repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
infilect/ml-course1 | keras-notebooks/Transfer-Learning/5.3.1 Keras and TF Integration.ipynb | mit | import tensorflow as tf
tf.__version__
from tensorflow.contrib import keras
"""
Explanation: Tight Integration
End of explanation
"""
from keras.datasets import cifar100
(X_train, Y_train), (X_test, Y_test) = cifar100.load_data(label_mode='fine')
from keras import backend as K
img_rows, img_cols = 32, 32
if K.... |
azhurb/deep-learning | tensorboard/Anna_KaRNNa_Hyperparameters.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner_1000_states-checkpoint.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
rueedlinger/machine-learning-snippets | notebooks/automl/classification_with_automl.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn import datasets, metrics, model_selection, preprocessing, pipeline
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import autosklearn.classification
wine = data... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
tensorflow/docs-l10n | site/ja/lite/performance/post_training_quant.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... |
basp/notes | squares_and_roots.ipynb | mit | def plot_rect(ax, p, fmt='b'):
x, y = p
ax.plot([0, x], [y, y], fmt) # horizontal line
ax.plot([x, x], [0, y], fmt) # vertical line
with plt.xkcd():
fig, axes = plt.subplots(1, figsize=(4, 4))
pu.setup_axes(axes, xlim=(-1, 4), ylim=(-1, 4))
for x in [1,2,3]: plot_rect(axes, (x, x))
"""
Exp... |
fantasycheng/udacity-deep-learning-project | tutorials/intro-to-rnns/Anna_KaRNNa_Exercises.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is bas... |
aylward/ITKTubeTK | examples/Demo-ConvertTubesToImage.ipynb | apache-2.0 | import os
import sys
import numpy
import itk
from itk import TubeTK as ttk
from itkwidgets import view
"""
Explanation: Convert Tubes To Images
This notebook contains a few examples of how to call wrapped methods in itk and ITKTubeTK.
ITK, ITKTubeTK, and ITKWidgets must be installed on your system for this notebook t... |
bzamecnik/ml | instrument-classification/analyze_instrument_ranges.ipynb | mit | plt.hist(x_rms_instruments_notes[x_rms_instruments_notes <= 1].flatten(), 200);
plt.hist(x_rms_instruments_notes[x_rms_instruments_notes > 1].flatten(), 200);
"""
Explanation: There's a peak at value around 1.0 which represents quiet.
End of explanation
"""
plt.imshow(x_rms_instruments_notes > 1, interpolation='non... |
gouthambs/karuth-source | content/extra/notebooks/numba_example.ipynb | artistic-2.0 | import numpy as np
import numba
import cython
%load_ext cython
import pandas as pd
numba.__version__, cython.__version__, np.__version__
"""
Explanation: Optimizing Python Code: Numba vs Cython
Goutham Balaraman
I came across an old post by jakevdp on Numba vs Cython. I thought I will revisit this topic because both... |
CalPolyPat/phys202-project | .ipynb_checkpoints/NeuralNetworks-checkpoint.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... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 1 Mathematics and Python/Lectures notebooks/10 numpy arrays and operations with them/matrix_operations-.ipynb | mit | import numpy as np
"""
Explanation: NumPy: матрицы и операции над ними
В этом ноутбуке из сторонних библиотек нам понадобится только NumPy. Для удобства импортируем ее под более коротким именем:
End of explanation
"""
a = np.array([[1, 2, 3], [2, 5, 6], [6, 7, 4]])
print "Матрица:\n", a
"""
Explanation: 1. Создани... |
sadahanu/Capstone | SCRAPE/review_gather.ipynb | mit | # create the category data frame
cat_id = [1,2,3,4,5]
category = ['Balls and Fetch Toys','Chew Toys','Plush Toys','Interactive Toys','Rope and Tug']
link = ['https://www.chewy.com/s?rh=c%3A288%2Cc%3A315%2Cc%3A317','https://www.chewy.com/s?rh=c%3A288%2Cc%3A315%2Cc%3A316',
