text stringlengths 2.5k 6.39M | kind stringclasses 3
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### x lines of Python
# Reading and writing LAS files
This notebook goes with [the Agile blog post](https://agilescientific.com/blog/2017/10/23/x-lines-of-python-load-curves-from-las) of 23 October.
Set up a `conda` environment with:
conda create -n welly python=3.6 matplotlib=2.0 scipy pandas
You'll need `wel... | github_jupyter |
<a href="https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20Deployment/Course%203%20-%20TensorFlow%20Datasets/Week%202/Examples/Week2ExerciseA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Licensed un... | github_jupyter |
```
%%bash
# Download model check-point and module from below repo by pudae:
# Check if tf-slim will have densenet121 at some point
wget -N https://github.com/pudae/tensorflow-densenet/raw/master/nets/densenet.py
wget -N https://ikpublictutorial.blob.core.windows.net/deeplearningframeworks/tf-densenet121.tar.gz
tar xz... | github_jupyter |
# Nodes
From the [Interface](basic_interfaces.ipynb) tutorial, you learned that interfaces are the core pieces of Nipype that run the code of your desire. But to streamline your analysis and to execute multiple interfaces in a sensible order, you have to put them in something that we call a ``Node``.
In Nipype, a nod... | github_jupyter |
```
interaction_dataframe = ppi_df
columns = ['xref_A', 'xref_B']
identifier_series = pd.Series(pd.unique(interaction_dataframe[columns].values.ravel('K')))
ids = identifier_series[identifier_series.str.startswith('ensembl:')]
from pathlib import Path
import pandas as pd
import numpy as np
from phppipy.dataprep import... | github_jupyter |
# Introduction
:label:`chap_introduction`
Until recently, nearly every computer program that we interact with daily
was coded by software developers from first principles.
Say that we wanted to write an application to manage an e-commerce platform. After huddling around a whiteboard for a few hours to ponder the pr... | github_jupyter |
(2.1.0)=
# 2.1.0 Download ORACC JSON Files
Each public [ORACC](http://oracc.org) project has a `zip` file that contains a collection of JSON files, which provide data on lemmatizations, transliterations, catalog data, indexes, etc. The `zip` file can be found at `http://oracc.museum.upenn.edu/[PROJECT]/json/[PROJECT].... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
def f(theta, t):
c1 = -0.2
c2 = 0.2
c3 = 1.2
return np.exp(t * (theta - c1)**2) + np.exp(t * ((theta - c2)**2 +0.1)) + np.exp(t * (theta - c3)**2)
def get_optimal_and_minimizer(t, iters, lr, theta_0=0.25):
c1 = -0.2
c2 = 0.2
c3 = 1.2
... | github_jupyter |
```
import pandas as pd
method_raw_text = pd.read_excel('sents_df.xlsx')
```
# Knowledge Related Sentences in Reviews - Co-Word Network
```
# replace all newlines from dataframe
method_raw_text = method_raw_text.replace('\n','', regex=True)
method_raw_text = method_raw_text.dropna()
import re
for line in method_raw_t... | github_jupyter |
Nineth exercice: non-Cartesian MR image reconstruction
=============================================
In this tutorial we will reconstruct an MRI image from radial undersampled kspace measurements. Let us denote $\Omega$ the undersampling mask, the under-sampled Fourier transform now reads $F_{\Omega}$.
Import neuroim... | github_jupyter |
## Job Description to Resume Comparator - FreqDist
This program compares the words found in a job description to the words in a resume. The current version compares all words and gives a naive percentage match.
