repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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statkraft/shyft-doc | notebooks/grid-pp/kalman_updating.ipynb | lgpl-3.0 | # first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%matplotlib inline
import datetime
import numpy as np
import os
from os import path
import sys # once the shyft_path is set correctly, you should be able to... |
ucsd-ccbb/Oncolist | notebooks/.ipynb_checkpoints/BasicCFNClusterSetup-checkpoint.ipynb | mit | import os
import sys
sys.path.append(os.getcwd().replace("notebooks", "cfncluster"))
## Input the AWS account access keys
aws_access_key_id = "/**aws_access_key_id**/"
aws_secret_access_key = "/**aws_secret_access_key**/"
## CFNCluster name
your_cluster_name = "geo"
## The private key pair for accessing cluster.
p... |
jphall663/GWU_data_mining | 02_analytical_data_prep/src/py_part_2_winsorize.ipynb | apache-2.0 | import pandas as pd # pandas for handling mixed data sets
import numpy as np # numpy for basic math and matrix operations
from scipy.stats.mstats import winsorize # scipy for stats and more advanced calculations
"""
Explanation: License
Copyright (C) 2017 J. Patrick Hall... |
bbartoldson/examples | pong/pong.ipynb | mit | import gym
import numpy as np
import tensorflow as tf
from IPython import display
import matplotlib.pyplot as plt
import time
config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
%matplotlib inline
"""
Explanation: Pong-Playing TensorFlow Neural Network
Import modules needed to train neural network in P... |
amueller/nyu_ml_lectures | Grid Searches for Hyper Parameters.ipynb | bsd-2-clause | from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data,
digits.targ... |
AllenDowney/ProbablyOverthinkingIt | hierarchical.ipynb | mit | from __future__ import print_function, division
from thinkbayes2 import Pmf, Suite
from fractions import Fraction
"""
Explanation: Bayesian interpretation of medical tests
This notebooks explores several problems related to interpreting the results of medical tests.
Copyright 2016 Allen Downey
MIT License: http://op... |
ivannz/study_notes | year_15_16/machine_learning_course/ensemble_practicum/xgboost/XGBoost.ipynb | mit | import time, os, re, zipfile
import numpy as np, pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: eXtreme Gradient Boosting library (XGBoost)
<center>An unfocused introduction by Ivan Nazarov</center>
Import the main toolkit.
End of explanation
"""
import sklearn as sk, xgboost as xg
... |
vitojph/2016progpln | notebooks/1-Intro-Python.ipynb | mit | print('Esto es un mensaje')
"""
Explanation: Introducción a Python
Vamos a hacer una pequeña introducción al lenguaje de programación Python. Para ello, me voy a apoyar principalmente en dos excelentes recursos para aprender Python online que siempre recomiendo:
el curso de Python en CodeCademy.
el curso Python for ... |
hannorein/rebound | ipython_examples/IntegratingArbitraryODEs.ipynb | gpl-3.0 | import rebound
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Integrating arbitrary ODEs
Although REBOUND is primarily an N-body integrator, it can also integrate arbitrary ordinary differential equations (ODEs). Even better: it can integrate arbitrary ODEs in parallel with an N-body simulation. T... |
kmorel/kmorel.github.io | images/vaccine-correlations/vaccinevislie.ipynb | mit | vaccine_data = pandas.read_csv(
'covid19_vaccinations_in_the_united_states.csv',
header=2,
index_col='State/Territory/Federal Entity',
)
print(vaccine_data.columns)
vaccine_data.head()
"""
Explanation: Data read from https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total on Octo... |
Esri/gis-stat-analysis-py-tutor | notebooks/ExtendingArcGISDirectly.ipynb | apache-2.0 | import arcpy as ARCPY
import numpy as NUM
import SSDataObject as SSDO
import scipy as SCIPY
import pandas as PANDA
import pysal as PYSAL
"""
Explanation: Leveraging Open-Source Python Packages for Data Analysis within the ArcGIS Environment (Direct Integration Strategy)
Using NumPy as the common denominator
Could use... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/basic_joinnodes.ipynb | gpl-2.0 | from nipype import JoinNode, Node, Workflow
from nipype.interfaces.utility import Function, IdentityInterface
def get_data_from_id(id):
"""Generate a random number based on id"""
import numpy as np
return id + np.random.rand()
def merge_and_scale_data(data2):
"""Scale the input list by 1000"""