'https://www.chewy.com/s?rh=c%3A288%2Cc%3... |
RJTK/dwglasso_cweeds | notebooks/clean_data.ipynb | mit | print('Original bounds: ', t[0], t[-1])
t_obs = t[D['T_flag'] != -1]
D = D[t_obs[0]:t_obs[-1]] # Truncate dataframe so it is sandwiched between observed values
t = D.index
T = D['T']
print('New bounds: ', t[0], t[-1])
t_obs = D.index[D['T_flag'] != -1]
t_interp = D.index[D['T_flag'] == -1]
T_obs = D.loc[t_obs, 'T']
... |
kit-cel/wt | sigNT/systems/frequency_response.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 10) )
"""
Explanation: Content and Objective
Show that fre... |
navaro1/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
dlsun/symbulate | docs/conditioning.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Documentation
Conditioning
<a id='contents'></a>
Conditional distributions
Conditioning with |
Conditioning events
Conditioning on multiple events
Conditioning on events in a probability space
Conditioning on the value of a continuous RV
Specifying... |
zlxs23/Python-Cookbook | data_structure_and_algorithm_py3_5.ipynb | apache-2.0 | rows = [
{'fname': 'Brian', 'lname': 'Jones', 'uid': 1003},
{'fname': 'David', 'lname': 'Beazley', 'uid': 1002},
{'fname': 'John', 'lname': 'Cleese', 'uid': 1001},
{'fname': 'Big', 'lname': 'Jones', 'uid': 1004}
]
# 根据任意dict field 来排序输入结果行
from operator import itemgetter
rows_by_fname = sorted(rows,key=... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-2/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, ... |
ud3sh/coursework | deeplearning.ai/coursera-improving-neural-networks/week1/assignment1/Initialization.ipynb | unlicense | import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec
%matplotlib inline
plt.rcParams['f... |
sarahmid/programming-bootcamp-v2 | lab6_exercises_ANSWERS.ipynb | mit | def fancy_calc(a, b, c):
x1 = basic_calc(a,b)
x2 = basic_calc(b,c)
x3 = basic_calc(c,a)
z = x1 * x2 * x3
return z
def basic_calc(x, y):
result = x + y
return result
x = 1
y = 2
z = 3
result = fancy_calc(x, y, z)
"""
Explanation: Programming Bootcamp 2016
Lesson 6 Exercises -- ANSWERS
Ea... |
p-chambers/occ_airconics | examples/notebooks/notebook_examples.ipynb | bsd-3-clause | from airconics import LiftingSurface, Engine, Fuselage
import airconics.AirCONICStools as act
from airconics.Addons.WebServer.TornadoWeb import TornadoWebRenderer
from IPython.display import display
"""
Explanation: Notebook for Airconics examples
This IPython notebook contains examples for generating and rendering th... |
Pybonacci/notebooks | Explorando el Planeta Nueve con Python usando poliastro.ipynb | bsd-2-clause | !conda install -qy poliastro --channel poliastro # Instala las dependencias con conda
!pip uninstall poliastro -y
#!pip install -e /home/juanlu/Development/Python/poliastro.org/poliastro
!pip install https://github.com/poliastro/poliastro/archive/planet9-fixes.zip # Instala la versión de desarrollo
%load_ext versi... |
davofis/computational_seismology | 05_pseudospectral/ps_derivative_solution.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
"""
Explanation: <div style='background-image: url("../../share/images/header.sv... |
dfm/emcee | docs/tutorials/parallel.ipynb | mit | %config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
import multiprocessing
multiprocessing.set_start_method("fork")
"""
Explanation: (parallel)=
Parallelization
End of explanation
"""
import os
os.en... |
mne-tools/mne-tools.github.io | stable/_downloads/548b4fc45f1ed79527138879cd79d3c8/muscle_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 matplotlib.pyplot as plt
import numpy as np
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_muscle_zs... |
