipynb | mit | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v2 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... |
dbenn/photometry_tools | SkyCoordAperturePhotometry.ipynb | mit | import os
from random import random
# TODO: shouldn't need ordered dictionary now either
from collections import OrderedDict
import numpy as np
import pandas as pd
from astropy.io import fits
from astropy.visualization import astropy_mpl_style
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
fro... |
jessicaowensby/We-Rise-Keras | notebooks/05_Transfer_Learning.ipynb | apache-2.0 | from __future__ import print_function
import datetime
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing import image
from keras.applications.imagenet_utils... |
claudiuskerth/PhDthesis | Data_analysis/SNP-indel-calling/dadi/dadiExercises/example_YRI_CEU.ipynb | mit | %pwd
import dadi
"""
Explanation: Table of Contents
<p><div class="lev2 toc-item"><a data-toc-modified-id="Load-dadi-module-01" href="#Load-dadi-module"><span class="toc-item-num">0.1 </span>Load dadi module</a></div><div class="lev2 toc-item"><a data-toc-modified-id="Import-custom-models-02" href="#Import... |
kingmolnar/DataScienceProgramming | 02-Python/Introduction_class.ipynb | cc0-1.0 | x = 7+3
print x, type(x)
print x
x = (x+5)*0.5
print x, type(x)
type(x)
"""
Explanation: <p style="text-align:right;color:red;font-weight:bold;font-size:16pt;padding-bottom:20px">Please, rename this notebook before editing!</p>
The Programming Language Python
References
Here are some references to freshen up on co... |
ML4DS/ML4all | U_lab1.Clustering/Lab_ShapeSegmentation_draft/LabSessionClustering[Conflicto].ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import imread
"""
Explanation: Lab Session: Clustering algorithms for Image Segmentation
Author: Jesús Cid Sueiro
Jan. 2017
End of explanation
"""
name = "birds.jpg"
name = "Seeds.jpg"
birds = imread("Images/" + name)
birdsG = np.... |
ireapps/cfj-2017 | exercises/06. pandas? pandas! (Part 2)-working.ipynb | mit | # to avoid errors with the FDA files, we're going to specify the encoding
# as latin_1, which is common with gov't data
# so it's a decent educated guess to start with
# main dataframe
# country code lookup dataframe
# refusal code lookup dataframe
# specify that the 'ASC_ID' column comes in as a string
# because ... |
albahnsen/PracticalMachineLearningClass | notebooks/11-Ensembles_Bagging.ipynb | mit | import numpy as np
# set a seed for reproducibility
np.random.seed(1234)
# generate 1000 random numbers (between 0 and 1) for each model, representing 1000 observations
mod1 = np.random.rand(1000)
mod2 = np.random.rand(1000)
mod3 = np.random.rand(1000)
mod4 = np.random.rand(1000)
mod5 = np.random.rand(1000)
# each m... |
tpin3694/tpin3694.github.io | python/pandas_list_unique_values_in_column.ipynb | mit | # Import modules
import pandas as pd
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
"""
Explanation: Title: List Unique Values In A Pandas Column
Slug: pandas_list_unique_values_in_column
Summary: List Unique Valu... |
wuafeing/Python3-Tutorial | 02 strings and text/02.03 match strings with shell wildcard.ipynb | gpl-3.0 | from fnmatch import fnmatch, fnmatchcase
fnmatch("foo.txt", "*.txt")
fnmatch("foo.txt", "?oo.txt")
fnmatch("Dat45.csv", "Dat[0-9]*")
names = ["Dat1.csv", "Dat2.csv", "config.ini", "foo.py"]
[name for name in names if fnmatch(name, "Dat*.csv")]
"""
Explanation: Previous
2.3 用Shell通配符匹配字符串
问题
你想使用 Unix Shell 中常用的通配符(... |
cliburn/sta-663-2017 | notebook/18E_Spark_SQL.ipynb | mit | from pyspark import SparkContext, SparkConf
conf = (SparkConf()
.setAppName('SparkSQL')
.setMaster('local[*]'))
sc = SparkContext(conf=conf)
from pyspark.sql import SQLContext
sqlc = SQLContext(sc)
"""
Explanation: Spark SQL
Official Documentation
A tour of the Spark SQL library, the spark-csv pac... |
Leguark/GeMpy | Prototype Notebook/.ipynb_checkpoints/Sandstone Project_legacy-checkpoint.ipynb | mit | T.jacobian?
