text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
import matplotlib
matplotlib.use('Agg')
%matplotlib qt
import matplotlib.pyplot as plt
import numpy as np
import os
import SimpleITK as sitk
from os.path import expanduser, join
from scipy.spatial.distance import euclidean
os.chdir(join(expanduser('~'), 'Medical Imaging'))
import liversegmentation
```
---
# Read ... | github_jupyter |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, CuDNNLSTM, CuDNNGRU, BatchNormalization, LocallyConnected2D, ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.... | github_jupyter |
## Common plotting pitfalls that get worse with large data
When working with large datasets, visualizations are often the only way available to understand the properties of that dataset -- there are simply too many data points to examine each one! Thus it is very important to be aware of some common plotting problems... | github_jupyter |
```
# GPU: 32*40 in 9.87s = 130/s
# CPU: 32*8 in 31.9s = 8/s
import os
import sys
import numpy as np
import mxnet as mx
from collections import namedtuple
print("OS: ", sys.platform)
print("Python: ", sys.version)
print("Numpy: ", np.__version__)
print("MXNet: ", mx.__version__)
!cat /proc/cpuinfo | grep processor | wc... | github_jupyter |
# Breast Cancer Diagnosis
In this notebook we will apply the LogitBoost algorithm to a toy dataset to classify cases of breast cancer as benign or malignant.
## Imports
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='darkgrid', palette='colorblind', color_codes=True)
from... | github_jupyter |
Title: Are the Warriors better without Kevin Durant?
Date: 2019-06-10 12:00
Tags: python
Slug: ab_kd
In the media, there have been debates about whether or not the Golden State Warriors (GSW) are better without Kevin Durant (KD). From the eye-test, it's laughable to even suggest this, as he's one of the top 3 players... | github_jupyter |
```
import requests
import arrow
import pprint
import json
from urllib.parse import urlencode
from functools import reduce
token = open("./NOTION_TOKEN", "r").readlines()[0]
notion_version = "2021-08-16"
extra_data = {"filter": {"and": [{"property": "标签",
"multi_select": {"is_not_empt... | github_jupyter |
<div class = 'alert alert-block alert-info'
style = 'background-color:#4c1c84;
color:#eeebf1;
border-width:5px;
... | github_jupyter |
# Регрессия - последняя подготовка перед боем!
> 🚀 В этой практике нам понадобятся: `numpy==1.21.2, pandas==1.3.3, matplotlib==3.4.3, scikit-learn==0.24.2, seaborn==0.11.2`
> 🚀 Установить вы их можете с помощью команды: `!pip install numpy==1.21.2, pandas==1.3.3, matplotlib==3.4.3, scikit-learn==0.24.2, seaborn==0... | github_jupyter |
# Hypothesis Testing
From lecture, we know that hypothesis testing is a critical tool in determing what the value of a parameter could be.
We know that the basis of our testing has two attributes:
**Null Hypothesis: $H_0$**
**Alternative Hypothesis: $H_a$**
The tests we have discussed in lecture are:
* One Popula... | github_jupyter |
```
from systemtools.hayj import *
from systemtools.basics import *
from systemtools.file import *
from systemtools.printer import *
from systemtools.logger import *
from annotator.annot import *
from datatools.jsonutils import *
from nlptools.tokenizer import *
from datatools.htmltools import *
from newssource.goodart... | github_jupyter |
```
%pylab inline
import numpy as np
import matplotlib.pyplot as plt
# PyTorch imports
import torch
# This has neural network layer primitives that you can use to build things quickly
import torch.nn as nn
# This has things like activation functions and other useful nonlinearities
from torch.nn import functional as ... | github_jupyter |
# Advanced Tutorial: Creating Gold Annotation Labels with BRAT
This is a short tutorial on how to use BRAT (Brat Rapid Annotation Tool), an
online environment for collaborative text annotation.
http://brat.nlplab.org/
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
# TO USE A DATABASE OTHER THA... | github_jupyter |
... ***CURRENTLY UNDER DEVELOPMENT*** ...