```
from nltk import sent_tokenize, word_tokenize, pos_tag
from nltk.corpus import stopwords
import pandas... | github_jupyter |
# Density Profile and IFT of mixture of Hexane + Ethanol and CPME
First it's needed to import the necessary modules
```
import numpy as np
import matplotlib.pyplot as plt
from sgtpy import component, mixture, saftvrmie
from sgtpy.equilibrium import bubblePy
from sgtpy.sgt import sgt_mix
```
The ternary mixture is c... | github_jupyter |
# The Unique Properties of Qubits
```
from qiskit import *
from qiskit.visualization import plot_histogram
```
You now know something about bits, and about how our familiar digital computers work. All the complex variables, objects and data structures used in modern software are basically all just big piles of bits. ... | github_jupyter |
# Cross Validation
> Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on pe... | github_jupyter |
# Data Download and Pre-processing
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import yaml
import os
from usal_echo import bucket, dcm_dir, img_dir, segmentation_dir, model_dir, classification_model
from usal_echo.d00_utils.db_utils import *
from usal_echo.d01_data.ingestion_dcm import ingest_dcm
from... | github_jupyter |
```
import numpy as np
import sys
import matplotlib.pyplot as plt
import os
import scipy.constants as ct
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
Ryd2eV=13.605692
bohr2nm=ct.physical_constants["Bohr radius"][0]*1e9
print bohr2nm
plt.rcParams['figure.figsize'] = (14.0, 10.0)
def readmps(filename):
... | github_jupyter |
# Find hospitals closest to an incident
The `network` module of the ArcGIS API for Python can be used to solve different types of network analysis operations. In this sample, we see how to find the hospital that is closest to an incident.
## Closest facility
The closest facility solver provides functionality for fin... | github_jupyter |
## maggy - MNIST Example
---
Created: 24/04/2019
This notebook illustrates the usage of the maggy framework for asynchronous hyperparameter optimization on the famous MNIST dataset.
In this specific example we are using random search over three parameters and we are deploying the median early stopping rule in order... | github_jupyter |
```
import sys
import os
import json
import numpy as np
import glob
import copy
%matplotlib inline
import matplotlib.pyplot as plt
import importlib
import util_human_model_comparison
import util_figures_psychophysics
sys.path.append('/packages/msutil')
import util_figures
def load_results_dict(results_dict_fn, pop_... | github_jupyter |
<font size=5>Confusion Matrix</font>
<p>When we build models, it is important to assess how good or bad our model is, and how well it performs on unseen data. Several metrics like accuracy, time taken etc. exist to evaluate model performance. We will see some of the most important and useful ones for the same.</p>
<p>... | github_jupyter |
# Cloud APIs for Computer Vision: Up and Running in 15 Minutes
This code is part of [Chapter 8- Cloud APIs for Computer Vision: Up and Running in 15 Minutes ](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch08.html).
## Get MSCOCO validation image ids with legible text
We will devel... | github_jupyter |
# Fleet Predictive Maintenance: Part 2. Data Preparation with Data Wrangler
1. [Architecure](0_usecase_and_architecture_predmaint.ipynb#0_Architecture)
1. [Data Prep: Processing Job from Data Wrangler Output](./1_dataprep_dw_job_predmaint.ipynb)
1. [Data Prep: Featurization](./2_dataprep_predmaint.ipynb)
1. [Train, Tu... | github_jupyter |
# Purpose
This notebook's purpose is to sift through all of the hospital chargemasters and metadata generated via the work already done in [this wonderful repo](https://github.com/vsoch/hospital-chargemaster) (from which I forked my repo). This is where the data engineering for Phase II of this project occurs. For mor... | github_jupyter |
```
import numpy as np
import random
from math import *
import time
import copy
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
torch.set_default_tensor_type('torch.DoubleTensor')
# defination of activation function
def activation(x):
return x * torch.sigmoid... | github_jupyter |
# Renaming CSV Files
This notebook renames the CSV files of the ESA project. The filenames in the SQL database are not very descriptive, therefore it was important to change the filenames for a better user experience.