# Setting extend, grid and compile
# Setting the extent
sandstone = GeoMig.Interpolator(696000,747000,6863000,6950000,-20000, 2000,
range_var = np.float32(110000),
u_grade = 9) # Range used in geomodeller
# Setting resolution of the grid
sands... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-200902020333.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Remote WMI ActiveScriptEventConsumers
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2020/09/02 |
| modification date | 2020/09/20 |
| playbook related ... |
davicsilva/dsintensive | notebooks/eda-miniprojects/racial_disc/sliderule_dsi_inferential_statistics_exercise_2-Copy1.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Examining Racial Discrimination in the US Job Market
Background
Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined... |
HuanglabPurdue/NCS | clib/jupyter_notebooks/ncs_tensorflow_simulation.ipynb | gpl-3.0 | import matplotlib
import matplotlib.pyplot as pyplot
import numpy
import os
import time
# tensorflow
import tensorflow as tf
from tensorflow.python.training import adagrad
from tensorflow.python.training import adam
from tensorflow.python.training import gradient_descent
# python3-6 NCS. This provideds the OTF and th... |
neurohackweek/kids_rsfMRI_motion | split_half_reliability/Development_Abide_Motion_Wrapper.ipynb | mit | import matplotlib.pylab as plt
%matplotlib inline
from matplotlib import ticker
from glob import glob
import numpy as np
import os
import pandas as pd
from scipy.stats import linregress, pearsonr, spearmanr
import nibabel as nib
import urllib
import seaborn as sns
sns.set_context('notebook', font_scale=2)
sns.set_style... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI: Vertex AI Migration: Custom XGBoost model with pre-built training container
<table a... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/1_core_tensorflow.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
print(tf.__version__)
"""
Explanation: Getting started with TensorFlow
Learning... |
idekerlab/cyrest-examples | notebooks/cookbook/Python-cookbook/Export.ipynb | mit | # import data from url
from py2cytoscape.data.cyrest_client import CyRestClient
# Create REST client for Cytoscape
cy = CyRestClient()
# Reset current session for fresh start
cy.session.delete()
# Load a sample network
network = cy.network.create_from('http://chianti.ucsd.edu/~kono/data/galFiltered.sif')
# Apply la... |
facaiy/book_notes | machine_learning/tree/decision_tree/presentation.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
data = load_iris()
# 准备特征数据
X = pd.DataFrame(data.data,
columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])
# 准备标签数据
y = pd.DataFrame(data.target, columns=['target'])
y.replace(to_replace=range(3), value=data.target_names, inplace=True)
# 组... |
kenjisato/intro-macro | doc/python/Optimal Growth (Euler).ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Computing the Optimal Grwoth Model by the Euler Equation
End of explanation
"""
alpha = 0.3
delta = 0.05
rho = 0.1
theta = 1
A = 1
def f(x):
return A * x**alpha
kgrid = np.linspace(0.0, 7.5, 300)
fig, ax = plt.subplots(1,1)... |
ramseylab/networkscompbio | class06_degdist_python3.ipynb | apache-2.0 | import pandas
import collections
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import igraph
"""
Explanation: CS446/546 - Class Session 6 - Degree Distribution
In this class session we are going to plot the degree distribution of the undirected human
protein-protein interaction network (PPI), w... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/02-Introduction to PyTorch/Part 4 - Fashion-MNIST.ipynb | apache-2.0 | import torch
from torchvision import datasets, transforms
import helper
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Downl... |
Danghor/Formal-Languages | Ply/Conflicts-Resolved.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'NUMBER' ]
def t_NUMBER(t):
r'0|[1-9][0-9]*'
t.value = float(t.value)
return t
literals = ['+', '-', '*', '/', '^', '(', ')']
t_ignore = ' \t'
def t_newline(t):
r'\n+'
t.lexer.lineno += t.value.count('\n')
def t_error(t):
print(f"Illegal character '{t.valu... |
jhconning/Dev-II | notebooks/EdgeworthProduction.ipynb | bsd-3-clause | edgeplot(50)
"""
Explanation: Edgeworth Box: Efficiency in production allocation
Efficiency in production
Consider a small-open economy with two production sectors -- agriculture and manufacturing -- with production in each sector taking place with constant returns to scale production functions. Producers in the agri... |
mne-tools/mne-tools.github.io | 0.17/_downloads/fd79fe12dec0d8ba3f96e5d55db03054/plot_ecog.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from mayavi import mlab
import mne
from mne.viz import plot_alignment, snapshot_brain_montage
print(__doc__)
""... |
SeismicPi/SeismicPi | Lessons/Lesson 5/Lesson 5.ipynb | mit | #find_position here
def find_position(a, t, s):
return (a-s*t)/2.0;
"""
Explanation: Lesson 5
In this lesson, we will use the find_position function from the previous lesson to implement a one-dimensional version of piTap. You can tap anywhere along a line and have the program predict where you've tapped!
