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# Find the Repos Available in your Database, and What Repository Groups They Are In
## Connect to your database
```
import psycopg2
import pandas as pd
import sqlalchemy as salc
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
import datetime
import json
warnings.filterwarning... | github_jupyter |
<a href="https://colab.research.google.com/github/noahgift/distributed-computing-explorations/blob/main/Concurrency_Python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Concurrency in Python
* *[Watch Video Lesson 6.6: Use concurrency methods... | github_jupyter |
# Facies detection model
Seismic horizon is a change in rock properties across a boundary between two layers of rock, particularly seismic velocity and density. Such changes are visible in seismic images (even for an untrained eye), and could be automatically detected. This notebook demonstrates how to build convolut... | github_jupyter |
```
from IPython.display import SVG
from sklearn.datasets import load_digits
from keras.utils.vis_utils import model_to_dot
from keras.models import Sequential, Model
from keras.layers import Input, Dense, concatenate, Activation
```
### Load dataset
- digits dataset in scikit-learn
- url: http://scikit-learn.org/stab... | github_jupyter |
## Excitability of Network
Construct network such that stimuli recieved from the first state
impacts processing of next state, the memory of the system.
```
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import spikey
np.random.seed(0)
def quincience_time(w_matrix, neuron, **config):
neu... | github_jupyter |
# Tutorial - Model Prediction
```
#Import Section
from sklearn.feature_selection import SelectKBest,f_regression
from sklearn.linear_model import LinearRegression,BayesianRidge,ElasticNet,Lasso,SGDRegressor,Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.preprocessing import LabelEncoder,Imputer,OneHot... | github_jupyter |
```
%load_ext line_profiler
%load_ext autoreload
import numpy as np
import tensorflow as tf
import neural_tangents as nt
from neural_tangents import stax
from jax.config import config; config.update("jax_enable_x64", True)
import jax.numpy as jnp
from jax import random, jit
from matplotlib import pyplot as plt
%mat... | github_jupyter |
## Train a model with linear data using XGBoost algorithm
### Model is trained with XGBoost installed in notebook instance
### In the later examples, we will train using SageMaker's XGBoost algorithm
```
# Install xgboost in notebook instance.
#### Command to install xgboost
!conda install -y -c conda-forge xgboost
... | github_jupyter |
<div align="right"><i>Peter Norvig<br>March 2019</i></div>
# Pairing Socks
[Bram Cohen](https://en.wikipedia.org/wiki/Bram_Cohen) posed a problem that I'll restate thusly:
> *You have N pairs of socks, all different, in the dryer.
You pull random socks out one-by-one, placing each sock in one of C possible places o... | github_jupyter |
# Bay Area Bike Share Analysis
## Introduction
> **Tip**: Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook.
[Bay Area Bike Share](http://www.bayareabikeshare.com/) is a company that provides on-demand bike rentals for customers in San Francisco, Redwood City,... | github_jupyter |
```
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import scipy as sc
import numpy as np
import matplotlib.ticker as mticker
```
<h2> Import data (Make sure to parse dates.Consider setting index colum... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
# from google.colab import drive
# drive.mount('/content/drive')
!pwd
path = '/content/drive/MyDrive/Research/AAAI/cifar_new/k_001b/sixth_run1_'
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotli... | github_jupyter |
