repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
nerdcommander/scientific_computing_2017 | lesson16/Lesson16_team_imp.ipynb | mit | def factorial(n):
"""calculates n factorial"""
print('n is ', n)
if n == 0:
return 1
else:
print('need factorial of', n-1)
answer = factorial(n-1)
print ('factorial of ', n-1, 'was', answer)
return answer * n
factorial(3)
"""
Explanation: Unit 2: Programming Des... |
mqvist/CarND-Behavioral-Cloning | Experiment_2.ipynb | mit | import os
from PIL import Image
def get_record_and_image(index):
record = df.iloc[index]
path = os.path.join('data', record.center)
return record, Image.open(path)
def layer_info(model):
for n, layer in enumerate(model.layers, 1):
print('Layer {:2} {:16} input shape {} output shape {}'.format(... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/supplemental/labs/deepconv_gan.ipynb | apache-2.0 | try:
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
tf.__version__
# To generate GIFs
!python3 -m pip install -q imageio
import glob
import os
import time
import imageio
import matplotlib.pyplot as plt
import numpy as np
import PIL
from IPython import display
from tensorflow.keras i... |
WNoxchi/Kaukasos | FADL1/L3CA2_rossmann_old.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... |
m2dsupsdlclass/lectures-labs | labs/06_deep_nlp/Transformers_Joint_Intent_Classification_Slot_Filling.ipynb | mit | import tensorflow as tf
tf.__version__
!nvidia-smi
# TODO: update this notebook to work with the latest version of transformers
%pip install -q transformers==2.11.0
"""
Explanation: Joint Intent Classification and Slot Filling with Transformers
The goal of this notebook is to fine-tune a pretrained transformer-based... |
mne-tools/mne-tools.github.io | 0.19/_downloads/85e12f42707b248635bc0c477c2ffc2f/plot_mne_solutions.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
# Read data
fname_evoked = data_path + '/MEG/s... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_dipole_fit.ipynb | bsd-3-clause | from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.... |
ernestyalumni/servetheloop | CurveFit/CurveFit.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.optimize import curve_fit
from scipy.stats import gamma # for drag vs. v fit
SUBDIR = './rawdata/' # subdirectory with all data
"""
Explanation: CurveFit
Various (nonlinear) curve fitting methods needed at various t... |
vkpedia/databuff | random-walks/YouTube-Spam/YouTube_Spam_Collection (Part 2).ipynb | mit | # Import modules
import numpy as np
import pandas as pd
"""
Explanation: YouTube Spam Collection Data Set (Part 2)
Source: UCI Machine Learning Repository
Original Source: YouTube Spam Collection v. 1
Alberto, T.C., Lochter J.V., Almeida, T.A. Filtragem Automática de Spam nos Comentários do YouTube. Anais do XII Enc... |
tensorflow/docs-l10n | site/zh-cn/guide/autodiff.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... |
guyk1971/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
Merinorus/adaisawesome | Homework/01 - Pandas and Data Wrangling/temp/Data Wrangling with Pandas.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('notebook')
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Data-Wrangling-with-Pandas"><span class="toc-item-num">1 </span>Data Wrangling with Pandas</a></div><d... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_stats_cluster_spatio_temporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne import (io, spatial_tris_connectivity, comput... |
dsquareindia/gensim | docs/notebooks/Corpora_and_Vector_Spaces.ipynb | lgpl-2.1 | import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Tutorial 1: Corpora and Vector Spaces
See this gensim tutorial on the web here.