imp... |
fastai/course-v3 | nbs/dl2/cyclegan_ws.ipynb | apache-2.0 | #path = Config().data_path()
#! wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/summer2winter_yosemite.zip -P {path}
#! unzip -q -n {path}/summer2winter_yosemite.zip -d {path}
#! rm {path}/summer2winter_yosemite.zip
path = Config().data_path()/'summer2winter_yosemite'
path.ls()
"""
Explanation:... |
vberthiaume/vblandr | udacity/udacity/3_regularization.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you train... |
adrn/GaiaPairsFollowup | paper/figures/Create-tgas-fits.ipynb | mit | from os import path
# Third-party
from astropy.io import ascii
from astropy.table import Table
import astropy.coordinates as coord
import astropy.units as u
from astropy.constants import G, c
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import numpy as np
plt.style.use('apw-notebook')
%matpl... |
liumengjun/cn-deep-learning | ipnd-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
postBG/DL_project | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
ledrui/Regression | week3/week-3-polynomial-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function to take a... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week1/week-1-simple-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SArray and SFrame functions to compute important summary statistics
* Write a functio... |
rueedlinger/machine-learning-snippets | notebooks/supervised/text_classification/text_classification.ipynb | mit | import re
import urllib.request
'''
with urllib.request.urlopen('http://www.gutenberg.org/cache/epub/22465/pg22465.txt') as response:
txt_german = response.read().decode('utf-8')
with urllib.request.urlopen('https://www.gutenberg.org/files/46/46-0.txt') as response:
txt_english = response.read().decode('utf-8'... |
darioizzo/d-CGP | doc/sphinx/notebooks/symbolic_regression_3.ipynb | gpl-3.0 | # Some necessary imports.
import dcgpy
import pygmo as pg
# Sympy is nice to have for basic symbolic manipulation.
from sympy import init_printing
from sympy.parsing.sympy_parser import *
init_printing()
# Fundamental for plotting.
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Multi-objectiv... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/2D/FD_2D_DX4_DT2_fast.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: FD_2D_DX4_DT2_fast 2-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the first-order acoustic wave equation
Tempora... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties:... |
rahulk90/vae_sparse | expt/TrainingVAEsparse.ipynb | mit | import sys,os,glob
from collections import OrderedDict
import numpy as np
from utils.misc import readPickle, createIfAbsent
sys.path.append('../')
from optvaedatasets.load import loadDataset as loadDataset_OVAE
from sklearn.feature_extraction.text import TfidfTransformer
"""
Explanation: VAEs on sparse data
The follo... |
NorfolkDataSci/presentations | 2017-10_class_imbalance/Class Imbalance.ipynb | mit | %matplotlib inline
from sklearn import utils
import matplotlib
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from imblearn.over_sampling import SMOTE
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from sklearn.linear_model import LogisticRegression
#metrics to... |
computational-class/computational-communication-2016 | code/.ipynb_checkpoints/16&17 networkx-checkpoint.ipynb | mit | %matplotlib inline
import networkx as nx
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import networkx as nx
G=nx.Graph() # G = nx.DiGraph() # 有向网络
# 添加（孤立）节点
G.add_node("spam")
# 添加节点和链接
G.add_edge(1,2)
print(G.nodes())
print(G.edges())
# 绘制网络
nx.draw(G, with_labels = True)
"""
Explanation: 网络科学理论简介... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_custom_image_classification_online_pipeline.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Custom training image classification model with pipeline for online ... |
junhwanjang/DataSchool | Lecture/02. 파이썬 프로그래밍/6) Numpy 시작하기.ipynb | mit | import numpy as np
a = np.array([0, 1, 2, 3])
a
"""
Explanation: NumPy
NumPy란
수치해석용 Python 라이브러리
C로 구현 (파이썬용 C라이브러리)
BLAS/LAPACK 기반
빠른 수치 계산을 위한 Structured Array 제공
Home
http://www.numpy.org/
Documentation
http://docs.scipy.org/doc/
Tutorial
http://www.scipy-lectures.org/intro/numpy/index.html
https://docs.scipy.org/... |
awitney/2017 | hic_workshop_2017/WD/Basic_HiC_analysis.ipynb | gpl-3.0 | # This is regular Python comment inside Jupyter "Code" cell.