jgarciab/wwd2017 | class4/class4b_inclass.ipynb | gpl-3.0 | ##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPython.core.display import dis... |
GoogleCloudPlatform/training-data-analyst | quests/tpu/flowers_resnet.ipynb | apache-2.0 | import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# do not change these
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION']... |
Danghor/Algorithms | Python/Chapter-04/Digit-Recognition.ipynb | gpl-2.0 | import gzip
import pickle
import numpy as np
"""
Explanation: Handwritten Digit Recognition using $k$-Nearest Neighbours
This notebook uses the <em style="color:blue;">$k$-nearest neighbours algorithm</em> to recognize handwritten digits. The digits we want to recognize
are stored as images of size $28 \times 28$ pix... |
tpin3694/tpin3694.github.io | python/pandas_list_comprehension.ipynb | mit | # Import modules
import pandas as pd
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
"""
Explanation: Title: Using List Comprehensions With Pandas
Slug: pandas_list_comprehension
Summary: Using List Comprehensions ... |
John-Keating/ThinkStats2 | code/chap04ex.ipynb | gpl-3.0 | %matplotlib inline
import nsfg
preg = nsfg.ReadFemPreg()
"""
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
Read the pregnancy file.
End of explanation
"""
import thinkstats2 as ts
live = preg[preg.outcome == 1]
wgt_cdf = ts.Cdf(live.totalwgt_lb, label = 'weight')
"""
Expl... |
bosscha/alma-calibrator | notebooks/2mass/10_PCA_combine_test_matchagain.ipynb | gpl-2.0 | obj = ["PKS J0006-0623", 1.55789, -6.39315, 1]
# name, ra, dec, radius of cone
obj_name = obj[0]
obj_ra = obj[1]
obj_dec = obj[2]
cone_radius = obj[3]
obj_coord = coordinates.SkyCoord(ra=obj_ra, dec=obj_dec, unit=(u.deg, u.deg), frame="icrs")
data_2mass = Irsa.query_region(obj_coord, catalog="fp_psc", radius=cone... |
mila-udem/summerschool2015 | fuel_tutorial/fuel_logreg.ipynb | bsd-3-clause | import numpy
import theano
from theano import tensor
# Size of the data
n_in = 28 * 28
# Number of classes
n_out = 10
x = tensor.matrix('x')
W = theano.shared(value=numpy.zeros((n_in, n_out), dtype=theano.config.floatX),
name='W',
borrow=True)
b = theano.shared(value=numpy.zeros((n... |
as595/AllOfYourBases | CDT-KickOff/LECTURE/GPMIntro.ipynb | gpl-3.0 | %matplotlib inline
"""
Explanation: ==============================================================================================
‹ GPMIntro.ipynb ›
Copyright (C) ‹ 2017 › ‹ Anna Scaife - anna.scaife@manchester.ac.uk ›
This program is free software: you can redistribute it ... |
csiu/100daysofcode | datamining/2017-03-04-day08.ipynb | mit | d = cmudict.dict()
def readability_ease(num_sentences, num_words, num_syllables):
asl = num_words / num_sentences
asw = num_syllables / num_words
return(206.835 - (1.015 * asl) - (84.6 * asw))
def readability_ease_interpretation(x):
if 90 <= x:
res = "5th grade] "
res += "Very eas... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to... |
indiependente/Social-Networks-Structure | results/Watts-Strogatz Results Analysis.ipynb | mit | #!/usr/bin/python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from stats import parse_results, get_percentage, get_avg_per_seed, draw_pie, draw_bars, draw_bars_comparison, draw_avgs
"""
Explanation: Watts-Strogatz Graph Experiments Output Visualization
End of explanation
"""
pr, eigen, bet ... |
ngcm/training-public | FEEG6016 Simulation and Modelling/08-Finite-Elements-Lab-2.ipynb | mit | from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
"""
Explanation: Finite Elements Lab 2 Worksheet
End of explanation
"""
%matplotlib inline
import numpy
from matplotlib import pyplot
from matplotl... |
jhillairet/scikit-rf | doc/source/examples/metrology/Measuring a Mutiport Device with a 2-Port Network Analyzer.ipynb | bsd-3-clause | import skrf as rf