Don’t forget to set:
End of explanation
"""
from gensim import corpora
documents = ["Human machine interface for... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/01-NLP-Python-Basics/04-Stop-Words.ipynb | apache-2.0 | # Perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
# Print the set of spaCy's default stop words (remember that sets are unordered):
print(nlp.Defaults.stop_words)
len(nlp.Defaults.stop_words)
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
... |
jdhp-docs/python_notebooks | nb_dev_python/python_keras_1d_non-linear_regression.ipynb | mit | import tensorflow as tf
tf.__version__
import keras
keras.__version__
import h5py
h5py.__version__
import pydot
pydot.__version__
"""
Explanation: Basic 1D non-linear regression with Keras
TODO: see https://stackoverflow.com/questions/44998910/keras-model-to-fit-polynomial
Install Keras
https://keras.io/#installati... |
spencer2211/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
ECP-CANDLE/Supervisor | workflows/cp1/scripts/cp1_scripts.ipynb | mit | df = pd.read_csv('~/Documents/results/cp1/non_nci_hpo_log/hpos.txt', sep="|", header=None, names=["i", "hpo_id", "params", "run_dir", "ts", "val_loss"])
df.groupby("hpo_id")['val_loss'].agg([np.min, np.max, np.mean, np.std])
n = 10
smallest = df.groupby('hpo_id')['val_loss'].nsmallest(n)
best_n = df.iloc[smallest.in... |
IanOlin/github-research | Unsupported/Affilliation/csvs/.ipynb_checkpoints/arrayify-checkpoint.ipynb | mit | importpath = "/home/jwb/repos/github-research/csvs/Companies/Ugly/Stack/"
exportpath = "/home/jwb/repos/github-research/csvs/Companies/Pretty/Stack/"
"""
Explanation: Ugly To Pretty for CSVS
Run on linux. Set an import path and an export path to folders.
Will take every file in import directory that is a mathematica g... |
dsiufl/2015-Fall-Hadoop | notes/.ipynb_checkpoints/1-hadoop-streaming-py-wordcount-checkpoint.ipynb | mit | hadoop_root = '/home/ubuntu/shortcourse/hadoop-2.7.1/'
hadoop_start_hdfs_cmd = hadoop_root + 'sbin/start-dfs.sh'
hadoop_stop_hdfs_cmd = hadoop_root + 'sbin/stop-dfs.sh'
# start the hadoop distributed file system
! {hadoop_start_hdfs_cmd}
# show the jave jvm process summary
# You should see NamenNode, SecondaryNameNod... |
TariqAHassan/BioVida | tutorials/1_openi.ipynb | bsd-3-clause | from biovida.images import OpeniInterface
opi = OpeniInterface()
"""
Explanation: BioVida: Open-i
Open-i is an open access biomedical search engine provided by the US National Institutes of Health. The service grants programmatic access to its over 1.2 million images through a RESTful web API. BioVida provides an ea... |
cathywu/flow | tutorials/tutorial12_inflows.ipynb | mit | from flow.scenarios import MergeScenario
"""
Explanation: Tutorial 12: Inflows
This tutorial walks you through the process of introducing inflows of vehicles into a network. Inflows allow us to simulate open networks where vehicles may enter (and potentially exit) the network consanstly, such as a section of a highway... |
tpin3694/tpin3694.github.io | python/group_pandas_data_by_hour_of_the_day.ipynb | mit | # Import libraries
import pandas as pd
import numpy as np
"""
Explanation: Title: Group Pandas Data By Hour Of The Day
Slug: group_pandas_data_by_hour_of_the_day
Summary: Group data by hour of the day using pandas.
Date: 2016-12-21 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Preliminaries
End of... |
adamwang0705/cross_media_affect_analysis | develop/20171011-daheng-check_topics_basic_statistics.ipynb | mit | """
Initialization
"""
'''
Standard modules
'''
import os
import pickle
import sqlite3
import time
from pprint import pprint
'''
Analysis modules
'''
import pandas as pd
'''
Custom modules
'''
import config
import utilities
'''
Misc
'''
nb_name = '20171011-daheng-check_topics_basic_statistics'
"""
Explanation: Ch... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
jshudzina/keras-tutorial | notebooks/01-TensorPoisonousMushrooms.ipynb | apache-2.0 | from pandas import read_csv
srooms_df = read_csv('../data/agaricus-lepiota.data.csv')
from sklearn_pandas import DataFrameMapper
import sklearn
import numpy as np
mappings = ([
('edibility', sklearn.preprocessing.LabelEncoder()),
('odor', sklearn.preprocessing.LabelBinarizer()),
('habitat', sklearn.preproc... |
dnaneet/ELC | DATA/ELC_computable_report_AY1920.ipynb | gpl-3.0 | #@title
#%%capture
import numpy as np #Linear algebra
import pandas as pd #Time series, datetime object manipulation
import matplotlib.pyplot as plt #plotting
#import seaborn as sb
#plt.style.use('fivethirtyeight') #Plot style preferred by author.