## Validation of the total water level
inputs required:
* historical wave conditions
* emulator output - synthetic wave conditions of TWL
* emulator output - synthetic wave conditions of TWL with 3 scenarios of SLR
in this notebook:
* Comparison of the extreme di... | github_jupyter |
<small><small><i>
All the IPython Notebooks in this lecture series by Dr. Milan Parmar are available @ **[GitHub](https://github.com/milaan9/02_Python_Datatypes)**
</i></small></small>
# Python Strings
In this class you will learn to create, format, modify and delete strings in Python. Also, you will be introduced to... | github_jupyter |
```
import pandas as pd
import numpy as np
visit = pd.read_csv("visitorCount.csv",dtype=str)
a = visit.melt( id_vars=['time'])
# a.to_csv("visitorMelt.csv")
movement = pd.read_csv("movements.csv")
movement = movement.astype('category')
len(movement)
stations = pd.read_csv("stations.csv")
stations['double_count'] = Fals... | github_jupyter |
# Time series in Pastas
*R.A. Collenteur, University of Graz, 2020*
Time series are at the heart of time series analysis, and therefore need to be considered carefully when dealing with time series models. In this notebook more background information is provided on important characteristics of time series and how thes... | github_jupyter |
```
#import the necessary modules
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy
import sklearn
import itertools
from itertools import cycle
import os.path as op
import timeit
import json
import math
import multiprocessing as m_proc
m_proc.cpu_count()
# Im... | github_jupyter |
# Data Cleaning And Feature Engineering
* Data is very dirty so we have to clean our data for analysis.
* Also have many missing values represented by -1(have to fix it is very important).
```
import pandas as pd
data=pd.read_csv('original_data.csv')
data.head()
data.shape
#droping duplicates
data=data.drop_duplicate... | github_jupyter |
# Chapter 3: Deep Learning Libraries
This chapter discusses the important libraries and frameworks that one needs to get started in artificial intelligence. We'll cover the basic functions of the three most popular deep learning frameworks: Tensorflow, Pytorch, and Keras, and show you how to get up and running in each... | github_jupyter |
<table>
<tr>
<td><img src='SystemLink_icon.png' /></td>
<td ><h1><strong>NI SystemLink Python API</strong></h1></td>
</tr>
</table>
## Test Monitor Service Example
***
The Test Monitor Service API provides functions to create, update, delete and query Test results and Test steps.
***
# Prerequi... | github_jupyter |
<a href="https://colab.research.google.com/github/satyajitghana/PadhAI-Course/blob/master/13_OverfittingAndRegularization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors... | github_jupyter |
# Marginal Gaussianization
* Author: J. Emmanuel Johnson
* Email: jemanjohnson34@gmail.com
In this demonstration, we will show how we can do the marginal Gaussianization on a 2D dataset using the Histogram transformation and Inverse CDF Gaussian distribution.
```
import os, sys
cwd = os.getcwd()
# sys.path.insert(0,... | github_jupyter |
```
!pip install -q --upgrade jax jaxlib
from __future__ import print_function, division
import jax.numpy as np
from jax import grad, jit, vmap
from jax import random
key = random.PRNGKey(0)
```
# The Autodiff Cookbook
*alexbw@, mattjj@*
JAX has a pretty general automatic differentiation system. In this notebook,... | github_jupyter |
# Multivariate Resemblance Analysis (MRA) Dataset A
In this notebook the multivariate resemblance analysis of Dataset A is performed for all STDG approaches.