# You can easily run "Hello world" in the "Code" cell (focus on the cell and press Shift+Enter):
print("Hello world!")
"""
Explanation: <a id="navigation"></a>
Hi-C data analysis
Welcome to the Jupyter notebook dedicated to Hi-C data analysis. Here we will b... |
maciejkula/lightfm | examples/stackexchange/hybrid_crossvalidated.ipynb | apache-2.0 | import numpy as np
from lightfm.datasets import fetch_stackexchange
data = fetch_stackexchange('crossvalidated',
test_set_fraction=0.1,
indicator_features=False,
tag_features=True)
train = data['train']
test = data['test']
"""
Explanat... |
irockafe/revo_healthcare | notebooks/MTBLS315/exploratory/MTBLS315_uhplc_pos_classifer-4ppm.ipynb | mit | import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import cross_val_score
#from sklearn.... |
yala/introdeeplearning | draft/rnn.ipynb | mit | import tensorflow as tf
import cPickle as pickle
from collections import defaultdict
import re, random
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
#Read data and do preprocessing
def read_data(fn):
with open(fn) as f:
data = pickle.load(f)
#Clean the text
new... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day2/Introduction to Photometry-Solutions.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as fits
## make matplotlib appear in the notebook rather than in a new window
%matplotlib inline
"""
Explanation: Introduction to Photometry - Solutions
Dora Föhring, University of Hawaii Institute for Astronomy
Aim: Demonstrate photometry on a ... |
ngovindaraj/Udacity_Projects | Data_Analysis/Data_Analysis.ipynb | mit | import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
%pylab inline
titanic_data = pd.read_csv('./titanic_data.csv')
titanic_data.head()
# Checking data types by column
titanic_data.dtypes
# Checking for duplicate entries
du... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/fgoals-f3-l/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'fgoals-f3-l', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CAS
Source ID: FGOALS-F3-L
Sub-Topics: Radiative Forcings.
Properties: 85 (4... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 01-grundlagen/pandas.ipynb | gpl-3.0 | # Import der Bibliotheken
import pandas as pd
# Extra packages
import numpy as np
import matplotlib.pyplot as plt # für die grafische Darstellung
import seaborn as sns # für grafische Darstellung. Muss vorher evtl. installiert werden
# jupyter notebook magic Befehl
%matplotlib inline
plt.rcParams['figure.figsize'] ... |
edhenry/notebooks | Sequential and Binary Search in Python.ipynb | mit | # Finding a single integer in an array of integers using Python's `in`
# operator
15 in [3,5,6,9,12,11]
"""
Explanation: This notebook will include examples of searching and sorting algorithms implemented in python. It is both for my own learning, and for anyone else who would like to use this notebook for anything ... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/jc/为慈善机构寻找捐助者/.Trash-0/files/finding_donors-zh.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualization code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Loa... |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Neural_Network_Part2_Solutions.ipynb | cc0-1.0 | from __future__ import print_function
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
"""
Explanation: Neural Network Part2
End of... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Neural Networks and Deep Learning/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... |
ziky5/F4500_Python_pro_fyziky | lekce_07/Moduly.ipynb | mit | from os import path
path.exists("data.csv")
"""
Explanation: Moduly moduly aneb O importování
aliasy
lze importovat jen jednu třídu/funkci/proměnnou, ale moduly mohou mit i více úrovní
End of explanation
"""
from os.path import exists
"""
Explanation: lze naimportovat jednotlive funkce
End of explanation
"""
from... |
tpin3694/tpin3694.github.io | regex/match_a_unicode_character.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match A Unicode Character
Slug: match_a_unicode_character
Summary: Match A Unicode Character
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