The current filenames look something like this: 1059614_14_lattice-v_1.csv
In this notebook, we wil... | github_jupyter |
```
from local_vars import root_folder
data_folder = r"Circles"
image_size = 64
batch_size = 20
input_intensity_scaling = 1 / 255.0
import pandas as pd
import numpy as np
import itertools
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from matplotlib import pyplot a... | github_jupyter |
# Calculating Thermodynamics Observables with a quantum computer
```
# imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from functools import partial
from qiskit.utils import QuantumInstance
from qiskit import Aer
from qiskit.algorithms import NumPyMinimumEigensolver, VQE
from qiskit_na... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
```
### 1. Data science formalism
```
from sklearn.datasets import load_iris
iris = load_iris()
iris.keys()
```
In supervised machine learning, you have some data with the corresponding label for thes... | github_jupyter |
```
import numpy as np
import cv2
import matplotlib.pyplot as plt
import os
from pdf2image import convert_from_path
import tempfile
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn import svm
import segment_boards
%matplotlib inline
def sbw(im):
plt.ims... | github_jupyter |
```
import numpy as np
from scipy.stats import gennorm, norm
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.stats import binned_statistic
def bin_data(data,
min_ct=10,
num_bins=10,
ascending=False,
noise=1E-6,
ma... | github_jupyter |
# Import
```
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('runs/lenet')
```
# Loa... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=2
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
```
import os
def get_recursive_filenames(directory,upc_to_filenames):
for name in os.listdir(directory):
path = os.path.join(directory, name)
if os.path.isdir(path):
get_recursive_filenames(path,upc_to_filenames)
else:
upc = os.path.basename(os.path.dirname(path))
... | github_jupyter |
INRODUCTION
This tutorial will describe a broad overview of the Plotly visualization tool for Python. It is a wrapper based around the matplotlib library. The advantages of using the plotly library is that it has a faster learning curve and lower complexity compared to the matplotlib library. It is suitable for data ... | github_jupyter |
# <font color='firebrick'><center>Idx Stats Report</center></font>
### This report provides information from the output of samtools idxstats tool. It outputs the number of mapped reads per chromosome/contig.
<br>
```
from IPython.display import display, Markdown
from IPython.display import HTML
import IPython.core.dis... | github_jupyter |
```
# DATA SETUP
''' Note: for more information about our data pre-processing
and categorizing into states, see the README in the data folder.
We have cited sources for all data used and included a brief
description of the states we decided on there.'''
import numpy as np
import pandas as pd
from qubayes_tools import... | github_jupyter |
# Vietnam
## Table of contents
1. [General Geography](#1)<br>
1.1 [Soil Resources](#11)<br>
1.2 [Road and Railway Network](#12)<br>
2. [Poverty in Vietnam](#2)<br>
2.1 [The percentage of malnourished children under 5 in 2018 by locality](#21)<br>
2.2 [Proportion of poor households by region from 1998... | github_jupyter |
```
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import sonnet as snt
from graph_nets import utils_tf
from graph_nets import utils_np
from graph_nets import graphs
from root_gnn.src.generative import mlp_gan as toGan
from root_gnn.utils_plot import add_mean_std
batch_size = 1... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import numpy as np
import tensorflow as tf
import json
with open('dataset-bpe.json') as fopen:
data = json.load(fopen)
train_X = data['train_X']
train_Y = data['train_Y']
test_X = data['test_X']
test_Y = data['test_Y']
EOS = 2
GO = 1
vocab_size = 32000
train_Y ... | github_jupyter |
```
from flask import Flask
import matplotlib.pyplot as plt
from flask import Flask, request, render_template
import pandas
import os
import sys
from flask import Flask, request, session, g, redirect, url_for, abort, render_template
from flaskext.mysql import MySQL
from flask_wtf import FlaskForm
from wtforms.fields.ht... | github_jupyter |
# 02 - Ensembling: Bagging, Boosting and Ensemble
<div class="alert alert-block alert-success">
<b>Version:</b> v0.1 <b>Date:</b> 2020-06-09
在这个Notebook中，记录了`Randomforest` `XGBoost` 以及模型组合的实现策略。
</div>
<div class="alert alert-block alert-info">
<b>💡:</b>
- **环境依赖**： Fastai v2 (0.0.18), XGBoost(1.1.1), sk... | github_jupyter |
# Chapter 2: Working With Lists
Much of the remainder of this book is dedicated to using data structures to produce analysis that is elegant and efficient. To use the words of economics, you are making a long-term investment in your human capital by working through these exercises. Once you have invested in these fixe... | github_jupyter |
# Pretrained Transformers as Universal Computation Engines Demo
This is a demo notebook illustrating creating a Frozen Pretrained Transformer (FPT) and training on the Bit XOR task, which converges within a couple minutes.