```
#import libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os
pri... | github_jupyter |
```
from qiskit.tools.jupyter import *
from qiskit import IBMQ
IBMQ.load_account()
#provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')
provider=IBMQ.get_provider(hub='ibm-q-research', group='uni-maryland-1', project='main')
backend = provider.get_backend('ibmq_armonk')
backend_config = backend.con... | github_jupyter |
# eICU Experiments
```
import tensorflow as tf
import numpy as np
import h5py
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import tensorflow_probability as tfp
import sklearn
from sklearn import metrics
import seaborn as sns
import random
```
Follow Read-me instruction to downl... | github_jupyter |
# TensorFlow Tutorial
Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Ke... | github_jupyter |
# 1D Variability hypothesis testing for HBEC IFN experiment
```
import scanpy as sc
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
from pybedtools import BedTool
import pickle as pkl
%matplotlib inline
import sys
sys.path.append('/home/ssm-user/... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
```
# Utilizando un modelo pre-entrenado
[`torchvision.models`](https://pytorch.org/vision/stable/models.html) ofrece una serie de modelos famosos de la literatura de *deep learning*
Por defecto el modelo se carga con pesos aleatorios
Si in... | github_jupyter |
# Tutorial about loading localization data from file
```
from pathlib import Path
import locan as lc
lc.show_versions(system=False, dependencies=False, verbose=False)
```
Localization data is typically provided as text or binary file with different formats depending on the fitting software. Locan provides functions ... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | github_jupyter |
```
import azureml
from azureml.core import Workspace, Experiment, Datastore, Environment
from azureml.core.runconfig import RunConfiguration
from azureml.data.datapath import DataPath, DataPathComputeBinding
from azureml.data.data_reference import DataReference
from azureml.core.compute import ComputeTarget, AmlComput... | github_jupyter |
# 6.7 门控循环单元（GRU）
## 6.7.2 读取数据集
```
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
(corpus_indices, char_to_idx, idx_to_char, vocab_size) =... | github_jupyter |
# The JupyterLab Interface
The JupyterLab interface consists of a main work area containing tabs of documents and activities, a collapsible left sidebar, and a menu bar. The left sidebar contains a file browser, the list of running terminals and kernels, the table of contents, and the extension manager.
![jupyter_lab... | github_jupyter |
# Check Cell Population Heterogeneity
## Libraries
```
import MySQLdb
import pandas
import numpy as np
from matplotlib import pylab as plt
import os
import seaborn as sns
from scipy.stats import mannwhitneyu as mw
from scipy import stats
import operator
from sklearn.preprocessing import StandardScaler,RobustScaler
fr... | github_jupyter |
# Large Scale Kernel Ridge Regression
```
import sys
sys.path.insert(0, '/Users/eman/Documents/code_projects/kernellib')
sys.path.insert(0, '/home/emmanuel/code/kernellib')
import numpy as np
from kernellib.large_scale import RKSKernelRidge, KernelRidge as RKernelRidge
from kernellib.utils import estimate_sigma, r_ass... | github_jupyter |
<a href="https://www.skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDL0120ENSkillsNetwork20629446-2021-01-01"><img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/... | github_jupyter |
# Burgers Optimization with a Differentiable Physics Gradient
To illustrate the process of computing gradients in a _differentiable physics_ (DP) setting, we target the same inverse problem (the reconstruction task) used for the PINN example in {doc}`physicalloss-code`. The choice of DP as a method has some immediate... | github_jupyter |
```
# Automatically reload imported modules that are changed outside this notebook
%load_ext autoreload
%autoreload 2
# More pixels in figures
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams["figure.dpi"] = 200
# Init PRNG with fixed seed for reproducibility
import numpy as np
np_rng = np.random.defau... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
# Mandala: self-managing experiments
## What is Mandala?
Mandala enables new, simpler patterns for working with complex and evolving
computational experiments.
It eliminates low-level code and decisions for how to save, load, query,
delete and otherwise organize results. To achieve this, it lets computational
code "m... | github_jupyter |
# notebook for processing fully reduced m3 data "triplets"
This is a notebook for processing L0 / L1B / L2 triplets (i.e.,
the observations that got reduced).