import calendar
from tabulate import tabulate #pretty display of table... |
darkomen/TFG | modelado/temperatura/modelado.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Mostramos todos los gráficos en el n... |
keras-team/keras-io | examples/vision/ipynb/edsr.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
AUTOTUNE = tf.data.AUTOTUNE
"""
Explanation: Enhanced Deep Residual Networks for single-image super-resolution
Author: Gitesh Chawda<br>
Date ... |
google-research/google-research | dnn_predict_accuracy/colab/dnn_predict_accuracy.ipynb | apache-2.0 | from __future__ import division
import time
import os
import json
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import colors
import pandas as pd
import seaborn as sns
from scipy import stats
from tensorflow import keras
from tensorflow.io import gfile
import lightgbm as lgb
DATAF... |
catalyst-cooperative/pudl | test/validate/notebooks/validate_gf_eia923.ipynb | mit | %load_ext autoreload
%autoreload 2
import sys
import pandas as pd
import sqlalchemy as sa
import pudl
import warnings
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(stream=sys.stdout)
formatter = logging.Formatter('%(message)s')
handler.setFormatter(formatter... |
neeasthana/ML-SQL | ML-SQL/ML-SQL-initialDemo.ipynb | gpl-3.0 | #Libraries
#from pyparsing import Word, Literal, alphas, Optional, OneOrMore, Group, Or, Combine, oneOf
from pyparsing import *
import string
import sys
import pandas as pd
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
"""
Explanation:... |
ianhamilton117/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... |
google/empirical_calibration | notebooks/kang_schafer_population_mean.ipynb | apache-2.0 | #@title Copyright 2019 The Empirical Calibration Authors.
# 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 l... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/SimilarMovies.ipynb | gpl-3.0 | import pandas as pd
r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3))
m_cols = ['movie_id', 'title']
movies = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.item', sep='|', names=m_cols, usecols=... |
liganega/Gongsu-DataSci | previous/notes2017/W10/GongSu22_Statistics_Population_Variance.ipynb | gpl-3.0 | from GongSu21_Statistics_Averages import *
"""
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
모집단 분산 점추정
안내사항
지난 시간에 다룬 21장 내용을 활용하고자 한다.
따라서 아래와 같이 21장 내용을 모듈로 담고 있는 파이썬 파일을 임포트 해야 한다.
주의: GongSu21_Statistics_Averages.py 파일이 동일한 디렉토리에 있어야 한다.
End of explanation
"""
p... |
etpinard/delightfulsoup | examples/ipython-notebook/notebook.ipynb | mit | import plotly
plotly.__version__
"""
Explanation: Plotly maps
with Plotly's Python API library and Basemap
This notebook comes in response to <a href="https://twitter.com/rjallain/status/496767038782570496" target="_blank">this</a> Rhett Allain tweet.