Copy and... |
gagneurlab/concise | nbs/effect_prediction.ipynb | mit | from effect_demo_setup import *
from concise.models import single_layer_pos_effect as concise_model
import numpy as np
# Generate training data for the model, use a 1000bp sequence
param, X_feat, X_seq, y, id_vec = load_example_data(trim_seq_len = 1000)
# Generate the model
dc = concise_model(pooling_layer="sum",
... |
ajgpitch/qutip-notebooks | examples/spin-chain-model.ipynb | lgpl-3.0 | %matplotlib inline
from qutip.qip.circuit import QubitCircuit
from qutip.qip.operations import gate_sequence_product
import numpy as np
"""
Explanation: QuTiP example: Physical implementation of Spin Chain Qubit model
Author: Anubhav Vardhan (anubhavvardhan@gmail.com)
Numerical simulation added by Boxi Li (etamin1201... |
KiranArun/A-Level_Maths | Integration/Integration.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Integration
End of explanation
"""
x = np.linspace(-10, 10, 201)
def f(x):
return x**2
y = f(x)
fig, ax = plt.subplots(1, figsize=(8,4))
ax.plot(x,y, 'g', label='line')
ax.fill_between(x,y, color='blue', alpha=0.3, label='area under graph')... |
phoebe-project/phoebe2-docs | 2.1/tutorials/fti.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplo... |
BDannowitz/polymath-progression-blog | jlab-ml-lunch-2/notebooks/00-Data-Exploration.ipynb | gpl-2.0 | %matplotlib widget
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import imageio
"""
Explanation: JLab ML Lunch 2 - Data Exploration
Second ML challenge hosted
On October 30th, a test dataset will be released, and predictions must be submitted within 24... |
davidwhogg/Avast | notebooks/fakedata2.ipynb | mit | def oned_gaussian(xs, mm, sig):
return np.exp(-0.5 * (xs - mm) ** 2 / sig ** 2) / np.sqrt(2. * np.pi * sig)
def make_synth(rv, xs, ds, ms, sigs):
"""
`rv`: radial velocity in m/s (or same units as `c` above
`xs`: `[M]` array of wavelength values
`ds`: depths at line centers
`ms`: locations of t... |
CyberCRI/dataanalysis-herocoli-redmetrics | v1.52.2/Tests/2.5 Google form analysis - PCA.ipynb | cc0-1.0 | %run "../Functions/2.1 Sampling.ipynb"
"""
Explanation: Google form analysis tests
Purpose: determine in what extent the current data can accurately describe correlations, underlying factors on the score.
Especially concerning the answerTemporalities[0] groups: are there underlying groups explaining the discrepancies ... |
SealedSaint/CarND-Term1-P1 | P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
jan-rybizki/Chempy | tutorials/7-Acessing Chempy paper 1 abundance tracks.ipynb | mit | %pylab inline
"""
Explanation: This notebook will help you to access the chemical abundance tracks that you see in paper 1 figure 17 but for all elements. You can use it to compare it to your own model/data.