arXiv: https://arxiv.org/pdf/2103.05247.pdf
Github: https://github.com/kzl/universal-computati... | github_jupyter |
<a href="https://colab.research.google.com/github/Scott-Huston/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling/blob/master/LS_DS_121_Join_and_Reshape_Data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
_Lambda School Data Science_
# Join and Re... | github_jupyter |
# An agent-based model of social support
*Joël Foramitti, 10.02.2022*
This notebook introduces a simple agent-based model to explore the propagation of social support through a population.
```
import agentpy as ap
import networkx as nx
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
```
The a... | github_jupyter |
```
!wget https://datahack-prod.s3.amazonaws.com/train_file/train_LZdllcl.csv -O train.csv
!wget https://datahack-prod.s3.amazonaws.com/test_file/test_2umaH9m.csv -O test.csv
!wget https://datahack-prod.s3.amazonaws.com/sample_submission/sample_submission_M0L0uXE.csv -O sample_submission.csv
import numpy as np
import p... | github_jupyter |
# TensorFlow2 및 SMDataParallel을 사용한 분산 데이터 병렬 BERT 모델 훈련
SMDataParallel은 Amazon SageMaker의 새로운 기능으로 딥러닝 모델을 더 빠르고 저렴하게 훈련할 수 있습니다. SMDataParallel은 PyTorch, TensorFlow 및 MXNet을 위한 분산 데이터 병렬 훈련 프레임워크입니다.
이 노트북 예제는 [Amazon SageMaker](https://aws.amazon.com/sagemaker/)에서 TensorFlow(버전 2.3.1)와 함께 SMDataParallel을 사용하여 [Ama... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
```
### Read in json line file
```
board_data = []
with open('company-officers-v2.json') as f:
for line in f:
board_data.append(json.loads(line))
board_data[1]
```
### Read out board member information
and write into ... | github_jupyter |
# Density Tree for N-dimensional data and labels
The code below implements a **density** tree for non-labelled data.
## Libraries
First, some libraries are loaded and global figure settings are made for exporting.
```
import numpy as np
import matplotlib.pyplot as plt
import os
from IPython.core.display import Image,... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0, '../')
sys.path.append('/home/arya_03/.envs/objdet/lib/python2.7/site-packages/')
import matplotlib
matplotlib.use('Agg')
from __future__ import division
import os
import numpy as np
import pandas as pd
from skimage.io import imread
import cv2
from pa... | github_jupyter |
# Multi-Layer Perceptron, MNIST
---
In this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.
The process will be broken down into the following steps:
>1. Load and visualize the data
2. Define a neural network
3. Train the model... | github_jupyter |
# Agilent 34411A versus Keysight 34465A
The following notebook perfoms a benchmarking of the two DMMs. In part one, raw readings of immediate voltages are timed
and compared. In part two, actual sweeps are performed with a QDac.
```
%matplotlib notebook
import time
import matplotlib.pyplot as plt
import numpy as np
... | github_jupyter |
<small><i>This notebook was originally put together by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2014. [Peter Prettenhofer](https://github.com/pprett) adapted it for PyCon Ukraine 2014. Source and license info is on [GitHub](https://github.com/pprett/sklearn_pycon2014/).</i></small>
# Part 2: Representati... | github_jupyter |
Tensor RTに変換された学習済みモデルをつかって自動走行します。
```
import torch
import torchvision
CATEGORIES = ['apex']
device = torch.device('cuda')
model = torchvision.models.resnet18(pretrained=False)
model.fc = torch.nn.Linear(512, 2 * len(CATEGORIES))
model = model.cuda().eval().half()
```
Tensor RT形式のモデルを読み込む。
```
import torch
from t... | github_jupyter |
```
#내장 함수 정리
#abs : 절댓값 리턴
num = abs(-5)
print(num)
#all, any : 참 거짓 리턴
"""
+-----------------------------------------+---------+---------+
| | any | all |
+-----------------------------------------+---------+---------+
| All Truthy values | True... | github_jupyter |
# Build Experiment from tf.layers model
Embeds a 3 layer FCN model to predict MNIST handwritten digits in a Tensorflow Experiment. The model is built using the __tf.layers__ API, and wrapped in a custom Estimator, which is then wrapped inside an Experiment.