# Test of widgets
* lets see what we got here
```
# try the following:
#!pip install ipywidgets==7.4.2
#!pip install bqplot
# lets import our usual stuff
import pandas as pd
import bqplot
import numpy as np
import traitlets
import ipywidgets
%matplotlib inline
data = np.random.random((10, 10))
# now add scales - col... | github_jupyter |
# Knowledge Based Recommendation System of Recipe Ingredients
## Notebook 3: Recommend Similar Ingredients using Word2Vec and Cosine Similarity
### Project Breakdown
1 Exploratory Data Analysis and Preprocessing
2: Build Word Embeddings using Word2Vec, FastText
3: Recommend similar ingredients
4: Build... | github_jupyter |
# Probabilistic Programming and Bayesian Methods for Hackers Chapter 2
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter2_MorePyMC/Ch2_MorePyMC_T... | github_jupyter |
```
from decodes.core import *
from decodes.io.jupyter_out import JupyterOut
import math
out = JupyterOut.unit_square( )
```
# Geometric Properties of Surfaces
The use of nearest neighbor approximation allowed us to present the geometric properties that captured the shape of a curve, quantities were derived by enact... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
df_olist_leads = pd.read_csv("../Sri_CapstoneProject_Olist/olist_marketing_qualified_leads_dataset.csv")
df_closed_leads = pd.read_csv("../Sri_CapstoneProject_Olist/olist_closed_deals_dataset.csv")
df_p... | github_jupyter |
# 基于注意力的神经机器翻译
此笔记本训练一个将立陶宛语翻译为英语的序列到序列（sequence to sequence，简写为 seq2seq）模型。此例子难度较高，需要对序列到序列模型的知识有一定了解。
训练完此笔记本中的模型后，你将能够输入一个立陶宛语句子，例如 *"Aš bandau."*，并返回其英语翻译 *"I try."*
对于一个简单的例子来说，翻译质量令人满意。但是更有趣的可能是生成的注意力图：它显示在翻译过程中，输入句子的哪些部分受到了模型的注意。
<img src="https://tensorflow.google.cn/images/spanish-english.png" alt="spanish... | github_jupyter |
# EM-based method for OT-based registration (polynomial)
## Description
### General approach
This version of OT registration formulates the problem of image registration as a latent variable model, and proposes an iterative, EM-based approach to inferring the spatial mapping between the two images from the data. Thi... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Realce-de-Contraste-Interativo-utilizando-Janela-e-Nível" data-toc-modified-id="Realce-de-Contraste-Interativo-utilizando-Janela-e-Nível-1"><span class="toc-item-num">1 </span>Realce de Contraste Interativo utilizando Janela e Nível</a></div><div c... | github_jupyter |
# Monte Carlo Methods
## Part 0: Explore BlackjackEnv
```
import sys
import gym
import numpy as np
from collections import defaultdict
from plot_utils import plot_blackjack_values, plot_policy
env = gym.make('Blackjack-v0')
print(env.observation_space)
print(env.action_space)
for i_episode in range(3):
state = e... | github_jupyter |
# Example: CanvasXpress violin Chart No. 5
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/violin-5.html
This example is generated using the reproducible JSON obtained from the above page an... | github_jupyter |
# Simple Recurrent Language Model
Predicting the next token.
# Imports and Setup
Common imports and standardized code for importing the relevant data, models, etc., in order to minimize copy-paste/typo errors.
Set the relevant text field (`'abstract'` or `'title'`) and whether we are working with `'one-hot'` or `'... | github_jupyter |
```
import tensorflow as tf
import numpy as np
```
## mnist dataset
<br>
http://yann.lecun.com/exdb/mnist/
```
import pickle
# load pickle dataset
def load(data_path):
with open(data_path,'rb') as f:
mnist = pickle.load(f)
return mnist["training_images"], mnist["training_labels"], mnist["test_images... | github_jupyter |
```
# download InferSent sentence encoder and GloVe vectors
!git clone https://github.com/facebookresearch/InferSent
!cp -r ./InferSent/* .
