repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
1200wd/bitcoinlib | docs/bitcoinlib-10-minutes.ipynb | gpl-3.0 | from bitcoinlib.keys import Key
k = Key()
k.info()
"""
Explanation: Learn BitcoinLib in 10 minutes
A short walk through all the important BitcionLib classes: create keys, transactions and wallets using this library.
You can run and experiment with the code examples if you have installed Jupyter
Keys
With the Key class... |
milancurcic/lunch-bytes | Spring_2019/LB29/GettingData_XR.ipynb | cc0-1.0 | import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import netCDF4 as nc
from mpl_toolkits.basemap import Basemap
"""
Explanation: <a name="top"></a>
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://cdn.miami.edu/_assets-co... |
yala/introdeeplearning | Lab2.ipynb | mit | import math
import pickle as p
import tensorflow as tf
import numpy as np
import utils
import json
"""
Explanation: Lab Part II: RNN Sentiment Classifier
In the previous lab, you built a tweet sentiment classifier with a simple feedforward neural network. Now we ask you to improve this model by representing it as a se... |
jinntrance/MOOC | coursera/ml-foundations/week6/Deep Features for Image Classification.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Using deep features to build an image classifier
Fire up GraphLab Create
End of explanation
"""
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
"""
Explanation: Load a common image analysis dataset
We will use a popular benchmark d... |
johnpfay/environ859 | 06_WebGIS/Notebooks/12-Exploring-the-BISON-API.ipynb | gpl-3.0 | #First, import the wonderful requests module
import requests
#Now, we'll deconstruct the example URL into the service URL and parameters, saving the paramters as a dictionary
url = 'http://bison.usgs.gov/api/search.json'
params = {'species':'Bison bison',
'type':'scientific_name',
'start':'0',
... |
samejack/blog-content | keras-ml/mnist-neural-network.ipynb | apache-2.0 | from keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
"""
Explanation: Python Keras MNIST 手寫辨識
這是一個神經網路的範例，利用了 Python Keras 來訓練一個手寫辨識分類 Model。
我們要的問題是將手寫數字的灰度圖像（28x28 Pixel）分類為 10 類（0至9）。使用的數據集是 MNIST 典數據集，它是由國家標準技術研究所（MNIST 的 NIST）在1980年代組裝而成的，包含 60,000 張訓練圖像和 ... |
ethen8181/machine-learning | deep_learning/rnn/1_pytorch_rnn.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot... |
ML4DS/ML4all | U1.KMeans/.ipynb_checkpoints/Python_intro-checkpoint.ipynb | mit | str1 = '"Hola" is how we say "hello" in Spanish.'
str2 = "Strings can also be defined with quotes; try to be sistematic."
"""
Explanation: A brief tutorial of basic python
From the wikipedia: "Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, a... |
sjchoi86/Tensorflow-101 | notebooks/char_rnn_train_tutorial.ipynb | mit | # Now convert all text to index using vocab!
corpus = np.array(list(map(vocab.get, data)))
print ("Type of 'corpus' is %s, shape is %s, and length is %d"
% (type(corpus), corpus.shape, len(corpus)))
check_len = 10
print ("\n'corpus' looks like %s" % (corpus[0:check_len]))
for i in range(check_len):
_wordidx ... |
tuchandra/sleep-analysis | spring-sleep-analysis.ipynb | mit | %matplotlib inline
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import json
import datetime
import scipy.stats
matplotlib.style.use('ggplot')
plt.rcParams['figure.figsize'] = [12.0, 8.0]
"""
Explanation: Spring Sleep Analysis
Studying springtime sleep habits though an... |
elleros/spoofed-speech-detection | gmm_ml_synthetic_speech_detection.ipynb | mit | import os
import time
import numpy as np
import pandas as pd
from bob.bio.spear import preprocessor, extractor
from bob.bio.gmm import algorithm
from bob.io.base import HDF5File
from bob.learn import em
from sklearn.metrics import classification_report, roc_curve, roc_auc_score
WAV_FOLDER = 'Wav/' #'ASV2015dataset/wav... |
dblyon/PandasIntro | Exercises_part_A_with_Solutions.ipynb | mit | %%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js')
"""
Explanation: <h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
End of explanation
"""
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.n... |
ktaneishi/deepchem | examples/notebooks/pong.ipynb | mit | import deepchem as dc
import numpy as np
class PongEnv(dc.rl.GymEnvironment):
def __init__(self):
super(PongEnv, self).__init__('Pong-v0')
self._state_shape = (80, 80)
@property
def state(self):
# Crop everything outside the play area, reduce the image size,
# and convert it to black and white... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/06_structured/1_explore.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb -l ${REGION} gs://$... |
keras-team/keras-io | examples/vision/ipynb/vivit.ipynb | apache-2.0 | !pip install -qq medmnist
"""
Explanation: Video Vision Transformer
Author: Aritra Roy Gosthipaty, Ayush Thakur (equal contribution)<br>
Date created: 2022/01/12<br>
Last modified: 2022/01/12<br>
Description: A Transformer-based architecture for video classification.