## general notes
We process the reduced data in triplets simply to improve the metadata on the
L0 and L2 products. We convert L1B first to extract several attr... | github_jupyter |
# Speed benchmarks
This is just for having a quick reference of how the speed of running the program scales
```
from __future__ import print_function
import pprint
import subprocess
import sys
sys.path.append('../')
# sys.path.append('/home/heberto/learning/attractor_sequences/benchmarking/')
import numpy as np
impo... | github_jupyter |
<!--- <div style="text-align: center;">
<font size="5">
<b>Data-driven Design and Analyses of Structures and Materials (3dasm)</b>
</font>
</div>
<br>
</br>
<div style="text-align: center;">
<font size="5">
<b>Lecture 1</b>
</font>
</div>
<center>
<img src=docs/tudelft_logo.jpg width=550px>
</center>
... | github_jupyter |
```
import pandas as pd
train = pd.read_csv("./datasets/labeledTrainData.tsv", header=0, delimiter='\t', quoting=3)
train.head()
train.shape
train.columns.values
train["review"][0]
from bs4 import BeautifulSoup
example1 = BeautifulSoup(train["review"][0])
example1.get_text()
import re
letters_only = re.sub("[^a-zA-Z]",... | github_jupyter |
<a href="https://colab.research.google.com/github/stephenbeckr/numerical-analysis-class/blob/master/Demos/Ch4_integration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Numerical Integration (quadrature)
- See also Prof. Brown's [integration not... | github_jupyter |
```
import gtsam
import numpy as np
from gtsam.gtsam import (Cal3_S2, DoglegOptimizer,
GenericProjectionFactorCal3_S2, NonlinearFactorGraph,
Point3, Pose3, Point2, PriorFactorPoint3, PriorFactorPose3,
Rot3, SimpleCamera, Values)
from utils impo... | github_jupyter |
```
# default_exp resimulation
```
# Match resimulation
> Simulating match outcomes based on the xG of individual shots
```
#hide
from nbdev.showdoc import *
#export
import collections
import itertools
import numpy as np
```
Use Poisson-Binomial distribution calculation from https://github.com/tsakim/poibin
It lo... | github_jupyter |
# Лабораторная работа 9. ООП.
```
import numpy as np
import matplotlib.pyplot as plt
```
# 1. Создание классов и объектов
В языке программирования Python классы создаются с помощью инструкции `class`, за которой следует произвольное имя класса, после которого ставится двоеточие; далее с новой строки и с отступом реал... | github_jupyter |
# Qcodes example with InstrumentGroup driver
This notebooks explains how to use the `InstrumentGroup` driver.
## About
The goal of the `InstrumentGroup` driver is to combine several instruments as submodules into one instrument. Typically, this is meant to be used with the `DelegateInstrument` driver. An example usag... | github_jupyter |
```
from __future__ import print_function
import sisl
import numpy as np
import matplotlib.pyplot as plt
from functools import partial
%matplotlib inline
```
TBtrans is capable of calculating transport in $N\ge 1$ electrode systems. In this example we will explore a 4-terminal graphene GNR cross-bar (one zGNR, the oth... | github_jupyter |
```
#pip install xlwt openpyxl xlsxwriter xlrd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
```
# Loading in Calibration datasets
```
#CO2 only
df_Eguchi_CO2= pd.read_excel('Solubility_Datasets_V1.xlsx', sheet_name='Eguchi_CO2', index_col=0)
d... | github_jupyter |
# Обратные связи в контуре управления
Для рассмотренных в предыдущих лекциях регуляторов требуется оценивать состояние объекта управления. Для построения таких оценок необходимо реализовать обратные связи в контуре управления. На практике для этого используются специальные устройства: датчики.
# Случайные величины
... | github_jupyter |
```
import spotipy
from spotipy.oauth2 import SpotifyOAuth
import pandas as pd
import time
scope = 'user-top-read user-library-read'
sp = spotipy.Spotify(client_credentials_manager=SpotifyOAuth(scope=scope))
sp.user_playlists(sp.current_user()['id'])
results = sp.current_user_top_artists(time_range='short_term', limit=... | github_jupyter |
# Latitude, Longitude for any pixel in a GeoTiff File
How to generate the latitude and longitude for a pixel at any given position in a GeoTiff file.
