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# Improve accuracy of pdf batch processing with Amazon Textract and Amazon A2I
In this chapter and this accompanying notebook learn with an example on how you can use Amazon Textract in asynchronous mode by extracting content from multiple PDF files in batch, and sending specific content from these PDF documents to an... | github_jupyter |
import modules and get command-line parameters if running as script
```
from probrnn import models, data, inference
import numpy as np
import json
from matplotlib import pyplot as plt
from IPython.display import clear_output
```
parameters for the model and training
```
params = \
{
"N_ITERATIONS": 10 *... | github_jupyter |
# Inference and Validation
Now that you have a trained network, you can use it for making predictions. This is typically called **inference**, a term borrowed from statistics. However, neural networks have a tendency to perform *too well* on the training data and aren't able to generalize to data that hasn't been seen... | github_jupyter |
# Point Spread Function Photometry with Photutils
The PSF photometry module of photutils is intended to be a fully modular tool such that users are able to completly customise the photometry procedure, e.g., by using different source detection algorithms, background estimators, PSF models, etc. Photutils provides impl... | github_jupyter |
# 🦌 RuDOLPH 350M
<b><font color="white" size="+2">Official colab of [RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP](https://github.com/sberbank-ai/ru-dolph)</font></b>
<font color="white" size="-0.75."><b>RuDOLPH</b> is a fast and light text-image-text transformer (350M GPT-3) for... | github_jupyter |
# Quantitative omics
The exercises of this notebook correspond to different steps of the data analysis of quantitative omics data. We use data from transcriptomics and proteomics experiments.
## Installation of libraries and necessary software
Copy the files *me_bestprobes.csv* and _AllQuantProteinsInAllSamples.csv_... | github_jupyter |
<table><tr>
<td style="background-color:#ffffff;text-align:left;"><a href="http://qworld.lu.lv" target="_blank"><img src="../images/qworld.jpg" width="30%" align="left"></a></td>
<td style="background-color:#ffffff;"> </td>
<td style="background-color:#ffffff;vertical-align:text-middle;text-align:righ... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive', force_remount=True)
cd 'drive/My Drive/Colab Notebooks/machine_translation'
from dataset import MTDataset
from model import Encoder, Decoder
from language import Language
from utils import preprocess
from train import train
from eval import validate
from ... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=Mathematics/FractionMultiplication/Frac... | github_jupyter |
# 1 - Sequence to Sequence Learning with Neural Networks
In this series we'll be building a machine learning model to go from once sequence to another, using PyTorch and torchtext. This will be done on German to English translations, but the models can be applied to any problem that involves going from one sequence to... | github_jupyter |
# mlforecast
> Scalable machine learning based time series forecasting.
**mlforecast** is a framework to perform time series forecasting using machine learning models, with the option to scale to massive amounts of data using remote clusters.
[
os.system('mkdir tacotron2-female-alignment')
import tensorflow as tf
import numpy as np
from glob import glob
import tensorflow as tf
import malaya_speech
import malaya_speech.train
from malaya_speech.train.model imp... | github_jupyter |
```
import numpy as np
import cvxpy as cp
import networkx as nx
import matplotlib.pyplot as plt
# Problem data
reservations = np.array([110, 118, 103, 161, 140])
flight_capacities = np.array([100, 100, 100, 150, 150])
cost_per_hour = 50
cost_external_company = 75
# Build transportation grah
G = nx.DiGraph()
# Add node... | github_jupyter |
```
import tempfile
import urllib.request
train_file = "datasets/thermostat/sample-training-data.csv"
test_file = "datasets/thermostat/test-data.csv"
import pandas as pd
COLUMNS = ["month", "day", "hour", "min", "pirstatus",
"isDay", "extTemp", "extHumidity", "loungeTemp", "loungeHumidity",
"state... | github_jupyter |
# Fuzzing APIs
So far, we have always generated _system input_, i.e. data that the program as a whole obtains via its input channels. However, we can also generate inputs that go directly into individual functions, gaining flexibility and speed in the process. In this chapter, we explore the use of grammars to synth... | github_jupyter |
```
#Copyright 2020 Vraj Shah, Arun Kumar