```
from __future__ import division, print_function
from ten... | github_jupyter |
# HOW TO ADD A NEW CLASS TO OBJECT DETECTION PIPELINE?
```
## Uncomment command below to kill current job:
#!neuro kill $(hostname)
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../')
from detection.model import get_model
from detection.coco_subset import CLS_SELECT, COLORS, N_COCO_CLASSES
from detec... | github_jupyter |
```
if 0 :
%matplotlib inline
else :
%matplotlib notebook
```
# Import libraries
```
import sys
import os
module_path = os.path.abspath('.') +"\\_scripts"
print(module_path)
if module_path not in sys.path:
sys.path.append(module_path)
from _00_Import_packages_git3 import *
from time import sleep
from s... | github_jupyter |
created by Fernando Perez ( https://www.youtube.com/watch?v=g8xQRI3E8r8 )
# Prerequisites2 : Python Data Science Environment
# Learning Plan
### Lesson 2-1: IPython
In this lesson, you learn how to use IPython.
### ... | github_jupyter |
This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).
# Data Structure Utilities
* slice
* range and xrange
* bisect
* sort
* sorted
* reversed
* enumerate
* zip
* list comprehensions
## slice
Slic... | github_jupyter |
# Alternative Models
In order to ensure the model used to make predictions for the analysis, I also tried training & testing various other models that were good candidates (based on the characteristics of our data).
Specifically, we also tested the following regression models:
1. Linear (Lasso Regularization)
2. Linea... | github_jupyter |
## Setup
```
%matplotlib qt
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import os
Path('mnist_distribution').mkdir(exist_ok=True)
os.chdir('mnist_distribution')
#load MNIST and concatenates train and test data
(x_train, _), (x_test, _) = mnist... | github_jupyter |
# Plots for litholog paper
Using the demo data provided in the `litholog` release, this notebook demonstrates data import, plotting and simple statistics of bed thickness and grain size.
## Import packages
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import scipy.... | github_jupyter |
This approach checks if some data preprocessing helps on the results.
```
import pandas as pd
import re
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Gena/map_get_center.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="ht... | github_jupyter |
# Advanced Matplotlib Concepts Lecture
In this lecture we cover some more advanced topics which you won't usually use as often. You can always reference the documentation for more resources!
#### Logarithmic scale
It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact onl... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good model.
This does have the potential to make the project quite messy! Before subm... | github_jupyter |
# EOS 1 image analysis Python code walk-through
- This is to explain how the image analysis works for the EOS 1. Python version 2.7.15 (Anaconda 64-bit)
- If you are using EOS 1, you can use this code for image analysis after reading through this notebook and understand how it works.
- Alternatively, you can also us... | github_jupyter |
# Exploration of the modulators and downstream effectors of HDAC6
```
import time
import sys
import getpass
from collections import defaultdict
import bel_repository
import bio2bel_hgnc
import bio2bel_famplex
import pandas as pd
import pybel
import pybel_jupyter
import pybel_tools
import hbp_knowledge
from pybel.dsl... | github_jupyter |
# Development Notebook for extracting icebergs from DEMs
by Jessica Scheick
Workflow based on previous methods and code developed by JScheick for Scheick et al 2019 *Remote Sensing*.