```
from osgeo import ogr, osr, gdal
# opening the geotiff file
ds = gdal.Open('G:\BTP\Satellite\Data\Test2\LE07_L1GT_147040_20050506_20170116_01_T2\LE07_L1GT_147040_200... | github_jupyter |
```
!pip install unidecode googletrans
!pip install squarify
import re
import time
import tweepy
import folium
import squarify
import warnings
import collections
import numpy as np
import pandas as pd
from PIL import Image
from folium import plugins
from datetime import datetime
from textblob import TextBlob
import ma... | github_jupyter |
# Document Classification & Clustering - Lecture
What could we do with the document-term-matrices (dtm[s]) created in the previous notebook? We could visualize them or train an algorithm to do some specific task. We have covered both classification and clustering before, so we won't focus on the particulars of algorit... | github_jupyter |
```
a = 'ok'
b = 'test'
print(a+b)
print(a*2)
name = 'Bob'
print(f'Hello, {name}')
greeting = 'Hello, {}'
with_name = greeting.format(name)
print(with_name)
size = input('Enter the size of your house: ')
integer = int(size)
floating = float(size)
print(integer, floating)
square_meters = integer / 10.8
print(f'{integer}... | github_jupyter |
# Kestrel+Model
### A [Bangkit 2021](https://grow.google/intl/id_id/bangkit/) Capstone Project
Kestrel is a TensorFlow powered American Sign Language translator Android app that will make it easier for anyone to seamlessly communicate with people who have vision or hearing impairments. The Kestrel model builds on the ... | github_jupyter |
# PyTorch: Tabular Classify Binary
```
import torch
import torch.nn as nn
from torch import optim
import torchmetrics
from sklearn.preprocessing import LabelBinarizer, StandardScaler
import aiqc
from aiqc import datum
```
---
## Example Data
Reference [Example Datasets](example_data... | github_jupyter |
<div class="contentcontainer med left" style="margin-left: -50px;">
<dl class="dl-horizontal">
<dt>Title</dt> <dd> Scatter Element</dd>
<dt>Dependencies</dt> <dd>Matplotlib</dd>
<dt>Backends</dt>
<dd><a href='./Scatter.ipynb'>Matplotlib</a></dd>
<dd><a href='../bokeh/Scatter.ipynb'>Bokeh</a></dd>
<dd>... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
sys.path.append('../../pyutils')
import metrics
import utils
```
# Introduction
In unsupervised learing, one has a set of $N$ observations $x_i \in \mathbb{R}^p$, having joint density $P(X)$.
The goal is to infer properties of th... | github_jupyter |
# Wie Sie dieses Notebook nutzen:
- Führen Sie diesen Code Zelle für Zelle aus.
- Um die Variableninhalte zu beobachten, nutzen Sie in Jupyter-Classic den "Variable Inspektor". Falls Sie dieses Notebook in Jupyter-Lab verwenden, nutzen Sie hierfür den eingebauten Debugger.
- Wenn Sie "Code Tutor" zur Visualisierung des... | github_jupyter |
# Hinge Loss
In this project you will be implementing linear classifiers beginning with the Perceptron algorithm. You will begin by writing your loss function, a hinge-loss function. For this function you are given the parameters of your model θ and θ0
Additionally, you are given a feature matrix in which the rows ar... | github_jupyter |
(Feedforward)=
# Chapter 8 -- Feedforward
Let's take a look at how feedforward is processed in a three layers neural net.
<img src="images/feedForward.PNG" width="500">
Figure 8.1
From the figure 8.1 above, we know that the two input values for the first and the second neuron in the hidden layer are
$$
h_1^{(1)} = ... | github_jupyter |
In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)).
Let's execute the cell below to display information about the GPUs running on the server by running the pgaccelinfo command, which ships with t... | github_jupyter |
```
import matplotlib
import matplotlib.pyplot as plt
import os
import random
import io
import imageio
import glob
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
from IPython.display import display, Javascript
from IPython.display import Image as IPyImage
impo... | github_jupyter |
```
%matplotlib nbagg
import os
os.environ["PYOPENCL_COMPILER_OUTPUT"]="1"
import numpy
import fabio
import pyopencl
from pyopencl import array as cla
from matplotlib.pyplot import subplots
ctx = pyopencl.create_some_context(interactive=True)
queue = pyopencl.CommandQueue(ctx, properties=pyopencl.command_queue_properti... | github_jupyter |
```
# default_exp downloaders
#export
import requests
import pathspec
import time
from pathlib import Path, PurePosixPath
from tightai.lookup import Lookup
from tightai.conf import CLI_ENDPOINT
#hide
test = False
if test:
CLI_ENDPOINT = "http://cli.desalsa.io:8000"
#export
class DownloadVersion(Lookup):
path =... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/Spark%20v2.7.6%20Notebooks/21.Gender_Classi... | github_jupyter |
## Load Library And Data
```
# importing the library
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# to know the ecoding type
import chardet
with open('E:\\Recommendation System\\book.csv', 'rb') as rawdata:
result = chardet.detect(rawdata.read(100000))
result
```
- ... | github_jupyter |
## Eng+Wales well-mixed example model
This is the inference notebook with increased inference window. There are various model variants as encoded by `expt_params_local` and `model_local`, which are shared by the notebooks in a given directory.