#
#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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in w... | github_jupyter |
```
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed unde... | github_jupyter |
# Creating a class
```
class Student: # created a class "Student"
name = "Tom"
grade = "A"
age = 15
def display(self):
print(self.name,self.grade,self.age)
# There will be no output here, because we are not invoking (calling) the "display" function to print
```
##... | github_jupyter |
# Linear Discriminant Analysis (LDA)
## Importing the libraries
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## Importing the dataset
```
dataset = pd.read_csv('Wine.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
```
## Splitting the dataset into the Training... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Inference PyTorch Bert Model with ONNX Runtime on CPU
In this tutorial, you'll be introduced to how to load a Bert model from PyTorch, convert it to ONNX, and inference it for high performance using ONNX Runtime. In the foll... | github_jupyter |
```
!pip install chart_studio
import plotly.graph_objects as go
import plotly.offline as offline_py
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
import numpy as np
%matplotlib inline
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/DSEI21000-... | github_jupyter |
```
# 加载文本分类数据集
from sklearn.datasets import fetch_20newsgroups
import random
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
print(... | github_jupyter |
# MDP from multidimensional HJB
see [pdf](https://github.com/songqsh/foo1/blob/master/doc/191206HJB.pdf) for its math derivation
see souce code at
- [py](hjb_mdp_v05_3.py) for tabular approach and
- [py](hjb_mdp_nn_v05.py) for deep learning approach
```
import numpy as np
import time
#import ipdb
import itertools... | github_jupyter |
```
!nvidia-smi
import sys
if 'google.colab' in sys.modules:
!pip install -Uqq fastcore onnx onnxruntime sentencepiece seqeval rouge-score
!pip install -Uqq --no-deps fastai ohmeow-blurr
!pip install -Uqq transformers datasets wandb
from fastai.text.all import *
from fastai.callback.wandb import *
from tran... | github_jupyter |
```
import pandas as pd
import numpy as np
from pathlib import Path
dir_path = Path().resolve().parent / 'demand_patterns'
low_patterns = "demand_patterns_train_low.csv"
fullrange_patterns = "demand_patterns_train_full_range.csv"
combined_pattern = 'demand_patterns_train_combined.csv'
comb = pd.read_csv(dir_path / com... | github_jupyter |
<a href="http://cocl.us/pytorch_link_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " />
</a>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN... | github_jupyter |
# Configuring pandas
```
# import numpy and pandas
import numpy as np
import pandas as pd
# used for dates
import datetime
from datetime import datetime, date
# Set some pandas options controlling output format
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', 8)
pd.set_option('... | github_jupyter |
Synergetics<br/>[Oregon Curriculum Network](http://4dsolutions.net/ocn/)
<h3 align="center">Computing Volumes in XYZ and IVM units</h3>
<h4 align="center">by Kirby Urner, July 2016</h4>
A cube is composed of 24 identical no... | github_jupyter |
```
!pip install yacs
!pip install gdown
import os, sys, time
import argparse
import importlib
from tqdm.notebook import tqdm
from imageio import imread
import torch
import numpy as np
import matplotlib.pyplot as plt
```
### Download pretrained
- We use HoHoNet w/ hardnet encoder in this demo
- Download other version ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("test2_result.csv")
df
df2 = pd.read_excel("Test_2.xlsx")
# 只含特征值的完整数据集
data = df2.drop("TRUE VALUE", axis=1)
# 只含真实分类信息的完整数据集
labels = df2["TRUE VALUE"]
# data2是去掉真实分类信息的数据集（含有聚类后的结果）
data2 = df.drop("TRUE VALUE", axis=1)
data... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Water/usgs_watersheds.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank... | github_jupyter |
<img align="right" src="images/ninologo.png" width="150"/>
<img align="right" src="images/tf-small.png" width="125"/>
<img align="right" src="images/dans.png" width="150"/>
# Start
This notebook gets you started with using
[Text-Fabric](https://github.com/Nino-cunei/uruk/blob/master/docs/textfabric.md) for coding in ... | github_jupyter |
```
import zmq
import msgpack
import sys
from pprint import pprint
import json
import numpy as np
import ceo
import matplotlib.pyplot as plt
%matplotlib inline
port = "5556"
```
# SETUP
```
context = zmq.Context()
print "Connecting to server..."
socket = context.socket(zmq.REQ)
socket.connect ("tcp://localhost:%s" %... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Format license keys.