Introduction
Videos are sequences of images. Let's... |
oxy-cms/cms-tours | tours/2016-F_COMP-131_Li,J/RasPi-SimpleCV/DemoCV-orig-Copy0.ipynb | gpl-3.0 | from SimpleCV import *
from time import sleep
#import pytesseract
from bs4 import BeautifulSoup
"""
Explanation: The beginning of a demo
End of explanation
"""
cam = Camera()
"""
Explanation: Here we set up the camera
End of explanation
"""
img = cam.getImage()
"""
Explanation: Now we also get a single image fr... |
GraysonR/titanic-data-analysis | 2015-12-23-titanic-initial-exploration.ipynb | mit | # Import magic
%matplotlib inline
# More imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# Set up Seaborn
sns.set() # matplotlib defaults
# Load and show CSV data
titanic_data = pd.read_csv('titanic_data.csv')
titanic_data.head()
"""
Explanation: Exploring Titani... |
bharat-b7/NN_glimpse | 2.2 CNN HandsOn - MNIST Dataset.ipynb | unlicense | import numpy as np
import keras
from keras.datasets import mnist
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Load the datasets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
"""
Explanation: CNN HandsOn with Keras
Problem Definition
... |
ernestyalumni/CompPhys | Cpp/Integrate.ipynb | apache-2.0 | from itertools import combinations
import sympy
from sympy import Function, integrate, Product, Sum, Symbol, symbols
from sympy.abc import a,b,h,i,k,m,n,x
from sympy import Rational as Rat
def lagrange_basis_polys(N,x,xpts=None):
"""
lagrange_basis_polynomials(N,x,xpts)
returns the Lagrange basis polynomi... |
AnasFullStack/Awesome-Full-Stack-Web-Developer | algorithms/algorithm_analyisis.ipynb | mit | import time
def sumOfN(n):
start = time.time()
theSum = 0
for i in range(1,n+1):
theSum = theSum + i
end = time.time()
return theSum,end-start
for i in range(5):
print("Sum is %d required %10.7f seconds"%sumOfN(1000000))
"""
Explanation: 2. Algorithm Analysis
Book URL
2.2. What Is Algo... |
tensorflow/docs-l10n | site/en-snapshot/io/tutorials/prometheus.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... |
dvirsamuel/MachineLearningCourses | Visual Recognision - Stanford/assignment2/BatchNormalization.ipynb | gpl-3.0 | # As usual, a bit of setup
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_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
tensorflow/cloud | g3doc/tutorials/hp_tuning_cifar10_using_google_cloud.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... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day4/stackdiff_Narayan/01_Registration/Register_images_exercise.ipynb | mit | !ls *fits
"""
Explanation: Image Registration Exercise
Written by Gautham Narayan (gnarayan@stsci.edu) for LSST DSFP #5
In this directory, you should be able to find two fits file from one of the projects I worked on
End of explanation
"""
import astropy.io.fits as afits
from astropy.wcs import WCS
from astropy.visu... |
gilmana/Cu_transition_time_course- | data_explore_failed_clstr_mthds/Data_exploring_post_clustering_failure.ipynb | mit | %matplotlib inline
df2_TPM_values = df2_TPM.loc[:,"5GB1_FM40_T0m_TR2":"5GB1_FM40_T180m_TR1"]
df2_TPM_values
df2_TPM_values.describe()
df2_TPM.idxmax?
index = df2_TPM_values.sort("5GB1_FM40_T0m_TR2", ascending = False).index.tolist()
top_expressed = df2_TPM.loc[index].iloc[:20,:]
top_expressed.to_csv("top_expressed.... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/labs/b_estimator.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==2.6
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1>2b. Machine Learning using tf.estimator... |
tbarrongh/cosc-learning-labs | src/notebook/Interface.ipynb | apache-2.0 | import learning_lab
"""
Explanation: Learning Lab: Interface
This Learning Lab explores the network interfaces of a network device.