Although Plotly does not feature built-in maps functionality (yet)... |
gaufung/ISL | training-materials/Stasmodels-training/Regrssion Diagnostics.ipynb | mit | from statsmodels.compat import lzip
import statsmodels
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.stats.api as sms
import matplotlib.pyplot as plt
# Load data
url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
dat = pd.read_csv(url)
# Fit... |
peastman/deepchem | examples/tutorials/The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb | mit | !pip install --pre deepchem
"""
Explanation: The Basic Tools of the Deep Life Sciences
Welcome to DeepChem's introductory tutorial for the deep life sciences. This series of notebooks is a step-by-step guide for you to get to know the new tools and techniques needed to do deep learning for the life sciences. We'll sta... |
flowersteam/naminggamesal | notebooks/5_Intro_Experiment.ipynb | agpl-3.0 | import naminggamesal.ngsimu as ngsimu
"""
Explanation: Experiments
End of explanation
"""
xp_cfg={
'pop_cfg':{
'voc_cfg':{
'voc_type':'matrix',
'M':5,
'W':10
},
'strat_cfg':{
'strat_type':'success_threshold',
'voc_update':'Mi... |
jeffzhengye/pylearn | .ipynb_checkpoints/jpx-tokyo-simple-lstm-network-scuec-checkpoint.ipynb | unlicense | # check gpu env with torch
import torch
print(torch.__version__) # 查看torch当前版本号
print(torch.version.cuda) # 编译当前版本的torch使用的cuda版本号
print("is_cuda_available:", torch.cuda.is_available()) # 查看当前cuda是否可用于当前版本的Torch，如果输出
print('gpu count:', torch.cuda.device_count())
# 查看指定GPU的容量、名称
device = "cuda:0"
print(f"{devic... |
microsoft/dowhy | docs/source/example_notebooks/load_graph_example.ipynb | mit | import os, sys
import random
sys.path.append(os.path.abspath("../../../"))
import numpy as np
import pandas as pd
import dowhy
from dowhy import CausalModel
from IPython.display import Image, display
"""
Explanation: Different ways to load an input graph
We recommend using the GML graph format to load a graph. You c... |
mwcraig/reducer | reducer/reducer-template.ipynb | bsd-3-clause | import reducer.gui
import reducer.astro_gui as astro_gui
from reducer.image_browser import ImageBrowser
from ccdproc import ImageFileCollection
from reducer import __version__
print(__version__)
"""
Explanation: Reducer: (Put your name here)
Reviewer: (Put your name here)
jupyter notebook crash course
Click on a cod... |
bjodah/aqchem | examples/kinetics_cstr.ipynb | bsd-2-clause | from collections import defaultdict
import numpy as np
from IPython.display import Latex
import matplotlib.pyplot as plt
from pyodesys.symbolic import SymbolicSys
from chempy import Substance, ReactionSystem
from chempy.kinetics.ode import get_odesys
from chempy.units import SI_base_registry, default_units as u
from ch... |
mdda/pycon.sg-2015_deep-learning | ipynb/blocks-introduction-mnist.ipynb | mit | from theano import tensor
x = tensor.matrix('features')
"""
Explanation: Introduction tutorial
In this tutorial we will perform handwriting recognition by training a
multilayer perceptron (MLP)
on the MNIST handwritten digit database.
The Task
MNIST is a dataset which consists of 70,000 handwritten digits. Each
digit... |
zoofIO/flexx-notebooks | flexx_tutorial_event.ipynb | bsd-3-clause | %gui asyncio
from flexx import event
"""
Explanation: Tutorial for flexx.event - properties and events
End of explanation
"""
class MyObject(event.Component):
@event.reaction('!foo')
def on_foo(self, *events):
print('received the foo event %i times' % len(events))
ob = MyObject()
for i in rang... |
dwhswenson/openpathsampling | examples/tests/test_snapshot.ipynb | mit | from __future__ import print_function
import numpy as np
import openpathsampling as paths
import openpathsampling.engines.features as features
"""
Explanation: Some testing and analysis of the new Snapshot implementation
End of explanation
"""
from IPython.display import Markdown
def code_to_md(snapshot_class):
... |
gwu-libraries/notebooks | 20181127-top-hashtags-json.ipynb | mit | !cat 50tweets.json | jq -cr '[.entities.hashtags][0][].text'
!cat tweets4hashtags.json | jq -cr '[.entities.hashtags][0][].text' > allhashtags.txt
"""
Explanation: Computing the top hashtags (JSON)
So you have tweets in a JSON file, and you'd like to get a list of the hashtags, from the most frequently occurring hash... |
MTG/essentia | src/examples/python/musicbricks-tutorials/1-stft_analsynth.ipynb | agpl-3.0 | # import essentia in streaming mode
import essentia
import essentia.streaming as es
"""
Explanation: STFT Analysis/Synthesis - MusicBricks Tutorial
Introduction
This tutorial will guide you through some tools for performing spectral analysis and synthesis using the Essentia library (http://www.essentia.upf.edu). STFT ... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection... |
jvcarr/portfolio | projects/West-Nile-Final.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import cross_val_score, StratifiedKFold , train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc
from sklearn.ensemble import Ra... |