See the [LeetCode](https://leetcode.com/problems/license-key-formatting/) problem p... | github_jupyter |
##### Copyright 2018 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | github_jupyter |
<a href="https://colab.research.google.com/github/ElizaLo/Practice-Python/blob/master/Data%20Compression%20Methods/Huffman%20Code/Huffman_code.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Huffman Coding
## **Solution**
```
import heapq
from c... | github_jupyter |
```
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
dataset = pd.read_csv(r'C:\Users\ANOVA AJAY PANDEY\Desktop\SEM4\CSE 3021 SIN\proj\stock analysis\Google_Stock_Price_Train.csv',index_col="Date",parse_dates=True)
dataset = pd.read_csv(r'C:\Users\ANOVA AJ... | github_jupyter |
```
import os
import sys
import itertools
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.regression.linear_model as sm
from scipy import io
from mpl_toolkits.axes_grid1 import make_axes_locatable
path_root = os.environ.get('DECIDENET_PATH')
path_code... | github_jupyter |
# Loading and Checking Data
## Importing Libraries
```
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import math
import numpy as np
import matplotlib.pyplot as plt
%matp... | github_jupyter |
## Apprentissage supervisé: Forêts d'arbres aléatoires (Random Forests)
Intéressons nous maintenant à un des algorithmes les plus popualires de l'état de l'art. Cet algorithme est non-paramétrique et porte le nom de **forêts d'arbres aléatoires**
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as p... | github_jupyter |
[Sascha Spors](https://orcid.org/0000-0001-7225-9992),
Professorship Signal Theory and Digital Signal Processing,
[Institute of Communications Engineering (INT)](https://www.int.uni-rostock.de/),
Faculty of Computer Science and Electrical Engineering (IEF),
[University of Rostock, Germany](https://www.uni-rostock.de/en... | github_jupyter |
```
import os
import argparse
from keras.preprocessing.image import ImageDataGenerator
from keras import callbacks
import numpy as np
from keras import layers, models, optimizers
from keras import backend as K
from keras.utils import to_categorical
import matplotlib.pyplot as plt
from utils import combine_images
from P... | 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 |
```
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from functools import lru_cache
from numba import jit
import community
import warnings; warnings.simplefilter('ignore')
@jit(nopython = True)
def generator(A):
B = np.zeros((len(A)+2, len(A)+2), np.int_)
B[1:-1,1:-1] = A
for i in r... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
os.chdir(sys.path[0]+"/../data")
import urllib.request
from bs4 import BeautifulSoup
import pandas as pd
import re
from tqdm import tqdm
categories = [
"100 metres, Men",
"200 metres, Men",
"400 metres, Men",
"800 ... | github_jupyter |
```
# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
!pip install jovian --upgrade -q
import jovian
jovian.set_project('pandas-practice-assignment')
jovian.set_colab_id('1EMzM1GAuekn6b3mjbgjC83UH-2XgQHAe')
```
# Assignment 3 - Pandas Data Analy... | github_jupyter |
```
import numpy as np
import S_Dbw as sdbw
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics.pairwise import pairwise_distances_argmin
np.random.seed(0)
S_Dbw_result = []
batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
cluster_std=[0.7,0.3,1.2]
n_... | github_jupyter |
```
from xml.etree import ElementTree
from xml.dom import minidom
from xml.etree.ElementTree import Element, SubElement, Comment, indent
def prettify(elem):
"""Return a pretty-printed XML string for the Element.
"""
rough_string = ElementTree.tostring(elem, encoding="ISO-8859-1")
reparsed = minidom.par... | github_jupyter |
# Multi-variate Rregression Metamodel with DOE based on random sampling
* Input variable space should be constructed using random sampling, not classical factorial DOE
* Linear fit is often inadequate but higher-order polynomial fits often leads to overfitting i.e. learns spurious, flawed relationships between input an... | github_jupyter |
# Histograms of time-mean surface temperature
## Import the libraries
```
# Data analysis and viz libraries
import aeolus.plot as aplt
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
# Local modules
from calc import sfc_temp
import mypaths
from names import names
from commons import MODELS
impo... | github_jupyter |
# Near to far field transformation
See on [github](https://github.com/flexcompute/tidy3d-notebooks/blob/main/Near2Far_ZonePlate.ipynb), run on [colab](https://colab.research.google.com/github/flexcompute/tidy3d-notebooks/blob/main/Near2Far_ZonePlate.ipynb), or just follow along with the output below.
This tutorial wi... | github_jupyter |
# Data Visualization
The RAPIDS AI ecosystem and `cudf.DataFrame` are built on a series of standards that simplify interoperability with established and emerging data science tools.