"""
# Create a variabl... |
adolfoguimaraes/machinelearning | Introduction/Exercicio01_Titanic.ipynb | mit | import pandas as pd
import numpy as np
#Lendo a base de dados
df = pd.read_csv('../datasets/titanic/train.csv')
print("Tabela Original")
df.head()
df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
df = df.dropna()
df['Gender'] = df['Sex'].map({'female': 0, 'male':1}).astype(int)
df['Port'] = df['Embarked'].map({'C'... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-06.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-6
End of explanation
"""
R1 = 0.10 # [Ohm]
R2 = 0.07 # [Ohm]
Xm = 10.0 # [Ohm]
X1 = 0.21 # [Ohm]
X2 = 0.21 # [Ohm]
Pmech = 500 # [W]
Pmisc = 0 # [W]
Pcore = 400 # [W]
Vphi = 120 # [... |
EFerriss/pynams | EXAMPLES_experimentation.ipynb | mit | import pynams
"""
Explanation: pynams functions that help when running experiments
End of explanation
"""
from pynams import fO2
fO2 = fO2(celsius=1000, buffer_curve='NNO')
print(fO2)
"""
Explanation: What is the log base 10 of the fO2 in bars for a given temperature and buffer?
End of explanation
"""
from pynams... |
IS-ENES-Data/submission_forms | test/Templates/.ipynb_checkpoints/Create_Submission_Form-checkpoint.ipynb | apache-2.0 | from dkrz_forms import form_widgets
form_widgets.show_status('form-generation')
"""
Explanation: Create your DKRZ data ingest request form
To generate a data submission form for you, please edit the cell below to include your name, email as well as the project your data belogs to
Then please press "Shift" + Enter to e... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2015.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - TD noté, 5 décembre 2014
Parcours de chemins dans un graphe acyclique (arbre).
End of explanation
"""
def adjacence(N):
# on crée uen matrice vide
mat = [ [ 0 for j in range(N) ] for i in range(N) ]
for i in range(0,N... |
james-prior/cohpy | 20150327-dojo-join.ipynb | mit | ''.join(['hello', 'gnew', 'world'])
' '.join(['hello', 'gnew', 'world'])
','.join(['hello', 'gnew', 'world'])
', '.join(['hello', 'gnew', 'world'])
' and '.join(['hello', 'gnew', 'world'])
"""
Explanation: The join method for strings is confusing for many beginners, so here are some examples. The first one, that a... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SolveMathProblems/3 - AdvancedFiniteDifferences.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Advanced finite difference
This notebook assumes that you have completed the finite difference operations notebook.
End of explanation
"""
voxel = np.load('voxel_data.npz')['data']
voxel.shape
fig = plt.figure(1, figsize=(20, 5))... |
zomansud/coursera | ml-classification/week-5/module-8-boosting-assignment-2-blank.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some feature engineering.
Modify the decision tree... |
kdmurray91/kwip-experiments | bifurcating/TreeSimulation.ipynb | mit | import utils
import gzip
import random
import string
import math
import ete3 as ete
from skbio import Alignment, DNA, DistanceMatrix
from skbio.tree import nj
import numpy as np
import skbio
import sys
seed = 1003
genome_size = 1 # mbp
num_samples = 8
num_runs = 3
mean_n_reads = 5e5
sd_n_reads = mean_n_reads * 0.1 # ... |
wallinm1/kaggle-facebook-bot | facebook_notebook.ipynb | mit | import pandas as pd
import re
import gc
import numpy as np
from scipy import sparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MinMaxScaler
from sklearn.feature_selection import SelectPercentile, chi2
from sklearn.externals import joblib
import xgboost as xgb
"""
Expl... |
fastai/course-v3 | nbs/dl2/01_matmul.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
"""
Explanation: Matrix multiplication from foundations
The foundations we'll assume throughout this course are:
Python
Python modules (non-DL)
pytorch indexable tensor, and tensor creation (including RNGs - random number generators)
fastai.datasets
Check import... |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment2/FullyConnectedNets.ipynb | mit | # As usual, a bit of setup
import sys
import os
sys.path.insert(0, os.path.abspath('..'))