***Important note about CRS handling*** This code was developed while also learning about Xarray, rioxarray, rasterio, and other Pytho... | github_jupyter |
```
import csv
from bs4 import BeautifulSoup
from selenium import webdriver
from datetime import datetime
import requests
driver=webdriver.Chrome(executable_path="F:\Web_Scraping\chromedriver.exe")
url = "https://www.naukri.com/"
def get_url(post,location):
template="https://in.indeed.com/jobs?q={}&l={}"
url=te... | github_jupyter |
```
import numpy as np
import tensorflow as tf
experiment = '_16snap'
_04847_img = np.load('4847' + experiment + '-image.npy')
_04799_img = np.load('4799' + experiment + '-image.npy')
_04820_img = np.load('4820' + experiment + '-image.npy')
_05675_img = np.load('5675' + experiment + '-image.npy')
_05680_img = np.load(... | github_jupyter |
# Homework 1
[](https://colab.research.google.com/github/rhennig/EMA6938/blob/main/Notebooks/Homework1.ipynb)
## Problem 1 (100 points using the rubric)
In this problem, we will investigate a polynomial regression model on a 2-dimensional datas... | github_jupyter |
# CORONA VIRUS PANDEMIC!🦠
# **What is Corona Virus?[](http://)**
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus.
Most people infected with the COVID-19 virus will experience mild to mo... | github_jupyter |
## Визуализация данных в Python
**Материал подготовила Арина Ситникова**
Теперь, когда мы рассмотрели основы препроцессинга данных в Python, используя библиотеки Numpy и Pandas, мы можем перейти к очень интересному, но при этом очень важному блоку, который составляет немалую часть работы специалистов в области data s... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
# IWI131 Programación
## Diccionarios
Un diccionario es una **colección no ordenada** que permite asociar llaves con valores. Utilizando la llave siempre es posible recuperar, de manera eficiente, el valor asociado.
- El funcionamiento de diccionarios es similar a cuando se recupera un elemento de una lista usando s... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn import metrics
from mlxtend.plotting import plot_decision_regions
from sklearn import preprocessing, metrics... | github_jupyter |
<a href="https://colab.research.google.com/github/rudyhendrawn/traditional-dance-video-classification/blob/main/tari_vgg16_lstm_224.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import os
import glob
from keras_video import VideoFrameGenerator... | github_jupyter |
# 1-Getting Started
Always run this statement first, when working with this book:
```
from scipy import *
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
## Numbers
```
2 ** (2 + 2)
1j ** 2 # A complex number
1. + 3.0j # Another complex number
```
##... | github_jupyter |
# Automatic Suggestion of Constraints
In our experience, a major hurdle in data validation is that someone needs to come up with the actual constraints to apply on the data. This can be very difficult for large, real-world datasets, especially if they are very complex and contain information from a lot of different so... | github_jupyter |
# Shingling with Jaccard
Comparing document similarities where the set of objects is word or character ngrams taken over a sliding window from the document (shingles). The set of shingles is used to determine the document similarity, Jaccard similarity, between a pair of documents.
```
from tabulate import tabulate
... | github_jupyter |
# Character Issues
```
s = 'café'
len(s)
b = s.encode('utf8')
b
len(b)
b.decode('utf8')
```
# Byte Essentials
```
cafe = bytes('café', encoding='utf_8')
cafe
cafe[0]
cafe[:1]
cafe_arr = bytearray(cafe)
cafe_arr
cafe_arr[-1:]
bytes.fromhex('31 4B CE A9')
import array
numbers = array.array('h', [-2, -1, 0, 1, 2])
octe... | github_jupyter |
```
# Reload when code changed:
%load_ext autoreload
%autoreload 2
%pwd
import os
import sys
path = "../"
sys.path.append(path)
#os.path.abspath("../")
print(os.path.abspath(path))
import core
import importlib
importlib.reload(core)
import logging
importlib.reload(core)
try:
logging.shutdown()
importlib.reloa... | github_jupyter |
<a href="https://colab.research.google.com/github/morcellinus/Python_ML-DL/blob/main/3.Iris_data_classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Iris classification
```
import pandas as pd
import numpy as np
from sklearn import dat... | github_jupyter |
# 4 - Convolutional Sentiment Analysis
In the previous notebooks, we managed to achieve a test accuracy of ~85% using RNNs and an implementation of the [Bag of Tricks for Efficient Text Classification](https://arxiv.org/abs/1607.01759) model. In this notebook, we will be using a *convolutional neural network* (CNN) to... | github_jupyter |
# Neural networks with PyTorch
Next I'll show you how to build a neural network with PyTorch.