Pre-requisite Learning Lab: Inventory
|Keyword|Definition|
|--|--|
|Network Interface|Connector from one network device to a network interface on one or more other network devices.|
|Netw... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/distributed_estimation.ipynb | bsd-3-clause | import numpy as np
from scipy.stats.distributions import norm
from statsmodels.base.distributed_estimation import DistributedModel
def _exog_gen(exog, partitions):
"""partitions exog data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
jj = int(mi... |
GoogleCloudPlatform/mlops-with-vertex-ai | 06-model-deployment.ipynb | apache-2.0 | import os
import logging
logging.getLogger().setLevel(logging.INFO)
"""
Explanation: 06 - Model Deployment
The purpose of this notebook is to execute a CI/CD routine to test and deploy the trained model to Vertex AI as an Endpoint for online prediction serving. The notebook covers the following steps:
1. Run the test... |
dschick/udkm1Dsim | docs/source/examples/m3tm.ipynb | mit | import udkm1Dsim as ud
u = ud.u # import the pint unit registry from udkm1Dsim
import scipy.constants as constants
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
u.setup_matplotlib() # use matplotlib with pint units
"""
Explanation: Microscopic 3-Temperature-Model
Here we adapt the NTM from th... |
macks22/gensim | docs/notebooks/ldaseqmodel.ipynb | lgpl-2.1 | # setting up our imports
from gensim.models import ldaseqmodel
from gensim.corpora import Dictionary, bleicorpus
import numpy
from gensim.matutils import hellinger
"""
Explanation: Dynamic Topic Models Tutorial
What is this tutorial about?
This tutorial will exaplin what Dynamic Topic Models are, and how to use them ... |
philippgrafendorfe/stackedautoencoders | MNIST_Autoencoder.ipynb | mit | from keras.layers import Input, Dense, Dropout
from keras.models import Model
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.optimizers import RMSprop
from keras.callbacks import TensorBoard
from __future__ import print_function
from IPython.display import SVG, Image
from ke... |
tclaudioe/Scientific-Computing | SC1v2/02_floating_point_arithmetic.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: <center>
<img src="http://sct.inf.utfsm.cl/wp-content/uploads/2020/04/logo_di.png" style="width:60%">
<h1> INF-285 - Computación Científica </h1>
<h2> Floating Point Arithmetic </h2>
<h2> <a href="#acknowledgements">... |
harpolea/pyro2 | multigrid/variable_coeff_elliptic.ipynb | bsd-3-clause | %pylab inline
from sympy import init_session
init_session()
alpha = 2.0 + cos(2*pi*x)*cos(2*pi*y)
phi = sin(2*pi*x)*sin(2*pi*y)
"""
Explanation: Variable Coefficient Poisson
Derive the form of a test variable-coefficient elliptic equation with periodic boundary conditions for testing the variable-coefficient multig... |
tensorflow/docs-l10n | site/ja/tensorboard/tensorboard_projector_plugin.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... |
scikit-optimize/scikit-optimize.github.io | 0.8/notebooks/auto_examples/hyperparameter-optimization.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
"""
Explanation: Tuning a scikit-learn estimator with skopt
Gilles Louppe, July 2016
Katie Malone, August 2016
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
If you are looking for a :obj:sklearn.model_selection.GridSearchCV replacement checkout
sphx_glr_auto_examples_... |
ysh329/Homework | CS100.1x Introduction to Big Data with Apache Spark/lab2_apache_log_student.ipynb | mit | from pyspark.sql import Row
Person = Row("name", "age")
print Person
ali = Person("Alice", 11)
print ali
help(Row)
import re
import datetime
from pyspark.sql import Row
month_map = {'Jan': 1, 'Feb': 2, 'Mar':3, 'Apr':4, 'May':5, 'Jun':6, 'Jul':7,
'Aug':8, 'Sep': 9, 'Oct':10, 'Nov': 11, 'Dec': 12}
def parse_ap... |
f-guitart/data_mining | notes/97 - Approaximate String Matching.ipynb | gpl-3.0 | import pandas as pd
names = pd.DataFrame({"name" : ["Alice","Bob","Charlie","Dennis"],
"surname" : ["Doe","Smith","Sheen","Quaid"]})
names
names.name.str.match("A\w+")
debts = pd.DataFrame({"debtor":["D.Quaid","C.Sheen"],
"amount":[100,10000]})
debts
"""
Explanation: String... |
liganega/Gongsu-DataSci | ref_materials/exams/2017/A01/finalexam_20171215.ipynb | gpl-3.0 | a = np.arange(6) + np.arange(0, 51, 10)[:, np.newaxis]
a
"""
Explanation: 2017년 2학기 공학수학 기말고사
이름 :
학번 :
시험에서 사용하는 모듈 임포트 하기
import __future__ import division, print_function
import numpy as np
import pandas as pd
from datetime import datetime as dt
넘파이 어레이 인덱싱과 슬라이싱
아래 코드로 생성된 어레이를 이용하는 문제이다.