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradie... |
ajgpitch/qutip-notebooks | docs/guide/BasicOperations.ipynb | lgpl-3.0 | from qutip import *
"""
Explanation: Basic Operations on Quantum Objects
Contents
First Things First
The Qobj Class
Functions Acting on the Qobj Class
<a id='first'></a>
First Things First
<br>
<div class="warn">
**Warning**: Do not run QuTiP from the installation directory.
</div>
In order to load the QuTiP librar... |
jArumugam/python-notes | P09Advanced Functions Test.ipynb | mit | def word_lengths(phrase):
# return map(lambda word: len(word), [word for word in phrase.split()])
return map(lambda word: len(word), phrase.split())
word_lengths('How long are the words in this phrase')
"""
Explanation: Advanced Functions Test
For this test, you should use the built-in functions to be ab... |
texib/deeplearning_homework | tensor-flow-exercises/2_fullyconnected.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
"""
Explanation: Deep Learning with TensorFlow
Credits: Forked from TensorFlow by Google
Setup
Refer to the setup instructions.
Exercise 2
Pre... |
carltoews/tennis | results/.ipynb_checkpoints/DI_plot1-checkpoint.ipynb | gpl-3.0 | from IPython.display import display, HTML
display(HTML('''<img src="image1.png",width=800,height=500">'''))
"""
Explanation: Plot 1: The predictive potential of rank difference
End of explanation
"""
import numpy as np # numerical libraries
import pandas as pd # for data analysis
import matplotlib as mpl # a big lib... |
n-witt/MachineLearningWithText_SS2017 | tutorials/7 Principal Component Analysis.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
"""
Explanation: Principal Component Analysis (PCA)
Up until now we've only talked about supervised methods.
What were these again?
Now we want to discuss unsupervised methods that highlight aspects of data witho... |
aaossa/Dear-Notebooks | Web scraping/Lessons learned in web scraping.ipynb | gpl-3.0 | import requests
"""
Explanation: Lessons learned in web scraping
End of explanation
"""
req = requests.head('http://www.google.com')
print(req.headers['Content-Length'])
req = requests.get('http://www.google.com')
print(req.headers['Content-Length'])
"""
Explanation: Making requests
Lesson 1 - Use the head!
A head... |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/zz_under_construction/zz_old/talks/DataWeekends/SparkMLDeployment/DataWeekends-Mar182017-SparkMLDeployment.ipynb | apache-2.0 | # You may need to Reconnect (more than Restart) the Kernel to pick up changes to these sett
import os
master = '--master spark://spark-master-2-1-0:7077'
conf = '--conf spark.cores.max=1 --conf spark.executor.memory=512m'
packages = '--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1'
jars ... |
tensorflow/workshops | extras/tensorflow_lattice/04_lattice_basics.ipynb | apache-2.0 | !pip install tensorflow_lattice
import tensorflow as tf
import tensorflow_lattice as tfl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
"""
Explanation: Basics of lattice models
In this... |
LucaCanali/Miscellaneous | Spark_Physics/HEP_benchmark/ADL_HEP_Query_Benchmark_Q1_Q5_CERNSWAN_Version.ipynb | apache-2.0 | # Start the Spark Session
# When Using Spark on CERN SWAN, run this cell to get the Spark Session
# Note: when running SWAN for this, do not select to connect to a CERN Spark cluster
# If you want to use a cluster anyway, please copy the data to a cluster filesystem first
from pyspark.sql import SparkSession
spark = (... |
hvillanua/deep-learning | transfer-learning/Transfer_Learning_Solution.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
kmclaugh/fastai_courses | ai-playground/Keras_Linear_Regression_Example.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
from keras.layers import Dense
from keras.models import Model, Sequential
from keras import initializers
"""
Explanation: Keras Model for a Simple Linear Function
In this notebook, I've created a simple Keras model to approximate a linea... |
aschaffn/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/theta-model.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=15)
plt.rc("lines", linewidth=3)
sns.set_style("darkgrid")
"""
Explanation: The Theta Model
The Theta model of Assimakopoulos & Nikolopoulo... |
ucsdlib/python-novice-inflammation | 1-intro-to-numpy-short.ipynb | cc0-1.0 | import numpy
"""
Explanation: Analyzing patient data
Words are useful, but what’s more useful are the sentences and stories we build with them.