```
# Import things like usual
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
... | github_jupyter |
# Simulators
## Introduction
This notebook shows how to import *Qiskit Aer* simulator backends and use them to execute ideal (noise free) Qiskit Terra circuits.
```
import numpy as np
# Import Qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.to... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Data-Load" data-toc-modified-id="Data-Load-1"><span class="toc-item-num">1 </span>Data Load</a></span></li><li><span><a href="#Integrated-Gradients" data-toc-modified-id="Integrated-Gradients-2">... | github_jupyter |
# A trip on a lift
### Experiment about the motion in 1D and Data Analysis
The motion of a lift can be considered as an example of a motion along a straight line, which we can shortly refer to as **motion in 1D**.
The aim of this worked example is that of studying the position, the velocity and the acceleration as a... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import statsmodels.formula.api as smf
import sklearn
from sklearn.linear_model import Lasso
import matplotlib.pyplot as plt
import sys
alpha = 0.7
```
### PROJECT INTRODUCTION
The World Health Organization (WHO) estimates that each year ... | github_jupyter |
# widgets.image_cleaner
fastai offers several widgets to support the workflow of a deep learning practitioner. The purpose of the widgets are to help you organize, clean, and prepare your data for your model. Widgets are separated by data type.
```
from fastai.vision import *
from fastai.widgets import DatasetFormatt... | github_jupyter |
# Harvesting Commonwealth Hansard
The proceedings of Australia's Commonwealth Parliament are recorded in Hansard, which is available online through the Parliamentary Library's ParlInfo database. [Results in ParlInfo](https://parlinfo.aph.gov.au/parlInfo/search/summary/summary.w3p;adv=yes;orderBy=_fragment_number,doc_d... | github_jupyter |
# Matplotlib
```
import matplotlib.pyplot as plt
import numpy as np
x = [1,2,3]
y = [2,4,6]
plt.scatter(x,y) #scatter point on all the mentioned axis
plt.show()
x = [1,2,3]
y = [2,4,6]
plt.plot(x,y) #connect the point for me
plt.show()
x = [1,2,3]
y = [2,4,6]
plt.plot(x,y) #connect the point for me
plt.scatter(x,y)
p... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
# Modul Python Bahasa Indonesia
## Seri Kesembilan
___
Coded by psychohaxer | Version 1.1 (2020.12.24)
___
Notebook ini berisi contoh kode dalam Python sekaligus outputnya sebagai referensi dalam coding. Notebook ini boleh disebarluaskan dan diedit tanpa mengubah atau menghilangkan nama pembuatnya. Selamat belajar dan ... | github_jupyter |
# Enumerating BiCliques to Find Frequent Patterns
#### KDD 2019 Workshop
#### Authors
- Tom Drabas (Microsoft)
- Brad Rees (NVIDIA)
- Juan-Arturo Herrera-Ortiz (Microsoft)
#### Problem overview
From time to time PCs running Microsoft Windows fail: a program might crash or hang, or you experience a kernel crash leadi... | github_jupyter |
## Multinomial Naive Bayes
O Multinomial Naive Bayes supõe que os recursos sejam gerados a partir de uma distribuição multinomial simples. A distribuição multinomial descreve a probabilidade de observar contagens entre várias categorias e, portanto, o Multinomial Naive Bayes é mais apropriado para características que r... | github_jupyter |
# LCLS Archiver restore
These examples show how single snapshots, and time series can be retreived from the archiver appliance.
Note that the times must always be in ISO 8601 format, UTC time (not local time).
```
%pylab --no-import-all inline
%config InlineBackend.figure_format = 'retina'
from lcls_live.archiver im... | github_jupyter |
<a href="https://colab.research.google.com/github/jsedoc/ConceptorDebias/blob/master/Experiments/Conceptors/Gradient_Based_Conceptors.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import torch
import torch.nn.functional as F
from torch import ... | github_jupyter |
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