End of explanation
"""
... |
tensorflow/docs-l10n | site/ko/probability/examples/Gaussian_Process_Regression_In_TFP.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
buguen/pylayers | pylayers/notebooks/Simultraj_and_CorSer.ipynb | lgpl-3.0 | m
for k,lk in enumerate(llinks):
print k,lk
"""
Explanation: Set links
Simulation has an object network which contains a links dictionnary.
This dictionnary has wireless standard as key, and list of links with their type as value.
End of explanation
"""
link={'ieee802154':[]}
link['ieee802154'].append(S.N.links[... |
shuaiyuancn/commercial-science-taster | notebooks/00 Ta-feng dataset.ipynb | apache-2.0 | import os
import pandas as pd
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : 14}
plt.rc('font', **font)
plt.rc('figure', figsize=(18, 6))
os.chdir('../datasets/ta-feng/')
"""
Exp... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/Fundamentals/Basic-ConvolutionNeuralNetworks-ImageProcessing.ipynb | apache-2.0 | # TODO: @Sam, please fill this in...
import tensorflow as tf
import numpy as np
from IPython.display import clear_output, Image, display, HTML
# Note: All datasets are available here: /root/pipeline/datasets/...
# Modules required for file download and extraction
import os
import sys
import tarfile
from six.moves... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/02_execution/WIN-190813181020.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Service Creation
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/13 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hypothes... |
patrick-kidger/diffrax | examples/continuous_normalising_flow.ipynb | apache-2.0 | import math
import os
import pathlib
import time
from typing import List, Tuple
import diffrax
import equinox as eqx # https://github.com/patrick-kidger/equinox
import imageio
import jax
import jax.lax as lax
import jax.nn as jnn
import jax.numpy as jnp
import jax.random as jrandom
import matplotlib.pyplot as plt
imp... |
ajayrfhp/dvd | examples/MNIST.ipynb | mit | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=False)
img = mnist.train.images[123]
img = np.reshape(img,(28,28))
plt.imshow(img, cmap = 'gray')
plt.show()
img = np.reshape... |
JasonSanchez/w261 | week3/MIDS-W261-HW-03-Sanchez.ipynb | mit | %%writefile ComplaintDistribution.py
from mrjob.job import MRJob
class ComplaintDistribution(MRJob):
def mapper(self, _, lines):
line = lines[:30]
if "Debt collection" in line:
self.increment_counter('Complaint', 'Debt collection', 1)
elif "Mortgage" in line:
self.in... |
googledatalab/notebooks | samples/ML Toolbox/Regression/Census/4 Service Evaluate.ipynb | apache-2.0 | import google.datalab as datalab
import google.datalab.ml as ml
import mltoolbox.regression.dnn as regression
import os
"""
Explanation: Evaluating a Model with Dataflow and BigQuery
This notebook is the third in the set of steps to run machine learning on the cloud. In this step, we will use the model training in the... |
AntArch/Presentations_Github | 20150916_OGC_Reuse_under_licence/20150916_OGC_Reuse_under_licence.ipynb | cc0-1.0 | from IPython.display import YouTubeVideo
YouTubeVideo('F4rFuIb1Ie4')
## PDF output using pandoc
import os
### Export this notebook as markdown
commandLineSyntax = 'ipython nbconvert --to markdown 20150916_OGC_Reuse_under_licence.ipynb'
print (commandLineSyntax)
os.system(commandLineSyntax)
### Export this noteboo... |
egentry/dwarf_photo-z | dwarfz/data/get_training_galaxy_images.ipynb | mit | # give access to importing dwarfz
import os, sys
dwarfz_package_dir = os.getcwd().split("dwarfz")[0]
if dwarfz_package_dir not in sys.path:
sys.path.insert(0, dwarfz_package_dir)
import dwarfz
# back to regular import statements
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
s... |
danielfather7/teach_Python | SEDS_Hw/seds-hw4-coding-and-testing-big-league-danielfather7/SEDS-HW4.ipynb | gpl-3.0 | import knn
import pandas as pd
test = pd.read_csv('testing.csv')
data = pd.read_csv('atomsradii.csv')
test
data
"""
Explanation: K-NN Classifier
Import packages and data:
End of explanation
"""
"""
def dist(vec1,vec2):
#Use scipy package to calculate Euclidean distance.