End of explanation
"""
# Single zone models for sun, arcturus and cas with default and alternative yield set
... |
prashantas/MyDataScience | DeepNetwork/deeplearningai_AndrewNG/Python+Basics+With+Numpy+v3.ipynb | bsd-2-clause | ### START CODE HERE ### (≈ 1 line of code)
test = 'Hello World'
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help fami... |
walkon302/CDIPS_Recommender | notebooks/_old/Exploring_Original_Dataset.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from IPython.core.debugger import Tracer
import seaborn as sns
%matplotlib inline
"""
Explanation: Setup
End of explanation
"""
import tensorflow as tf
import sklearn
import h5py
import keras
from keras.preprocessing import image
f... |
mne-tools/mne-tools.github.io | 0.20/_downloads/ad79868fcd6af353ce922b8a3a2fc362/plot_30_info.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
"""
Explanation: The Info data structure
This tutori... |
tensorflow/cloud | src/python/tensorflow_cloud/core/tests/examples/dogs_classification.ipynb | apache-2.0 | !pip install tensorflow-cloud
import datetime
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_cloud as tfc
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
"""
Explanation: TensorFlow Clo... |
geoneill12/phys202-2015-work | assignments/assignment04/MatplotlibExercises.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
"""
a = np.random.randn(2,100)
x = a[0,:]
y = a[1,:]
plt.figure(figsize=(5,5))
plt.scatter(x, y, color='green', alpha = 0.9)
plt.xlabel('Random Data')
plt.ylabel('y(R... |
ozorich/phys202-2015-work | assignments/assignment02/ProjectEuler59.ipynb | mit | assert 65 ^ 42 == 107
assert 107 ^ 42 == 65
assert ord('a') == 97
assert chr(97) == 'a'
"""
Explanation: Project Euler: Problem 59
https://projecteuler.net/problem=59
Each character on a computer is assigned a unique code and the preferred standard is ASCII (American Standard Code for Information Interchange). For exa... |
jpilgram/phys202-2015-work | assignments/assignment03/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
Kidel/In-Codice-Ratio-OCR-with-CNN | Notebooks/03_Mnist-Dataset-Expansion.ipynb | apache-2.0 | import os.path
from IPython.display import Image
from util import Util
u = Util()
import numpy as np
# Explicit random seed for reproducibility
np.random.seed(1337)
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from kera... |
nick-youngblut/SIPSim | ipynb/theory/non-equilibrium_calcs.ipynb | mit | %load_ext rpy2.ipython
%%R
library(dplyr)
library(tidyr)
library(ggplot2)
library(gridExtra)
%%R
GC2MW = function(x){
A = 313.2
T = 304.2
C = 289.2
G = 329.2
GC = G + C
AT = A + T
x = x / 100
x*GC + (1-x)*AT
}
GC2BD = function(GC){
# GC = percentage
BD = GC / 100 * 0.098... |
getsmarter/bda | module_2/M2_NB1_SourcesOfData.ipynb | mit | import pandas as pd
from pandas_datareader import data, wb
import numpy as np
import matplotlib.pylab as plt
import matplotlib
import folium
import geocoder
import wikipedia
#set plot options
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (10, 8)
"""
Explanation: <div align="right">Python 3.6 Jupyter Note... |
empet/Plotly-plots | Spiral-Plot.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
PI=np.pi
a=2
theta=np.linspace(3*PI/2, 8*PI, 400)
z=a*theta*np.exp(-1j*theta)
plt.figure(figsize=(6,6))
plt.plot(z.real, z.imag)
plt.axis('equal')
"""
Explanation: Spiral plot
Spiral plot is a method of visualizing (periodic) time series. Here we... |
pvalienteverde/ElCuadernillo | ElCuadernillo/20160301_TensorFlowGradientDescentWithMomentum/GradientDescentWithMoment.ipynb | mit | import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
%matplotlib inline
import sys
import time
from IPython.display import Image
sys.path.append('/home/pedro/git/ElCuadernillo/ElCuadernillo/20160301_TensorFlowGradientDescentWithMomentum')
import gr... |
elenduuche/deep-learning | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
google-research/google-research | aptamers_mlpd/learning/create_binned_and_super_bin_labels.ipynb | apache-2.0 | import pandas as pd
import math
import numpy as np
"""
Explanation: Copyright 2021 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless requ... |
turbomanage/training-data-analyst | quests/endtoendml/labs/4_preproc.ipynb | apache-2.0 | %pip install apache-beam[gcp]==2.13.0
"""
Explanation: <h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https://cloud.google.com/dataflow/">Cloud Dataflow</a></li>
<li>Simulate a dataset where no ultrasound is performed (i.e. male or female unkno... |
AlpineNow/python-alpine-api | doc/JupyterNotebookExamples/Introduction.ipynb | mit | import alpine as AlpineAPI
from pprint import pprint
import json
"""
Explanation: Introduction
Let's start with a example of an Alpine API session.