Outputs of this notebook:
(same as `inf` notebook with added `tWin` labe... | github_jupyter |
```
#Use this command to run it on floydhub: floyd run --gpu --env tensorflow-1.4 --data emilwallner/datasets/imagetocode/2:data --data emilwallner/datasets/html_models/1:weights --mode jupyter
from os import listdir
from numpy import array
from keras.preprocessing.text import Tokenizer, one_hot
from keras.preprocessin... | github_jupyter |
# import required library
```
# Import numpy, pandas for data manipulation
import numpy as np
import pandas as pd
# Import matplotlib, seaborn for visualization
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# Import the data
weather_data = pd.read_csv('weather... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D2_LinearSystems/student/W2D2_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 3: Combining determinism and stochasticity
... | github_jupyter |
# String
## `print()`
Fungsi `print()` mencetak seluruh argumennya sebagai *string*, dipisahkan dengan spasi dan diikuti dengan sebuah *line break*:
```
name = "Budi"
print("Hello World")
print("Hello", 'World')
print("Hello", name)
```
> Catatan: Fungsi untuk mencetak di Python 2.7 dan Python 3 berbeda. Di Python 2... | github_jupyter |
<style>div.container { width: 100% }</style>
<img style="float:left; vertical-align:text-bottom;" height="65" width="172" src="../assets/holoviz-logo-unstacked.svg" />
<div style="float:right; vertical-align:text-bottom;"><h2>Tutorial 5. Interactive Pipelines</h2></div>
The plots built up over the first few tutorials... | github_jupyter |
# Cell Basic Filtering
## Content
The purpose of this step is to get rid of cells having **obvious** issues, including the cells with low mapping rate (potentially contaminated), low final reads (empty well or lost a large amount of DNA during library prep.), or abnormal methylation fractions (failed in bisulfite conv... | github_jupyter |
```
# i 可能的取值：0、2、4、6、len(A)
from collections import Counter
class Solution:
def canReorderDoubled(self, A):
if not A: return True
a_freq = Counter(A)
seen = set()
for a in A:
if a in seen: continue
if a_freq[a] == 0:
seen.add(a)
... | github_jupyter |
# Recommendations with IBM
In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform.
You may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page. Either way assure that your ... | github_jupyter |
# ELG Signal-to-Noise Calculations
This notebook provides a standardized calculation of the DESI emission-line galaxy (ELG) signal-to-noise (SNR) figure of merit, for tracking changes to simulation inputs and models. See the accompanying technical note [DESI-3977](https://desi.lbl.gov/DocDB/cgi-bin/private/ShowDocume... | github_jupyter |
# Face Generation
In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!
The project will be broken down into a series of tasks from **loading in data to defining and training adversarial net... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
# 빠른 학습을 위한 tfrecords 데이터셋 생성
- 컴페티션 기본 데이터는 data/public 하위 폴더에 있다고 가정합니다. (train.csv, sample_submission.csv, etc)
- 또한 train.zip, test.zip 역시 data/public 하위에 압축을 풀어놓았다고 가정하고 시작하겠습니다.