from scipy.spatial import distance
... |
mfouesneau/pyphot | examples/QuickStart.ipynb | mit | %matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
import pyphot
"""
Explanation: pyphot - A tool for computing photometry from spectra
Some examples are provided in this notebook
Full documentation available at http://mfouesneau.github.io/docs/pyphot/
End of explanation
"""
... |
karlstroetmann/Artificial-Intelligence | Python/4 Automatic Theorem Proving/Parser.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'NUMBER', 'VAR', 'FCT', 'BACKSLASH' ]
"""
Explanation: A Simple Parser for Term Rewriting
This file implements a parser for terms and equations. It uses the parser generator Ply. To install Ply, change the cell below into a code cell and execute it. If the package ply is already in... |
jenshnielsen/HJCFIT | exploration/OpenMP_example.ipynb | gpl-3.0 | import os
os.environ['OMP_NUM_THREADS'] = '4'
"""
Explanation: OpenMP example
In this example we illustrate how OpenMP can be used to speedup the calculation of the likelihood.
First we set the number of openmp threads. This is done via an environmental variable called OMP_NUM_THREADS. In this example we set the value... |
manipopopo/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb | apache-2.0 | !pip install unidecode
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Text Generation using a RNN
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/... |
jmfranck/pyspecdata | docs/_downloads/871a80cfd7a71c1edf9cefede4305190/matrix_mult.ipynb | bsd-3-clause | # -*- coding: utf-8 -*-
from pylab import *
from pyspecdata import *
from numpy.random import random
import time
init_logging('debug')
"""
Explanation: Matrix Multiplication
Various ways of implementing different matrix multiplications.
Read the documentation embedded in the code.
End of explanation
"""
a_nd = nddat... |
computational-class/cjc2016 | code/sympy.ipynb | mit | sy.integrate(6*x**5, x)
sy.integrate(x**3, (x, 0, 10)) #定积分
sy.integrate(6*x**5+y, x,y) #双重不定积分
sy.integrate(x**3+y, (x, -1, 1),(y,1,3) ) #双重定积分
"""
Explanation: 表达式变换
sy.exp(sy.I*x).expand(complex=True) # 展开为复数, 可理解为将x当作复数处理 表达式展开
sy.cos(x).series(x, 0, 10) # .series(var, point, orde... |
ageron/tensorflow-safari-course | 01_basics_phases_ex1.ipynb | apache-2.0 | from __future__ import absolute_import, division, print_function, unicode_literals
"""
Explanation: Try not to peek at the solutions when you go through the exercises. ;-)
First let's make sure this notebook works well in both Python 2 and Python 3:
End of explanation
"""
import tensorflow as tf
tf.__version__
"""
... |
omaraltaher/kaggle-pizza-project | Random_Acts_of_Pizza_Kaggle_Competition_Project.ipynb | mit | # This tells matplotlib not to try opening a new window for each plot.
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# General libraries.
import json
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
# SK-learn libraries for learning.
from sklearn.... |
h-mayorquin/time_series_basic | presentations/2016-01-25(Wall-Street-Letters-Visualizations-Of-The-Spatio-Temporal-Clusters).ipynb | bsd-3-clause | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
# Now nexa modules|
import sys
sys.path.append("../")
from visualization.sensor_clustering import visualize_clusters_text_to_image
"""
Explanation: Visualization of the Spatiotemporal clusters.
Here we present a visualization of how th... |
Soil-Carbon-Coalition/atlasdata | Challenge observations -- cleanup and classification.ipynb | mit | # The preamble
import pandas as pd
#pd.set_option('mode.sim_interactive', True)
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
#from collections import OrderedDict
import json, csv
import re
df
"""
Explanation: This notebook is about classifying Challenge data according to type, and some clean... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/tfx/template.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... |
astroumd/GradMap | notebooks/Lectures2021/Lecture1/GradMap_L1_Student.ipynb | gpl-3.0 | ## You can use Python as a calculator:
5*7 #This is a comment and does not affect your code.
#You can have as many as you want.
#Comments help explain your code to others and yourself.
#No worries.