Intel-Corporation/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_speech_recognition.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... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex26-Identify Marine Heatwaves from High-resolution Daily SST Data.ipynb | mit | %matplotlib inline
import numpy as np
from datetime import date
from matplotlib import pyplot as plt
# Load marineHeatWaves definition module
import marineHeatWaves as mhw
"""
Explanation: Identify Marine Heatwaves from High-resolution Daily SST Data
Marine ecosystems are strongly influenced by heatwaves, a kind of ... |
bashtage/statsmodels | examples/notebooks/statespace_fixed_params.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
"""
Explanation: Estimating or specifying parameters in state space models
In this notebook we show how to fix specific val... |
hesam-setareh/nest-simulator | pynest/examples/gif_pop_psc_exp.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import nest
"""
Explanation: Population rate model of generalized integrate-and-fire neurons
This script simulates a finite network of generalized integrate-and-fire (GIF) neurons directly on the mesoscopic population level using ... |
QuantScientist/Deep-Learning-Boot-Camp | day03/2.3 Deep Convolutional Neural Networks.ipynb | mit | from keras.applications import VGG16
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
import os
# -- Jupyter/IPython way to see documentation
# please focus on parameters (e.g. include top)
VGG16??
vgg16 = VGG16(include_top=True, weights='imagenet')
"""
Explanation: Deep CNN Models
... |
sdss/marvin | docs/sphinx/jupyter/Shanghai_Demo_Tools.ipynb | bsd-3-clause | from __future__ import print_function, division, absolute_import
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Marvin Workshop (Shanghai 2016)
This Jupyter notebook will guide you through the installation of Marvin and will give you a hint of its capabilities. But enough talk, let's begin by inst... |
kit-cel/lecture-examples | mloc/ch4_Deep_Learning/pytorch/pytorch_tutorial_2.ipynb | gpl-2.0 | import torch
import numpy as np
%matplotlib inline
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from IPython import display
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
"""
Explanation: PyTorch Tutorial - Part 2
T... |
Kaggle/learntools | notebooks/intro_to_programming/raw/ex5.ipynb | apache-2.0 | from learntools.core import binder
binder.bind(globals())
from learntools.intro_to_programming.ex5 import *
print('Setup complete.')
"""
Explanation: In the tutorial, you learned how to define and modify Python lists. In this exercise, you will use your new knowledge to solve several problems.
Set up the notebook
Run... |
dereneaton/ipyrad | tests/cookbook-bucky.ipynb | gpl-3.0 | ## conda install -c BioBuilds mrbayes
## conda install -c ipyrad ipyrad
## conda install -c ipyrad bucky
## import Python libraries
import ipyrad.analysis as ipa
import ipyparallel as ipp
"""
Explanation: Cookbook for running BUCKy in parallel in a Jupyter notebook
This notebook uses the Pedicularis example data set ... |
ababino/circles_metacog | circles_metacog_analysis.ipynb | mit | %matplotlib inline
from __future__ import unicode_literals
import pandas as pd
import numpy as np
from glob import glob
from matplotlib import pyplot as plt
import seaborn as sns
from metacog_utils import add_sdt_utils, metacog_dfs, jointplot_group
from IPython.display import display
"""
Explanation: Circles Metacog A... |
matthias-k/pysaliency-examples | notebooks/Demo_Saliency_Maps.ipynb | mit | import pysaliency
import pysaliency.external_datasets
data_location = 'cache/datasets'
mit_stimuli, mit_fixations = pysaliency.external_datasets.get_mit1003(location=data_location)
index = 0
plt.imshow(mit_stimuli.stimuli[index])
f = mit_fixations[mit_fixations.n == index]
plt.scatter(f.x, f.y, color='r')
_ = plt.ax... |
ethanrowe/flowz | userguide/02. Intro to Artifacts.ipynb | mit | # An ExtantArtifact that will be used here and elsewhere in the guide
class GuideExtantArtifact(ExtantArtifact):
def __init__(self, num):
super(GuideExtantArtifact, self).__init__(self.get_me, name='GuideExtantArtifact')
self.num = num
@gen.coroutine
def get_me(self):
# O, pardon! s... |
arne-cl/alt-mulig | python/rstdt-lisp-import-test.ipynb | gpl-3.0 | import os
import sys
import glob
import nltk
RSTDT_MAIN_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0')
RSTDT_DOUBLE_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank/data/RSTtrees-WSJ-double-1.0')
RSTDT_TOKENIZED_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank... |
antonpetkoff/learning | information-retreival/2018_10_08_inverted_index.ipynb | gpl-3.0 | sample_bbc_news_sentences = [
"China confirms Interpol chief detained",
"Turkish officials believe the Washington Post writer was killed in the Saudi consulate in Istanbul.",
"US wedding limousine crash kills 20",
"Bulgarian journalist killed in park",
"Kanye West deletes social media profiles",
... |
rajul/tvb-library | tvb/simulator/demos/region_deterministic_smooth_parameter_variation.ipynb | gpl-2.0 | from tvb.simulator.lab import *
"""
Explanation: Demonstrate using the simulator at the region level, deterministic integration, how to smoothly change a model parameter at run time.