from itertools import combinations
%matplotlib inline
from pylab import *
rf.stylely()
"""
Explanation: Measuring a Multiport Device with a 2-Port Network Analyzer
Introduction
In microwave measurements, one commonly needs to measure a n-port device with a m-port network analyzer ($m<n$ of course). ... |
metpy/MetPy | v0.9/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
# Change default to be better for skew-T
plt.rcParams['figure.figsize'] = (9, 9)
# Upper air data can be... |
nadvamir/deep-learning | 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... |
UWSEDS/LectureNotes | PreFall2018/Objects/Building Software With Objects.ipynb | bsd-2-clause | from IPython.display import Image
Image(filename='Classes_vs_Objects.png')
"""
Explanation: Why Objects?
Provide modularity and reuse through hierarchical structures
Object oriented programming is a different way of thinking.
Programming With Objects
End of explanation
"""
# Definiting a Car class
class Car(objec... |
dnc1994/MachineLearning-UW | ml-clustering-and-retrieval/6_hierarchical_clustering.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
import time
from scipy.sparse import csr_matrix
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances
%matplotlib inline
"""
Explanation: Hierarchical Clustering
Hierarchical clustering refers to a class... |
5agado/data-science-learning | statistics/Statistics - Basic Theorems.ipynb | apache-2.0 | %matplotlib notebook
import numpy as np
import seaborn as sns
sns.set_context("paper")
"""
Explanation: Table of Contents
Law Of Large Numbers
Central Limit Theorem
Experiment: Sum Of N Dice
End of explanation
"""
# Define info for die population
die = np.arange(6)+1
die_dist = np.array([1/len(values)]*len(values... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l08c09_forecasting_with_cnn.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... |
GoogleCloudPlatform/training-data-analyst | CPB100/lab4a/demandforecast.ipynb | apache-2.0 | import google.datalab.bigquery as bq
import pandas as pd
import numpy as np
import shutil
%bq tables describe --name bigquery-public-data.new_york.tlc_yellow_trips_2015
"""
Explanation: Demand forecasting with BigQuery and TensorFlow
In this notebook, we will develop a machine learning model to predict the demand for... |
mne-tools/mne-tools.github.io | 0.24/_downloads/72bb0e260a352fd7c21fee1dd2f83d79/decoding_spoc_CMC.ipynb | bsd-3-clause | # Author: Alexandre Barachant <alexandre.barachant@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.decoding import SPoC
from mne.datasets.fieldtrip_cmc import data_path
from sklearn.pipeline import make... |
GoogleCloudPlatform/training-data-analyst | learning_rate.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Install Sklearn
!python3 -m pip install --user sklearn
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1 || pip install tensorflow==2.1
"""
Explanation: Observing Learning Curve Changes
Learning Objectives
Le... |
PythonFreeCourse/Notebooks | week01/7_Logic_Operators.ipynb | mit | print("True and True => " + str(True and True))
print("False and True => " + str(False and True))
print("True and False => " + str(True and False))
print("False and False => " + str(False and False))
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של... |
Jackporter415/phys202-2015-work | assignments/assignment08/InterpolationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp2d
from scipy.interpolate import interp1d
"""
Explanation: Interpolation Exercise 1
End of explanation
"""
with np.load('trajectory.npz') as data:
t = data['t']
x = data['x']
y... |
ethen8181/machine-learning | reinforcement_learning/multi_armed_bandits.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
# 2. ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.