5+7
5-7
5/7
"""
Explanation: Introduction to "Doing Science" in Python for REAL Beginners
Python is one of many lan... |
google/earthengine-community | tutorials/sentinel-2-s2cloudless/index.ipynb | apache-2.0 | import ee
# Trigger the authentication flow.
ee.Authenticate()
# Initialize the library.
ee.Initialize()
"""
Explanation: Sentinel-2 Cloud Masking with s2cloudless
Author: jdbcode
This tutorial is an introduction to masking clouds and cloud shadows in Sentinel-2 (S2) surface reflectance (SR) data using Earth Engine.... |
kgrodzicki/machine-learning-specialization | course-3-classification/module-4-linear-classifier-regularization-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Logistic Regression with L2 regularization
The goal of this second notebook is to implement your own logistic regression classifier with L2 regularization. You will do the following:
Extract features from Amazon product reviews.
Convert an SFrame into a... |
ethen8181/machine-learning | python/algorithms/basic_data_structure.ipynb | mit | from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
themes = get_themes()
set_nb_theme(themes[1])
%load_ext watermark
%watermark -a 'Ethen' -d -t -v -p jupyterthemes
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span... |
WNoxchi/Kaukasos | FADL1/L3CA2_rossmann-Copy1.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
# from fastai.imports import *
# from fastai.torch_imports import *
from fastai.structured import * # non-PyTorch specfc Machine-Learning tools; indep lib
# from fastai.dataset import * # lets us do fastai PyTorch stuff w/ structured columnar data
from fastai.col... |
atavory/ibex | examples/in_prog/movielens_simple_row_aggregating_features_with_stacking.ipynb | bsd-3-clause | from sklearn import datasets
print(datasets.load_boston()['DESCR'])
import os
from sklearn import base
from scipy import stats
import pandas as pd
import seaborn as sns
sns.set_style('whitegrid')
sns.despine()
import ibex
from ibex.sklearn import model_selection as pd_model_selection
from ibex.sklearn import linear... |
kylepjohnson/notebooks | skflow/tensorflow, misc.ipynb | mit | import tensorflow.contrib.learn as skflow
from sklearn.datasets import load_iris
from sklearn import metrics
iris = load_iris()
iris.keys()
iris.feature_names
iris.target_names
# Withhold 3 for testing
test_idx = [0, 50, 100]
train_data = np.delete(iris.data, test_idx, axis=0)
train_target = np.delete(iris.target... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/STS_approximate_inference_for_models_with_non_Gaussian_observations.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
BadWizard/intro_programming | notebooks/classes.ipynb | mit | class Rocket():
# Rocket simulates a rocket ship for a game,
# or a physics simulation.
def __init__(self):
# Each rocket has an (x,y) position.
self.x = 0
self.y = 0
"""
Explanation: Classes
So far you have learned about Python's core data types: strings, numbers, lists, tupl... |
emiliom/stuff | DRB_vizer_json_services.ipynb | cc0-1.0 | import json
import requests
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import datetime
import time
import calendar
import pytz
#from matplotlib.dates import date2num, num2date
utc_tz = pytz.utc
def epochsec_to_dt(epochsec):
""" Return the datetime object for epoc... |
strandbygaard/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
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')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
turbomanage/training-data-analyst | courses/fast-and-lean-data-science/06_MNIST_Estimator_to_TPUEstimator.ipynb | apache-2.0 | import os, re, math, json, shutil, pprint, datetime
import PIL.Image, PIL.ImageFont, PIL.ImageDraw # "pip3 install Pillow" or "pip install Pillow" if needed
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.python.platform import tf_logging
print("Tensorflow version " + tf... |
joseerlang/PySpark_docker | notebook/Contador de palabras.ipynb | apache-2.0 | fileName='book.txt'
"""
Explanation: Leer texto
Lo primero que tenemos que hacer es cargar el texto. Para nuestro ejemplo, cargaremos una obra del proyecto
Gutenberg.