With a growing number of libraries adding GPU support, and a `cudf.DataFrame`’s ability to convert `.to_pandas()`, a large portion of th... | github_jupyter |
<a href="https://colab.research.google.com/github/AWH-GlobalPotential-X/AWH-Geo/blob/master/notebooks/AWH-Geo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Welcome to AWH-Geo
This tool requires a [Google Drive](https://drive.google.com/drive/my-d... | github_jupyter |
# Chapter 7
```
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
import statsmodels.api as sm
import statsmodels.formula.api as smf
from patsy import dmatrix
from scipy import stats
from scipy.special import logsumexp
%config Inline.figure_format = 'retina'
... | github_jupyter |
MIT License
Copyright (c) 2017 Erik Linder-Norén
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish,... | github_jupyter |
# Training and hosting SageMaker Models using the Apache MXNet Gluon API
When there is a person in front of you, your human eyes can immediately recognize what direction the person is looking at (e.g. either facing straight up to you or looking at somewhere else). The direction is defined as the head-pose. We are goin... | github_jupyter |
# Kaggle San Francisco Crime Classification
## Berkeley MIDS W207 Final Project: Sam Goodgame, Sarah Cha, Kalvin Kao, Bryan Moore
### Environment and Data
```
# Additional Libraries
%matplotlib inline
import matplotlib.pyplot as plt
# Import relevant libraries:
import time
import numpy as np
import pandas as pd
from... | github_jupyter |
# Data Cleaning
For each IMU file, clean the IMU data, adjust the labels, and output these as CSV files.
```
%load_ext autoreload
%autoreload 2
%matplotlib notebook
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensembl... | github_jupyter |
```
import csv
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from google.colab import files
```
The data for this exercise is available at: https://www.kaggle.com/datamunge/sign-language-mnist/home
Sign up and download to find 2 CSV files: sign_mnist_te... | github_jupyter |
# Getting Started with *pyFTracks* v 1.0
**Romain Beucher, Roderick Brown, Louis Moresi and Fabian Kohlmann**
The Australian National University
The University of Glasgow
Lithodat
*pyFTracks* is a Python package that can be used to predict Fission Track ages and Track lengths distributions for some given thermal-his... | github_jupyter |
```
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as Data
import torchvision
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
path = 'data/mnist/'
raw_train = pd.read_csv(path + 'train.csv')
raw_test = pd.read_csv(pa... | github_jupyter |
```
import sys
sys.path.append('C:\\Users\dell-pc\Desktop\大四上\Computer_Vision\CNN')
from data import *
from network import three_layer_cnn
# data
train_data, test_data = loaddata()
import numpy as np
print(train_data.keys())
print("Number of train items: %d" % len(train_data['images']))
print("Number of test items: %d"... | github_jupyter |
# Preliminary instruction
To follow the code in this chapter, the `yfinance` package must be installed in your environment. If you do not have this installed yet, review Chapter 4 for instructions on how to do so.
# Chapter 9: Risk is a Number
```
# Chapter 9: Risk is a Number
import pandas as pd
import numpy as np... | github_jupyter |
# Boucles
https://python.sdv.univ-paris-diderot.fr/05_boucles_comparaisons/
Répéter des actions
## Itération sur les éléments d'une liste
```
placard = ["farine", "oeufs", "lait", "sucre"]
for ingredient in placard:
print(ingredient)
```
Remarques :
- La variable *ingredient* est appelée *variable d'itération... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
print(os.listdir("../input"))
import time
# import pytorch
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import SGD,Adam,lr_scheduler
from torch.utils.data im... | github_jupyter |
```
from os import path
# Third-party
import astropy
import astropy.coordinates as coord
from astropy.table import Table, vstack
from astropy.io import fits
import astropy.units as u
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from pyvo.dal import TAPService
from pyi... | github_jupyter |
```
import csv
import matplotlib
import matplotlib.pyplot as plt
auth_csv_path = "./auth_endpoint_values.csv"
service_csv_path = "./service_endpoint_values.csv"
def convert_cpu_to_dict(file_path):
data = []
with open(file_path) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
csv_re... | github_jupyter |
<a href="https://bmi.readthedocs.io"><img src="https://raw.githubusercontent.com/csdms/espin/main/media/bmi-logo-header-text.png"></a>
# Run the `Heat` model through its BMI
`Heat` models the diffusion of temperature on a uniform rectangular plate with Dirichlet boundary conditions. This is the canonical example used... | github_jupyter |
<a href="https://colab.research.google.com/github/iamsoroush/DeepEEGAbstractor/blob/master/cv_rnr_8s_proposed_gap.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title # Clone the repository and upgrade Keras {display-mode: "form"}
!git clone... | github_jupyter |
<a href="https://colab.research.google.com/github/Yoshibansal/ML-practical/blob/main/Cat_vs_Dog_Part-1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##Cat vs Dog (Binary class classification)
ImageDataGenerator
(Understanding overfitting)
Downlo... | github_jupyter |
# Part I. ETL Pipeline for Pre-Processing the Files
## PLEASE RUN THE FOLLOWING CODE FOR PRE-PROCESSING THE FILES
#### Import Python packages
```
# Import Python packages
import pandas as pd
import cassandra
import re
import os
import glob
import numpy as np
import json
import csv
```
#### Creating list of filepat... | github_jupyter |
# MPLPPT
`mplppt` is a simple library made from some hacky scripts I used to use to convert matplotlib figures to powerpoint figures. Which makes this a hacky library, I guess 😀.