A lot of powerful tools are built into languages like Python, even more live in the libraries they are used to build
We need to import a library called NumPy
Use this library... |
angelmtenor/data-science-keras | simple_stock_prediction.ipynb | mit | %matplotlib inline
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import keras
import helper
#helper.reproducible(seed=42)
sns.set()
"""
Explanation: Simple Stock Prediction
Predicting Alphabet Inc. stock price using a Recurrent Neural Network
Dataset from Goog... |
gevero/py_matrix | py_matrix/examples/Gold Circular Magnetic Dichroism.ipynb | gpl-3.0 | # libraries
import numpy as np # numpy
import scipy as sp # scipy
import scipy.constants as sp_c # scientific constants
import sys # sys to add py_matrix to the path
# matplotlib inline plots
import matplotlib.pylab as plt
%matplotlib inline
# adding py_matrix parent folder to python path
sys.path.append('../../')
i... |
matthewzimmer/traffic-sign-classification | Traffic_Signs_Recognition-WIP.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
import math
... |
gregorjerse/rt2 | 2015_2016/lab13/Extending values on vertices.ipynb | gpl-3.0 | from itertools import combinations, chain
def simplex_closure(a):
"""Returns the generator that iterating over all subsimplices (of all dimensions) in the closure
of the simplex a. The simplex a is also included.
"""
return chain.from_iterable([combinations(a, l) for l in range(1, len(a) + 1)])
... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/pca_fertility_factors.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.multivariate.pca import PCA
plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=14)
"""
Explanation: statsmodels Principal Component Analysis
Key ideas: Principal component analysis, world bank data, fertility
In this ... |
rickiepark/tfk-notebooks | tensorflow_for_beginners/5. Fully Connected Neural Network.ipynb | mit | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation: 텐서플로우 라이브러리를 임포트 하세요.
텐서플로우에는 MNIST 데이터를 자동으로 로딩해 주는 헬퍼 함수가 있습니다. "MNIST_data" 폴더에 데이터를 다운로드하고 훈련, 검증, 테스트 데이터를 자동으로 읽어 들입니다. one_hot 옵션을 설정하면 정답 레이블을 원핫벡터로 바꾸어 줍니다.
End of explana... |
squishbug/DataScienceProgramming | 11-Similarity-Based-Learning/SimilarityBased.ipynb | cc0-1.0 | import numpy as np
import math as ma
import matplotlib.pyplot as plt
%matplotlib inline
X = np.array([3.3, 1.2])
Y = np.array([2.1, -1.8])
plt.arrow(0,0,*X, head_width=0.2);
plt.arrow(0,0,*Y, head_width=0.2);
plt.xlim([0, 4]);
plt.ylim([-2,2]);
plt.show();
# Euclidean distance manually:
ma.sqrt(np.sum((X-Y)**2))
# ... |
Kaggle/learntools | notebooks/feature_engineering_new/raw/tut_bonus.ipynb | apache-2.0 | #$HIDE_INPUT$
import os
import warnings
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from IPython.display import display
from pandas.api.types import CategoricalDtype
from category_encoders import MEstimateEncoder
from sklearn.cluster import KMe... |
greenelab/GCB535 | 23_Prelab_Python-I/Python-I-Prelab.ipynb | bsd-3-clause | print "I am Python code! Press Shift+Enter to run me!"