End of explanation
"""
import re
def removePunctuation(text):
return re.sub('[^a-z| |0-9]', '', text.strip().lower())
"""
Explanation: Ahora vam... |
thomasantony/CarND-Projects | Exercises/Term1/TensorFlow-L2/lab.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
print('All m... |
gregunz/ada2017 | 01 - Pandas and Data Wrangling/Homework 1.ipynb | mit | # all imports here
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import os
import glob
import re
import math
import calendar
import datetime
import random
from calendar import monthrange
from sklearn import datasets, linear_model, ensemble
from sklearn.model_selection i... |
mne-tools/mne-tools.github.io | dev/_downloads/7119b87595b4873e173edd147fee099b/dics_source_power.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import mne
from mne.datasets import somato
from ... |
StingraySoftware/notebooks | Transfer Functions/Data Preparation.ipynb | mit | from PIL import Image
im = Image.open('2d.png')
width, height = im.size
"""
Explanation: Setting Up Data
We use Image module from Python Imaging library to digitize 2-d plot from Uttley et al. (2014)
End of explanation
"""
intensity = np.array([[1 for j in range(width)] for i in range(height)])
"""
Explanation: In... |
hcorona/recsys-101-workshop | notebooks/notebook-2-toy-example.ipynb | mit | import os
os.chdir('..')
# Import all the packages we need to generate recommendations
import numpy as np
import pandas as pd
import src.utils as utils
import src.recommenders as recommenders
import src.similarity as similarity
# Enable logging on Jupyter notebook
import logging
logger = logging.getLogger()
logger.se... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_PY_Recursion_Factorial_Example.ipynb | mit | # some may find this code more readable ... single line solutions to the function are provided immediately below it.
import sys
n = int(input())
def factorial(n):
if n <= 1:
return n
else:
return n*factorial(n-1)
factorial(n)
# this cell explores creating a function that solves the problem with... |
ganguli-lab/twpca | notebooks/demo-crossval.ipynb | mit | from twpca.datasets import jittered_population
from scipy.ndimage import gaussian_filter1d
rates, spikes = jittered_population()
smooth_std = 1.0
data = gaussian_filter1d(spikes, smooth_std, axis=1)
fig, axes = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(9,3))
for ax, trial in zip(axes, data):
ax.imshow(... |
fancompute/ceviche | examples/simulate_splitter_fdtd.ipynb | mit | def reshape_arr(arr, Nx, Ny):
return arr.reshape((Nx, Ny, 1))
eps_r = np.load('data/eps_r_splitter4.npy')
eps_wg = np.load('data/eps_waveguide.npy')
plt.imshow(eps_r.T, cmap='gist_earth_r')
plt.show()
Nx, Ny = eps_r.shape
J_in = np.load('data/J_in.npy')
J_outs = np.load('data/J_list.npy')
J_wg = np.flipud(J_in.c... |
statsmaths/stat665 | lectures/lec18/notebook18.ipynb | gpl-2.0 | %pylab inline
import copy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.datasets import mnist, cifar10
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Activation, Flatten, Reshape
from keras.optimizers import SGD, RMSprop
from keras.utils i... |
mne-tools/mne-tools.github.io | 0.24/_downloads/f398f296c84e53a14339d2c3c36e91a4/movement_detection.ipynb | bsd-3-clause | # Authors: Adonay Nunes <adonay.s.nunes@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# License: BSD-3-Clause
import os.path as op
import mne
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_movement, compute_average_dev_head_t
# Load dat... |
davidgutierrez/HeartRatePatterns | Jupyter/LoadDataMimic-II.ipynb | gpl-3.0 | import sys
sys.version_info
"""
Explanation: Cargue de datos s SciDB
1) Verificar Prerequisitos
Python
SciDB-Py requires Python 2.6-2.7 or 3.3
End of explanation
"""
import numpy as np
np.__version__
"""
Explanation: NumPy
tested with version 1.9 (1.13.1)
End of explanation
"""
import requests
requests.__version_... |
jonathansick/androcmd | notebooks/Brick 23 IR V2.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina'
# %config InlineBackend.figure_format='svg'
import os
import time
from glob import glob
import numpy as np
brick = 23
STARFISH = os.getenv("STARFISH")
isoc_dir = "b23ir2_isoc"
lib_dir = "b23ir2_lib"
synth_dir = "b23ir2_synth"
fit_dir = "b23ir2_fit"
wfc3_... |
random-forests/tensorflow-workshop | archive/zurich/01_tensorflow_warmup.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
"""
Explanation: TensorFlow warmup
This is a notebook to get you started with TensorFlow.
End of explanation
"""
# This is for graph visualization.
from IPython.display import clear_output, Image, display, HTML
def strip_consts(graph_def, max_const_size=32):
"""Strip ... |
DiXiT-eu/collatex-tutorial | unit8/unit8-collatex-and-XML/Read files.ipynb | gpl-3.0 | import os
os.listdir('partonopeus')
"""
Explanation: Loading and parsing XML files from the file system
Our XML files are in a subdirectory called 'partonopeus'. We load the os library and use its listdir() method to verify the contents of that directory.