## Goal
`mplppt` seeks to implement an alternative `savefig` function for `matplotlib` figures. This `savefig` function saves a `matplotli... | github_jupyter |
```
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
# Releasing the GPU memory
torch.cuda.empty_cache()
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | github_jupyter |
<a href="https://colab.research.google.com/github/BachiLi/A-Tour-of-Computer-Animation/blob/main/A_Tour_of_Computer_Animation_Table_of_Contents.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**A Tour of Computer Animation** -- [Tzu-Mao Li](https://... | github_jupyter |
```
import urllib2
from bs4 import BeautifulSoup
url = 'https://www.baidu.com/'
content = urllib2.urlopen(url).read()
soup = BeautifulSoup(content, 'html.parser')
soup
print(soup.prettify())
for tag in soup.find_all(True):
print(tag.name)
soup('head')# or soup.head
soup.body
soup.body.name
soup.meta.string
soup.f... | github_jupyter |
# Approximate q-learning
In this notebook you will teach a __tensorflow__ neural network to do Q-learning.
__Frameworks__ - we'll accept this homework in any deep learning framework. This particular notebook was designed for tensorflow, but you will find it easy to adapt it to almost any python-based deep learning fr... | github_jupyter |
# Developing Advanced User Interfaces
*Using Jupyter Widgets, Pandas Dataframes and Matplotlib*
While BPTK-Py offers a number of high-level functions to quickly plot equations (such as `bptk.plot_scenarios`) or create a dashboard (e.g. `bptk.dashboard`), you may sometimes be in a situation when you want to create more... | github_jupyter |
<a href="https://colab.research.google.com/github/kartikgill/The-GAN-Book/blob/main/Skill-08/Cycle-GAN-No-Outputs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Import Useful Libraries
```
import pandas as pd
import numpy as np
import matplotlib... | github_jupyter |
# 5. Algorithmic Question
You consult for a personal trainer who has a back-to-back sequence of requests for appointments. A sequence of requests is of the form : 30, 40, 25, 50, 30, 20 where each number is the time that the person who makes the appointment wants to spend. You need to accept some requests, however yo... | github_jupyter |
```
import pickle
import pandas as pd
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import bcolz
import unicodedata
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
import torch.optim as optim
import matplotlib.pyplot as ... | github_jupyter |
<a href="https://colab.research.google.com/github/agemagician/Prot-Transformers/blob/master/Embedding/Advanced/Electra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<h3> Extracting protein sequences' features using ProtElectra pretrained-model <h3... | github_jupyter |
# Decision Point Price Momentum Oscillator (PMO)
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:dppmo
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# fix_yahoo_finance is used to fetch data
import fix_yahoo... | github_jupyter |
```
import re
import requests
import time
from requests_html import HTML
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
options = Options()
options.add_argument("--headless")
driver = webdriver.Chrome(options=options)
categories = [
"https://www.amazon.com/Best-Sellers-Toys-Ga... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
# 上述函数，其输入是包含1个多个枚举类别的2D数组，需要reshape成为这种数组
# from sklearn.preprocessing import CategoricalEncoder #后面会添加这个方法
from sklearn... | github_jupyter |
```
library(keras)
```
**Loading MNIST dataset from the library datasets**
```
mnist <- dataset_mnist()
x_train <- mnist$train$x
y_train <- mnist$train$y
x_test <- mnist$test$x
y_test <- mnist$test$y
```
**Data Preprocessing**
```
# reshape
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_re... | github_jupyter |
## This notebook contains a sample code for the COMPAS data experiment in Section 5.2.
Before running the code, please check README.md and install LEMON.