Run Time ~ 3 seconds
End of explanation
"""
#rs.configure()
LOG.info("Configuring...")
#Initialise a Model, Coupling, and Connectivit... |
Pittsburgh-NEH-Institute/Institute-Materials-2017 | schedule/week_2/Near_matching.ipynb | gpl-3.0 | from collatex import *
collation = Collation()
collation.add_plain_witness("A", "The gray koala")
collation.add_plain_witness("B", "The big grey koala")
alignment_table = collate(collation, segmentation=False)
print(alignment_table)
from collatex import *
collation = Collation()
collation.add_plain_witness("A", "The g... |
sharefm/DSF | project.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
%matplotlib inline
"""
Explanation: Project for Data Science Fundamentals course
Loay Abdulatif & Sharef Mustafa
The Question that we are investigating is that:
Who are the attackers of a websit... |
ethen8181/machine-learning | data_science_is_software/notebooks/data_science_is_software.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)
"""
Explanation: <h1>T... |
matthewzimmer/traffic-sign-classification | plotting/matplotlib/plotting.ipynb | mit | x = linspace(0, 5, 10)
y = x ** 2
figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('title')
plot()
"""
Explanation: plot example
End of explanation
"""
from __future__ import division
from IPython.display import display
from sympy.interactive import printing
printing.init_printing(use_latex='mathjax')
impor... |
tommyogden/maxwellbloch | docs/examples/mbs-lambda-weak-pulse-more-atoms-with-coupling.ipynb | mit | mb_solve_json = """
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"detuning": 0.0,
"detuning_positive": true,
"label": "probe",
"rabi_freq": 1.0e-3,
"rabi_freq_t_args":
{
"ampl": 1.0,
"centre": 0.0,
"fw... |
mathcoding/programming | notebooks_v3/Lab1_Introduzione.v3.ipynb | mit | 345
"""
Explanation: Elementi di Programmazione
Un linguaggio di programmazione serve sia per istruire una macchina ad eseguire dei conti, che per organizzare le nostre idee su come quei conti devono essere eseguiti. Per questo, nella scelta di un linguaggio di programmazione, dobbiamo tener presente quali sono gli st... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/List Comprehensions.ipynb | apache-2.0 | # Grab every letter in string
lst = [x for x in 'word']
# Check
lst
"""
Explanation: Comprehensions
In addition to sequence operations and list methods, Python includes a more advanced operation called a list comprehension.
List comprehensions allow us to build out lists using a different notation. You can think of i... |
garibaldu/boundary-seekers | boundary-seeker.ipynb | mit | def sigmoid(phi):
return 1.0/(1.0 + np.exp(-phi))
def calc_prob_class1(params):
# Sigmoid perceptron ('logistic regression')
tildex = X - params['mean']
W = params['wgts']
phi = np.dot(tildex, W)
return sigmoid(phi) # Sigmoid perceptron ('logistic regression')
def calc_membership(params):
... |
AdityaSoni19031997/Machine-Learning | Coursera_DL/Python+Basics+With+Numpy+v3.ipynb | mit | ### 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... |
delsner/dl-exploration | notebooks/04 - Backpropagation .ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Backpropagation
This is meant to deepen the understanding of backpropagation and (stochastic) gradient descent in NN.