End of explanation
"""
inputFiles = {}
for inputFile in os.li... |
lionell/laboratories | eco_systems/dima4.ipynb | mit | from scipy.integrate import ode
birth_rate = 128
death_rate = 90
intraspecific_competition = 2
ps = [birth_rate, death_rate, intraspecific_competition]
def f(t, N, ps):
return ps[0] * (N ** 2) / (N + 1) - ps[1] * N - ps[2] * (N ** 2)
def solve(N0, t0=0, t1=1, h=0.05):
r = ode(f).set_integrator('dopri5')
... |
jeffzhengye/pylearn | tensorflow_learning/tf2/notebooks/transfer_learning-中文-checkpoint.ipynb | unlicense | import numpy as np
import tensorflow as tf
from tensorflow import keras
"""
Explanation: 迁移学习与微调，Transfer learning & fine-tuning
Author: fchollet<br>
Date created: 2020/04/15<br>
Last modified: 2020/05/12<br>
Description: Complete guide to transfer learning & fine-tuning in Keras. <br>
翻译： 叶正
设置，Setup
End of explanati... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/09_sequence/labs/sinewaves.ipynb | apache-2.0 | # You must update BUCKET, PROJECT, and REGION to proceed with the lab
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
SEQ_LEN = 50
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['SEQ_LEN'] = str(SEQ_LEN)
os.env... |
KJE2001/seminars | 02_linear_algebra.ipynb | mit | import numpy as np
a = np.array([1, 2, 3]) # Create a rank 1 array
print(type(a)) # Prints "<type 'numpy.ndarray'>"
print(a.shape) # Prints "(3,)"
"""
Explanation: <figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
Linear algebra crash course
Roberto Di Remigio, Luc... |
rafburzy/Statistics | 05_hypothesis_testing.ipynb | mit | # importing required modules
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# runing the functions script
%run stats_func.py
# loading the iris dataset
df = pd.read_csv('iris.csv')
df.head()
# for further analysis sepal length and sepal width will be u... |
ShubhamDebnath/Coursera-Machine-Learning | Course 5/Neural machine translation with attention v4.ipynb | mit | from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
from keras.layers import RepeatVector, Dense, Activation, Lambda
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.models import load_model, Model
import keras.backend as K
import numpy as np
from... |
learn1do1/learn1do1.github.io | python_notebooks/Watershed Problem.ipynb | mit | heights = [[1,2,3,4],[1,2,1,0],[1,0,1,0],[0,0,1,4]]
class position():
def __init__(self, coordinates, height):
self.coordinates = coordinates
self.height = height
def __repr__(self):
return ','.join([str(self.coordinates), str(self.height)])
"""
Explanation: Watershed Problem
... |
qutip/qutip-notebooks | examples/smesolve-jc-photocurrent.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import qutip.settings
from qutip import *
from qutip.ipynbtools import HTMLProgressBar
from matplotlib import rcParams
rcParams['font.family'] = 'STIXGeneral'
rcParams['mathtext.fontset'] = 'stix'
rcParams['font.size'] = '14'
N = 15
w0 = 1.0 * 2... |
hawkdidy/Southwest_Report | Exploring_Domestic_Flights.ipynb | mit | df_flights_2008 = pd.read_csv('/home/hakim/Documents/Airline_delays-/flight_data_historical/2008.csv')
print(df_flights_2008.tail())
df_flights_2008.shape
df_flights_2008.isnull().sum(axis=0)
"""
Explanation: This notebook will be describing an analysis of US domestic flight data. We will first take a dive into a s... |
srcole/qwm | misc/Power spectra of nonstationary rhythms.ipynb | mit | import numpy as np
import neurodsp
%matplotlib inline
import matplotlib.pyplot as plt
np.random.seed(0)
"""
Explanation: It was once claimed
Alpha rhythms have nonzero power only at an alpha-band frequency and its higher harmonics
However, this assumes the alpha rhythm is stationary, which is often not true in neur... |
vpoggi/catalogue_toolkit | notebooks/Homogenisation.ipynb | agpl-3.0 | parser = ISFReader("inputs/isc_test_catalogue_isf.txt",
selected_origin_agencies=["ISC", "GCMT", "HRVD", "NEIC", "EHB", "BJI"],
selected_magnitude_agencies=["ISC", "GCMT", "HRVD", "NEIC", "BJI"])
catalogue = parser.read_file("ISC_DB1", "ISC Global M >= 5")
print "Catalogue contains... |
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