!mkdir -p dataset/GloVe
!curl -Lo encoder/infersent1.pickle https://dl.fbaipublicfiles.com/senteval/infersent/infersent1.pkl
!curl -Lo dataset/GloVe/glove.840B.300d.zip http://nlp.... | github_jupyter |
<a href="https://colab.research.google.com/github/csy99/dna-nn-theory/blob/master/supervised_viridae_adam256_save_embedding.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import pandas as pd
import matplotlib
import matplotli... | github_jupyter |
# <font face="times"><font size="6pt"><p style = 'text-align: center;'> BRYN MAWR COLLEGE
<font size="6pt"><p style = 'text-align: center;'><b><font face="times">Computational Methods in the Physical Sciences</b><br/><br/>
<p style = 'text-align: center;'><b><font face="times">Module 1: A Brief Introduction to Pytho... | github_jupyter |
```
import pandas as pd
import tsfresh
import os
import json
import scapy
import numpy as np
import warnings
from scapy.all import *
warnings.filterwarnings("ignore") #ignore warnings caused by
#################################################################
# ... | github_jupyter |
# Exponential
Here we analyse how accurate are the approximate functions for exponential
### Define a benchmark method
```
import os, sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import torch as th
import matplotlib.pyplot as plt
import numpy as np
def benchmark(real_func, approx_func, interval, n_points=... | github_jupyter |
# Expectation-maximization algorithm
In this assignment, we will derive and implement formulas for Gaussian Mixture Model — one of the most commonly used methods for performing soft clustering of the data.
### Installation
We will need ```numpy```, ```scikit-learn```, ```matplotlib``` libraries for this assignment
... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
fro... | github_jupyter |
# Задание 2.2 - Введение в PyTorch
Для этого задания потребуется установить версию PyTorch 1.0
https://pytorch.org/get-started/locally/
В этом задании мы познакомимся с основными компонентами PyTorch и натренируем несколько небольших моделей.<br>
GPU нам пока не понадобится.
Основные ссылки:
https://pytorch.org/t... | github_jupyter |
# Qcodes example with Rohde Schwarz RTO 1000 series Oscilloscope
```
%matplotlib notebook
import matplotlib.pyplot as plt
import qcodes as qc
from qcodes.instrument_drivers.rohde_schwarz.RTO1000 import RTO1000
rto = RTO1000('rto', 'TCPIP0::172.20.3.86::inst0::INSTR')
# Before we do anything, let's make sure that the ... | github_jupyter |
```
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
df = pd.read_csv('../dataset/GOOG-year.csv')
df.head()
from collections import deque
import random
class Actor:
def __init__(self, name, input_size, output_size, size_layer):
w... | github_jupyter |
# Publishing SQL Data as JSON Documents
## Publishing JSON
Up to this point, we have focused on JSON functions that allow you to extract values, objects, and arrays from documents. There are many circumstances where you want to be able to take the existing data in a table and make it available to outside world as JSON... | github_jupyter |
# pyLectureMultiModalAnalysis
## Feature extraction from video segment
### Functions: 2
#### video2frame(), frame2features()
### Author: Stelios Karozis
## RandomVector()
```
def RandomVector(trainmode=True,sz=100):
import pickle
import numpy as np
from numpy import array
import random
if ... | github_jupyter |
#Random Forest
## Importación de librerías y datos
Por medio de nuestra libería ESIOS_contoller.py importamos nuestro último dataset de datos y lo parseamos para su uso. Sirve tanto como para Drive como jupiter.
```
import json, urllib, datetime, pickle, time
import pandas as pd
import numpy as np
import seaborn as ... | github_jupyter |
# GSuite Tools
This notebook contains examples for using GSuite.
```
from pymagic.gsuite_tools import GDrive,GSheets,GMail
import pandas as pd
import os, sys
# if sys.platform == "linux":
# wd = "/home/collier/Downloads/"
# else:
# wd = "/Users/collier/Downloads/"
# os.chdir(wd)
```
# Authentication
T... | github_jupyter |
# Frequently Asked Questions
## What is "Scientific Programming"?
**Scientific programming targets to solve scientific problems with the help of computers**.
It is sometimes used as synonym for [computational science](https://en.wikipedia.org/wiki/Computational_science), but in my opinion these are not entirely th... | github_jupyter |
# Advanced Feature Engineering in BQML
**Learning Objectives**
1. Evaluate the model
2. Extract temporal features, feature cross temporal features
3. Apply ML.FEATURE_CROSS to categorical features
4. Create a Euclidian feature column, feature cross coordinate features
5. Apply the BUCKETIZE function, TRANSFORM clause... | github_jupyter |
<small><small><i>
All of these python notebooks are available at [https://gitlab.erc.monash.edu.au/andrease/Python4Maths.git]