Softmax Linear Classifier
Initially a linear classifier, then move to 2-layer NN.
End of explanation
"""
# Generate a spiral d... |
spatialaudio/sweep | software_sweep.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Simulation of Impulse Response Measurements
The software (https://github.com/franzpl/sweep) has been written in the context of my bachelor thesis with the topic "On the influence of windowing of sweep signals for room impulse measur... |
danielbultrini/FXFEL | Particle Distribution Visualization.ipynb | bsd-3-clause | import processing_tools as pt
"""
Explanation: First, import the processing tools that contain classes and methods to read, plot and process standard unit particle distribution files.
End of explanation
"""
filepath = './example/example.h5'
data = pt.ParticleDistribution(filepath)
data.su2si
data.dict['x']
"""
Ex... |
danielfrg/danielfrg.github.io-source | content/blog/notebooks/2016/02/ssn-names.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import os
data_dir = os.path.expanduser("~/data/names/names")
files = os.listdir(data_dir)
data = pd.DataFrame(columns=["year", "name", "sex", "occurrences"])
for fname in files:
if fname.endswith(".txt"):
fpath = os.path.join(data_dir, fname)
df = pd.rea... |
coursemdetw/reveal2 | content/notebook/Elements of Evolutionary Algorithms.ipynb | mit | import random
from deap import algorithms, base, creator, tools
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
def evalOneMax(individual):
return (sum(individual),)
"""
Explanation: <img src='http://www.puc-rio.br/sobrepuc/admin/vrd/brasa... |
jupyter/nbgrader | nbgrader/docs/source/user_guide/autograded/hacker/ps1/problem1.ipynb | bsd-3-clause | NAME = "Alyssa P. Hacker"
COLLABORATORS = "Ben Bitdiddle"
"""
Explanation: Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel$\rightarrow$Restart) and then run all cells (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill i... |
Merinorus/adaisawesome | Homework/05 - Taming Text/HW05_awesometeam_Q2.ipynb | gpl-3.0 | import pandas as pd
import pycountry
from nltk.sentiment import *
import numpy as np
import matplotlib.pyplot as plt
import codecs
import math
import re
import string
"""
Explanation: Question 2) Find all the mentions of world countries in the whole corpus,
using the pycountry utility (HINT: remember that there wil... |
iagapov/ocelot | demos/ipython_tutorials/4_wake.ipynb | gpl-3.0 | # the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
import time
# import from Ocelot main modules and functions
f... |
ireapps/cfj-2017 | completed/00. Python Fundamentals (Part 1).ipynb | mit | # variable assignment
# https://www.digitalocean.com/community/tutorials/how-to-use-variables-in-python-3
# strings -- enclose in single or double quotes, just make sure they match
my_name = 'Cody'
# numbers
int_num = 6
float_num = 6.4
# the print function
print(8)
print('Hello!')
print(my_name)
print(int_num)
print... |
ageron/ml-notebooks | 06_decision_trees.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot ... |
phoebe-project/phoebe2-docs | development/tutorials/l3.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: "Third" Light
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
import m... |
Esri/gis-stat-analysis-py-tutor | notebooks/NeighborhoodSearching.ipynb | apache-2.0 | import Weights as WEIGHTS
import os as OS
inputFC = r'../data/CA_Polygons.shp'
fullFC = OS.path.abspath(inputFC)
fullPath, fcName = OS.path.split(fullFC)
masterField = "MYID"
"""
Explanation: Neighborhood Structures in the ArcGIS Spatial Statistics Library
Spatial Weights Matrix
On-the-fly Neighborhood Iterators [GA ... |
tpin3694/tpin3694.github.io | machine-learning/calculate_the_trace_of_a_matrix.ipynb | mit | # Load library
import numpy as np
"""
Explanation: Title: Calculate The Trace Of A Matrix
Slug: calculate_the_trace_of_a_matrix
Summary: How to calculate the trace of a matrix in Python.