```
import os
import os.path as pth
import json
import shutil
import pandas as pd
from tqdm import tqdm
data_base_path = pth.join('dat... | github_jupyter |
## In situ data and trajectories incl. Bepi Colombo, PSP, Solar Orbiter
https://github.com/cmoestl/heliocats
Author: C. Moestl, IWF Graz, Austria
twitter @chrisoutofspace, https://github.com/cmoestl
last update: 2021 August 24
needs python 3.7 with the conda helio environment (see README.md)
uses heliopy for ge... | github_jupyter |
```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import torch
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_... | github_jupyter |
# 深度学习工具 PyTorch 简介
在此 notebook 中，你将了解 [PyTorch](http://pytorch.org/)，一款用于构建和训练神经网络的框架。PyTorch 在很多方面都和 Numpy 数组很像。毕竟，这些 Numpy 数组也是张量。PyTorch 会将这些张量当做输入并使我们能够轻松地将张量移到 GPU 中，以便在训练神经网络时加快处理速度。它还提供了一个自动计算梯度的模块（用于反向传播），以及另一个专门用于构建神经网络的模块。总之，与 TensorFlow 和其他框架相比，PyTorch 与 Python 和 Numpy/Scipy 堆栈更协调。
## 神经网络
深度学习以人工神经网络为... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from bokeh.plotting import *
from sklearn.cluster.bicluster import SpectralCoclustering
from bokeh.models import HoverTool, ColumnDataSource
from itertools import product
whisky = pd.read_csv('whiskies.txt')
whisky["Region"] = pd.read_csv('regio... | github_jupyter |
```
import datetime as dt
import panel as pn
pn.extension()
```
The ``DateRangeSlider`` widget allows selecting a date range using a slider with two handles.
For more information about listening to widget events and laying out widgets refer to the [widgets user guide](../../user_guide/Widgets.ipynb). Alternatively y... | github_jupyter |
# MBZ-XML-TO-EXCEL
First pubished version May 22, 2019. This is version 0.0004 (revision July 26, 2019)
Licensed under the NCSA Open source license
Copyright (c) 2019 Lawrence Angrave
All rights reserved.
Developed by: Lawrence Angrave
Permission is hereby granted, free of charge, to any person obtaining a copy ... | github_jupyter |
# _*Qiskit Finance: Loading and Processing Stock-Market Time-Series Data*_
The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.
***
### Contributors
Jakub Marecek<sup>[1]</sup>
### Affiliation
- <sup>[1]</sup>IBMQ
### Intr... | github_jupyter |
Azure ML & Azure Databricks notebooks by Parashar Shah.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as y... | github_jupyter |
# 🔢 Vectorizing Guide
Firstly, we must import what we need from Relevance AI
```
from relevanceai import Client
from relevanceai.utils.datasets import (
get_iris_dataset,
get_palmer_penguins_dataset,
get_online_ecommerce_dataset,
)
client = Client()
```
## Example 1
For this first example we going to w... | github_jupyter |
1. Recap
==
In the last mission, we explored how to use a simple k-nearest neighbors machine learning model that used just one feature, or attribute, of the listing to predict the rent price. We first relied on the <span style="background-color: #F9EBEA; color:##C0392B">accommodates</span> column, which describes the ... | github_jupyter |
```
#convert
```
# babilim.model.layers.roi_ops
> Operations for region of interest extraction.
```
#export
from babilim.core.annotations import RunOnlyOnce
from babilim.core.module_native import ModuleNative
#export
def _convert_boxes_to_roi_format(boxes):
"""
Convert rois into the torchvision format.
... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
import pathlib
sys.path.append(str(pathlib.Path().cwd().parent))
from typing import Tuple
from load_dataset import Dataset
from plotting import plot_ts
dataset = Dataset('../data/dataset/')
```
### В чем заключаются недостатки полносвязных сетей?
* невозможность ула... | github_jupyter |
# Milestone2 Document
## Feedback
- Introduction: A nice introduction!
- Background -0.5: It would be hard for users to understand automatic differentiation, computational graph, and evaluation trace if you don't give the corresponding illustrations in the Background section
**Revision: provided a concrete ex... | github_jupyter |
```
import pandas as pd
from joblib import dump, load
import os
#set up directory
#os.chdir()
#Drug dic
#open file
df_drugs=pd.read_csv(r"C:\Users\mese4\Documents\The Data incubator\project\Drugmap\drugbank vocabulary.csv", encoding='ISO-8859-1')
synonyms = []
drug_names = df_drugs['Common_name'].tolist()
drug_names... | github_jupyter |
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