"""
Explanation: Lesson 1: Introduction to Python
Table of contents
How to use this notebook
Introduction
Writing your first script
The print statement
Variables and data types
Basic math
Commenting code
Test your understanding: practice set 1
1. How to use thi... |
flohorovicic/pynoddy | docs/notebooks/Training_Set_3.ipynb | gpl-2.0 | %matplotlib inline
# here the usual imports. If any of the imports fails,
# make sure that pynoddy is installed
# properly, ideally with 'python setup.py develop'
# or 'python setup.py install'
import sys, os
import matplotlib.pyplot as plt
import numpy as np
# adjust some settings for matplotlib
from matplotlib imp... |
AlphaGit/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
bollwyvl/ip-bootstrap | docs/Icon.ipynb | bsd-3-clause | from IPython.html import widgets
from ipbs.widgets import Icon
import ipbs.bootstrap as bs
from ipbs.icons import FontAwesome, Size
"""
Explanation: Icon
End of explanation
"""
fa = FontAwesome()
"""
Explanation: First, grab a FontAwesome instance which knows about all of the icons.
End of explanation
"""
fa.spac... |
cstrelioff/ARM-ipynb | Chapter3/chptr3.1.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# use matplotlib style sheet
plt.style.use('ggplot')
# import statsmodels for R-style regression
import statsmodels.formula.api as smf
"""
Explanation: 3.1: One ... |
antonpetkoff/learning | text-mining/TM_lab08_MLP_Reg.ipynb | gpl-3.0 | import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_20newsgroups
from nltk import TweetTokenizer
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical... |
phenology/infrastructure | applications/notebooks/examples/python/connect_to_spark.ipynb | apache-2.0 | #Add all dependencies to PYTHON_PATH
import sys
sys.path.append("/usr/lib/spark/python")
sys.path.append("/usr/lib/spark/python/lib/py4j-0.10.4-src.zip")
sys.path.append("/usr/lib/python3/dist-packages")
#Define environment variables
import os
os.environ["HADOOP_CONF_DIR"] = "/etc/hadoop/conf"
os.environ["PYSPARK_PYTH... |
jpilgram/phys202-2015-work | project/NeuralNetworks.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
def show_digit(i):
plt.matshow(digits.images[i]);
interact(show_digit, i=(0,100));
"""
Explanation: Neural Networks
This project w... |
ewulczyn/talk_page_abuse | src/analysis/Characterizing Context of Attacks.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from load_utils import *
d = load_diffs()
df_events, df_blocked_user_text = load_block_events_and_users()
"""
Explanatio... |
tpin3694/tpin3694.github.io | python/pandas_dataframe_count_values.ipynb | mit | import pandas as pd
"""
Explanation: Title: Count Values In Pandas Dataframe
Slug: pandas_dataframe_count_values
Summary: Count Values In Pandas Dataframe
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Import the pandas module
End of explanation
"""
year = pd.Series([1875, 1876, ... |
sdpython/ensae_teaching_cs | _doc/notebooks/competitions/2017/prepare_data_2017.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.ml - 2017 - Préparation des données
Ce notebook explique comment les données de la compétation 2017 ont été préparées. On récupére d'abord les données depuis le site OpenFoodFacts.
End of explanation
"""
import os
os.stat("c:/temp/fr... |
ML4DS/ML4all | C3.Classification_LogReg/RegresionLogistica_student.ipynb | mit | # To visualize plots in the notebook
%matplotlib inline
# Imported libraries
import csv
import random
import matplotlib
import matplotlib.pyplot as plt
import pylab
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
"""
Ex... |
GoogleCloudPlatform/tf-estimator-tutorials | Experimental/Movielens Recommendation.ipynb | apache-2.0 | !pip install annoy
import math
import os
import pandas as pd
import numpy as np
from datetime import datetime
import tensorflow as tf
from tensorflow import data
print "TensorFlow : {}".format(tf.__version__)
SEED = 19831060
"""
Explanation: Recommendation Model with Approximate Item Matching
This notebook shows h... |
jmhsi/justin_tinker | data_science/courses/deeplearning2/seq2seq-translation.ipynb | apache-2.0 | import unicodedata, string, re, random, time, math, torch, torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import keras, numpy as np
from keras.preprocessing import sequence
"""
Explanation: Requirements
End of explanation
"""
SOS_token = 0
EOS_token = 1
c... |
mbeyeler/opencv-machine-learning | notebooks/04.01-Preprocessing-Data.ipynb | mit | from sklearn import preprocessing
import numpy as np
X = np.array([[ 1., -2., 2.],
[ 3., 0., 0.],
[ 0., 1., -1.]])