</i></small></small>
# Python ...
- is an open source programming language
- is an object-oriented programming language
- is an interpreter-language
- provides easy interfaces to other language... | github_jupyter |
# Load data
<https://www.kaggle.com/c/bike-sharing-demand>
```
import sage
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# Load data
df = sage.datasets.bike()
feature_names = df.columns.tolist()[:-3]
# Split data, with total count serving as regression target
... | github_jupyter |
```
import pandas as pd
from scipy import stats
from scipy.stats import norm
gee_userid = pd.read_csv('./gee_results/gee_user_id.csv')
gee_userid = gee_userid[['name','estimate','naive_z']]
# gee_userid['pnorm'] = 2*min(norm.cdf(gee_userid['naive_z']),1-norm.cdf(gee_userid['naive_z']))
gee_userid['p1'] = 2*norm.cdf(gee... | github_jupyter |
# Lesson 3 Exercise 2: Focus on Primary Key
<img src="images/cassandralogo.png" width="250" height="250">
### Walk through the basics of creating a table with a good Primary Key in Apache Cassandra, inserting rows of data, and doing a simple SQL query to validate the information.
#### We will use a python wrapper/ py... | github_jupyter |
```
# 경찰서별 담당 행정구역
sgg_nm_transfer = {
'마산동부경찰서' : '창원시마산회원구' ,
'마산중부경찰서' : '창원시마산합포구' ,
'서울강남경찰서' : '강남구' ,
'서울강동경찰서' : '강동구' ,
'서울강북경찰서' :'강북구' ,
'서울강서경찰서' : '강서구',
'서울관악경찰서' : '관악구' ,
'서울광진경찰서' : '광진구',
'서울구로경찰서' : '구로구' ,
'서울금천경찰서' : '금천구' ,
'서울남대문경찰서' : '중... | github_jupyter |
```
import warnings
warnings.filterwarnings("ignore")
import os
import json
import jieba
import torch
import pickle
import codecs
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from ark_nlp.model.re.casrel_bert import CasRelBert
from ark_nlp.model.re.casrel_bert import CasRelBertConfig
from ark... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$$
\newcommand{\set}[1]{\left\{#1\right\}}
\newcommand{\abs}[1]{\left\lvert#1\right\rvert}
\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
\newcommand{\inner}[2]{\left\langle#1,#2\right\rangle}
\newcomma... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
import glob
import os
import pickle
from collections import OrderedDict
from scipy import signal
from tqdm import tqdm
from pathlib import Path
from sklearn.pipeline import Pipeline
from torchsummary import s... | github_jupyter |
# TextAttack with Custom Dataset and Word Embedding. This tutorial will show you how to use textattack with any dataset and word embedding you may want to use
[](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2noteboo... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
```
### Revenue per Weekday
section - revenue per minute:
* fruit 4€
* spices 3€
* dairy 5€
* drinks 6€
```
df = pd.read_csv('data/data_clean.csv', index_col=0, sep= ',', header=0)
# create revenue column
def label_revenue (row):
if row['locati... | github_jupyter |
Neuromorphic engineering I
## Lab 9: Silicon Neuron Circuits
Team member 1: Jan Hohenheim
Team member 2: Maxim Gärtner
Date: 25.11.21
-------------------------------------------------------------------------------------------------------------------
In this lab, we will test a circuit that generates action potent... | github_jupyter |
```
import csv
import os
import enchant
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sys.path.append('..')
sns.set()
sns.set_style("ticks")
output_file = os.path.join(
'..',
'results',
'post-ocr-correction',
'char-to-char-encoder-decoder',
'english',
'output-english-... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
```
# Snowflake time of flight
Have you ever watched a snowflake fall and thought, "How long has that snowflake been falling?"