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn import feature_extraction
from sklearn import preprocessing
from sklear... | github_jupyter |
```
import pandas as pd
import numpy as np
import re
from scipy.integrate import odeint
# Read the data in, then select the relevant columns, and adjust the week so it is easier to realize
# as a time series.
virii = ["A (H1)", "A (H3)", "A (2009 H1N1)", "A (Subtyping not Performed)", "B"]
virus = "B"
file = "data/200... | github_jupyter |
# Scene Classification-Test
## 1. Preprocess-KerasFolderClasses
- Import pkg
- Extract zip file
- Preview "scene_classes.csv"
- Preview "scene_{0}_annotations_20170922.json"
- Test the image and pickle function
- Split data into serval pickle file
This part need jupyter notebook start with "jupyter notebook --Notebook... | github_jupyter |
```
from collections import OrderedDict
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import scipy as sp
from theano import shared
%config InlineBackend.figure_format = 'retina'
az.style.use('arviz-darkgrid')
```
#### Code 11.1
```
trolley_df = pd.read_c... | github_jupyter |
<a href="https://colab.research.google.com/github/iesous-kurios/DS-Unit-2-Applied-Modeling/blob/master/module4/BuildWeekProject.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%%capture
import sys
# If you're on Colab:
if 'google.colab' in sys.... | github_jupyter |
# Chapter 3
***Ver como criar uma tabela de conteúdo TOC**
## Strings
```
a = "My dog's name is"
b = "Bingo"
c = a + " " + b
c
#trying to add string and integer
d = "927"
e = 927
d + e
```
## Lists
```
a = [0, 1, 1, 2, 3, 5, 8, 13]
b = [5., "girl", 2+0j, "horse", 21]
b[0]
b[1]
```
<div class="alert alert-block al... | github_jupyter |
```
%matplotlib inline
```
Captum을 사용하여 모델 해석하기
===================================
**번역**: `정재민 <https://github.com/jjeamin>`_
Captum을 사용하면 데이터 특징(features)이 모델의 예측 또는 뉴런 활성화에
미치는 영향을 이해하고, 모델의 동작 방식을 알 수 있습니다.
그리고 \ ``Integrated Gradients``\ 와 \ ``Guided GradCam``\ 과 같은
최첨단의 feature attribution 알고리즘을 적용할 수 있습니다.... | github_jupyter |
```
import sys
sys.path.append('../src')
from numpy import *
import matplotlib.pyplot as plt
from Like import *
from PlotFuncs import *
import WIMPFuncs
pek = line_background(6,'k')
fig,ax = MakeLimitPlot_SDn()
alph = 0.25
cols = cm.bone(linspace(0.3,0.7,4))
nucs = ['Xe','Ge','NaI']
zos = [0,-50,-100,-50]
C_Si = WIM... | github_jupyter |
# <center>HW 01: Geomviz: Visualizing Differential Geometry<center>
## <center>Special Euclidean Group SE(n)<center>
<center>$\color{#003660}{\text{Swetha Pillai, Ryan Guajardo}}$<center>
# <center> 1.) Mathematical Definition of Special Euclidean SE(n)<center>
### <center> This group is defined as the set of direct... | github_jupyter |
## A track example
The file `times.dat` has made up data for 100-m races between Florence Griffith-Joyner and Shelly-Ann Fraser-Pryce.
We want to understand how often Shelly-Ann beats Flo-Jo.
```
%pylab inline --no-import-all
```
<!-- Secret comment:
How the data were generated
w = np.random.normal(0,.07,10000)
x ... | github_jupyter |
## Load Python Packages
```
# --- load packages
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.nn.modules.distance import PairwiseDistance
from torch.utils.data import Dataset
from torchvision import transforms
from torchsummary import summary
from torch.... | github_jupyter |
# Data
Data en handelingen op data
## Informatica
een taal leren $\sim$ **syntax** (noodzakelijk, maar niet het punt)
... informatica studeren $\sim$ **semantiek** (leren hoe machines denken!)
Een programmeertaal als Python leren heeft alles te maken met syntax waarmee je handelingen kan schrijven die een machine ... | github_jupyter |
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