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src... |
nreimers/deeplearning4nlp-tutorial | 2015-10_Lecture/Lecture2/code/3_Intro_Lasagne_Solution.ipynb | apache-2.0 | import gzip
import cPickle
import numpy as np
import theano
import theano.tensor as T
import lasagne
# Load the pickle file for the MNIST dataset.
dataset = 'data/mnist.pkl.gz'
f = gzip.open(dataset, 'rb')
train_set, dev_set, test_set = cPickle.load(f)
f.close()
#train_set contains 2 entries, first the X values, se... |
SSDS-Croatia/SSDS-2017 | Day-1/First day - Introduction to Machine Learning with Tensorflow [SOLVED].ipynb | mit | import tensorflow as tf
"""
Explanation: Summer School of Data Science - Split '17
1. Introduction to Machine Learning with TensorFlow
This hands-on session serves as an introductory course for essential TensorFlow usage and basic machine learning with TensorFlow. This notebook is partly based on and follow the approa... |
tedunderwood/changepoint | diagonal_permutation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import csv, random
import numpy as np
from scipy import spatial
pcafields = ['PC' + str(x) for x in range(1,15)]
# Here we just create a list of strings that will
# correspond to field names in the data provided
# by M... |
ioam/scipy-2017-holoviews-tutorial | solutions/04-working-with-tabular-data-with-solutions.ipynb | bsd-3-clause | import numpy as np
import scipy.stats as ss
import pandas as pd
import holoviews as hv
hv.extension('bokeh')
%opts Curve Scatter [tools=['hover']]
"""
Explanation: <a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%;" align="left"/></a>
<div style="float:right;"><h2>04. Working ... |
ericmjl/Network-Analysis-Made-Simple | archive/5-graph-input-output-student.ipynb | mit | import zipfile
# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listdir('datasets/'):
print('Unzipping the divvy_2013.zip file in the datasets folder.')
with zipfile.ZipFile("datasets/divvy_2013.zip","r") as zip_ref:
zip_ref.extractall('datasets'... |
pranavj1001/LearnLanguages | python/DataAnalysis/numpy/NumPy.ipynb | mit | import numpy as np
"""
Explanation: NumPy
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
In this notebook we'll try various numpy methods and in the p... |
xlbaojun/Note-jupyter | 05其他/pandas文档-zh-master/数据合并、连接和拼接-Merge, join, and concat.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
df1 = pd.DataFrame({'A':['A0','A1','A2','A3'],
'B':['B0','B1','B2','B3'],
'C':['C0','C1','C2','C3'],
'D':['D0','D1','D2','D3']},
index=[0,1,2,3])
df1
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7']... |
arsenovic/galgebra | examples/ipython/colored_christoffel_symbols.ipynb | bsd-3-clause | from __future__ import print_function
import sys
from galgebra.printer import Format, xpdf
Format()
from sympy import symbols, sin, pi, latex, Array, permutedims
from galgebra.ga import Ga
from IPython.display import Math
"""
Explanation: This example is kindly contributed by FreddyBaudine for reproducing pygae/galg... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/SDK_Custom_Training_with_Unmanaged_Image_Dataset.ipynb | apache-2.0 | !pip3 uninstall -y google-cloud-aiplatform
!pip3 install google-cloud-aiplatform
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
"""
Explanation: Feedback or issues?
For any feedback or questions, please open an issue.
Vertex SDK for Python: Custom Training Example with Unmanaged Imag... |
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