Initialize a session.
Take a tour of some commands.
Run a workflow and download the results.
Import the Python Alpine API and some other useful packages.
End of explana... |
sylvchev/coursIntroPython | cours/5-ApprendrePython-Bibliotheque.ipynb | gpl-3.0 | f=open('fichiertravail.txt', 'w')
"""
Explanation: Fichiers et entrées/sorties
Pour écrire ou lire dans un fichier, open() renvoie un objet de type fichier, et est utilisée plus généralement avec deux arguments : open(nomfichier, mode).
End of explanation
"""
f.write('Voici un test\n')
"""
Explanation: Le premie... |
MichaelGrupp/evo | notebooks/pandas_bridge.ipynb | gpl-3.0 | # magic plot configuration
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%matplotlib notebook
from IPython.display import display
"""
Explanation: pandas_bridge
The evo.tools.pandas_bridge module converts:
* evo.core.trajectory.PosePath3D
* evo.core.trajectory.PoseTrajectory3D
* evo.core.re... |
dnc1994/MachineLearning-UW | ml-classification/module-10-online-learning-assignment-solution.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a Num... |
evanmiltenburg/python-for-text-analysis | Chapters-colab/Chapter_03_Strings.ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
google/starthinker | colabs/anonymize.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: BigQuery Anonymize Dataset
Copies tables and view from one dataset to another and anynonamizes all rows. Used to create sample datasets for dashboards.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "Licen... |
stephank16/enes_graph_use_case | prov_templates/Data_ingest_use_case_templates.ipynb | gpl-3.0 | # import variable setting dictionaries from dkrz data ingest tool chain
# and remove __doc__ strings from dictionary (would clutter PROV graph visualizations)
from provtemplates import workflow_steps
from collections import MutableMapping
from contextlib import suppress
def delete_keys_from_dict(dictionary, keys):
... |
blue-yonder/tsfresh | notebooks/examples/02 sklearn Pipeline.ipynb | mit | import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from tsfresh.examples import load_robot_execution_failures
from tsfresh.transformers import RelevantFeatur... |
sysid/nbs | lstm/SequenceClassification_LSTM_CNN.ipynb | mit | import numpy
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset but only kee... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/generic_mle.ipynb | bsd-3-clause | import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
"""
Explanation: Maximum Likelihood Estimation (Generic models)
This tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples:
Probit ... |
JoseGuzman/myIPythonNotebooks | pub/Migration velocity.ipynb | gpl-2.0 | %pylab inline
import pandas as pd
# read CSV file in pandas
mydf = pd.read_csv('.data/Julie_R1_Bef_S4_cell123_Position.csv', skiprows=2)
mydf.head()
"""
Explanation: <H1>Migration velocity</H1>
<P> To compute the velocity of the trajectories of several particles, we generated a file with the 3D coordinates (Position... |
google/jax | docs/notebooks/vmapped_log_probs.ipynb | apache-2.0 | import functools
import itertools
import re
import sys
import time
from matplotlib.pyplot import *
import jax
from jax import lax
import jax.numpy as jnp
import jax.scipy as jsp
from jax import random
import numpy as np
import scipy as sp
"""
Explanation: Autobatching log-densities example
This notebook demonstra... |
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