Date: 2017-09-02 12:00
Category: Machine Learning
Tags: Vectors Matrices Arrays
Authors: Chris Albon
Preliminaries
End of ex... |
fdmazzone/Ecuaciones_Diferenciales | Teoria_Basica/scripts/Segundo Parcial 2015.ipynb | gpl-2.0 | from sympy import *
init_printing()
x,y=symbols('x,y')
u=y*x**2-x**2/y**2
(x*u.diff(x)+y*u.diff(y)).simplify()
u.subs(y,1)
"""
Explanation: Ejercicio 1 Resolver el siguiente problema de valores iniciales para una ecuación en derivadas parciales
$$x\frac{\partial u}{\partial x}+y\frac{\partial u}{\partial y}=3x^2y$$
$... |
gfeiden/Notebook | Projects/senap/common_blocks.ipynb | mit | import fileinput as fi
"""
Explanation: MARCS Common Blocks
Identifying Fortran common blocks used throughout the MARCS model atmosphere package. The goal is to have a list of common blocks with an index of each routine they appear in.
End of explanation
"""
!head -n 5 marcs_common_blocks.txt
"""
Explanation: I hav... |
Krastanov/cutiepy | examples/Schroedinger_Equation_Solver_Examples-with_code.ipynb | bsd-3-clause | from cutiepy import *
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
cutiepy.codegen.DEBUG = True
"""
Explanation: Table of Contents
Rabi Oscillations
Simulating the Full Hamiltonian
With Rotating Wave Approximation
Coherent State in a Harmonic Oscillator
Jaynes-Cummings Revival
Definite Phot... |
google/eng-edu | ml/pc/exercises/image_classification_part2.ipynb | apache-2.0 | # 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 the L... |
kfollette/AST337-Fall2017 | Labs/Lab6/Lab6.ipynb | mit | # The standard fare:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
# Recall our use of this module to work with FITS files in Lab 4:
from astropy.io import fits
# This lets us use various Unix (or Unix-like) commands within Python:
import os
# We will see what this does ... |
mamrehn/machine-learning-tutorials | ipynb/[tinydb] First steps.ipynb | cc0-1.0 | path = './testData.json'
from tinydb import TinyDB, where
db = TinyDB(path)
"""
Explanation: TinyDB
TinyDB is a small and lightweight NoSQL database framework based on simple JSON files.
Source
Official Website:
- getting started
- advanced usage
Code
Some examples to create a database and insert, delete and seach for... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/01-NLP-Python-Basics/01-Tokenization.ipynb | apache-2.0 | # Import spaCy and load the language library
import spacy
nlp = spacy.load('en_core_web_sm')
# Create a string that includes opening and closing quotation marks
mystring = '"We\'re moving to L.A.!"'
print(mystring)
# Create a Doc object and explore tokens
doc = nlp(mystring)
for token in doc:
print(token.text, e... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/20_transfer_learning/notebooks/transfer-learning/Transfer_Learning.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_... |
JrtPec/opengrid | notebooks/Demo/Demo_caching.ipynb | apache-2.0 | import pandas as pd
from opengrid.library import misc
from opengrid.library import houseprint
from opengrid.library import caching
import charts
hp = houseprint.Houseprint()
"""
Explanation: Demo caching
This notebook shows how caching of daily results is organised. First we show the low-level approach, then a high-le... |
Mahdisadjadi/phoenixcrime | map.ipynb | mit | import shapefile
import matplotlib.patches as patches
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
"""
Explanation: Inspired by this gist!
To get data from go to this website:
http://www.census.gov/cgi-bin/geo/shapefiles201... |
tensorflow/docs | site/en/guide/migrate/tensorboard.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... |
BDannowitz/polymath-progression-blog | jlab-ml-lunch-2/notebooks/02-Recommender-System-Surprise.ipynb | gpl-2.0 | import pandas as pd
from surprise import Dataset, Reader
from surprise.model_selection import cross_validate
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from jlab import load_test_data, get_test_detector_plane
"""
Explanation: 02 - Surprise Recommender System
Use a well-supported recommender packag... |
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