Here, we want to determine the time of flight for a snowflake. We'll start simple and add some complex... | github_jupyter |
```
from jupyter_cadquery.occ import Part, PartGroup, show
from jupyter_cadquery import set_sidecar
set_sidecar("OCC", init=True)
```
# OCC bottle (ported over to OCP)
```
import math
from OCP.gp import gp_Pnt, gp_Vec, gp_Trsf, gp_Ax2, gp_Ax3, gp_Pnt2d, gp_Dir2d, gp_Ax2d, gp
from OCP.GC import GC_MakeArcOfCircle, G... | github_jupyter |
```
from geoscilabs.dcip.DC_Pseudosections import MidpointPseudoSectionWidget, DC2DPseudoWidget
from IPython.display import display
```
# Building Pseudosections
2D profiles are often plotted as pseudo-sections by extending $45^{\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\... | github_jupyter |
```
# default_exp tabular.core
```
# tabular.core
> API details.
```
#hide
#export
import pandas as pd
from fastai.data.external import *
from fastcore.all import *
from pathlib import PosixPath
from fastcore.test import *
from fastai.tabular.all import *
import fastai
from fastai.tabular.core import _maybe_expand
f... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import collections
def build_dataset(words, n_words):
count = [['GO', 0], ['PAD', 1], ['EOS', 2], ['UNK', 3]]
count.extend(collections.Counter(words).most_common(n_words - 1))
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictiona... | github_jupyter |
## 7. Set Yourself Up for Success
A Python `set` is an _unordered_ collection: the elements of a set do not have a position or order, so you cannot do indexing, slicing, or other sequence-like operations on sets as you would do on, for instance, lists.
Sets in Python mimic mathematical sets: the elements do not repe... | github_jupyter |
```
import os
from time import time
import numpy as np
from sklearn.linear_model import RidgeClassifier
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from tlopu.model_utils import pick_model
from tlopu.features import fast_conv_features, decoding, get_random_featur... | github_jupyter |
# Exporting ImageNet Inception
_WARNING: you are on the master branch; please refer to examples on the branch corresponding to your `cortex version` (e.g. for version 0.24.*, run `git checkout -b 0.24` or switch to the `0.24` branch on GitHub)_
In this notebook, we'll show how to export the [pre-trained Imagenet Ince... | github_jupyter |
```
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
import io
from sklearn import preprocessing
import pandas as pd
# Read the data
data=pd.read_csv('pid_uri_id.csv')
trackinfo=data[['trackid','track_uri']]
trackinfo=trackinfo.drop_duplicates()
import os
text=list(data['trackid'... | github_jupyter |
<a href="https://colab.research.google.com/github/Micle5858/mit-deep-learning/blob/master/Neural_Networks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Introduction to Neural Networks
In this notebook you will learn how to create and use a neural... | github_jupyter |
# k-means with text data
In this assignment you will
* Cluster Wikipedia documents using k-means
* Explore the role of random initialization on the quality of the clustering
* Explore how results differ after changing the number of clusters
* Evaluate clustering, both quantitatively and qualitatively
When properly ex... | github_jupyter |
```
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from IPython.display import Image
Image('graph.png')
```
We are going to use networkx package to construct the graph and find the shortest paths. Refer to the [NetworkX documentation](https://networkx.github.io/documentation/stable/).
```
#t... | github_jupyter |
### Examples from Udacity course on web development
Link to Website: [https://de.udacity.com/course/web-development--cs253](https://de.udacity.com/course/web-development--cs253)
See lesson 5.
#### 1. HTTP requests with urllib2
```
import urllib2
page = urllib2.urlopen("http://www.example.com")
# dir(page) will list... | github_jupyter |
# Deep learning framework example: Movie Review Dataset
This notebook demonstrates how to use the deeplearning API to train and test the model on the [Stanford movie review corpus](https://nlp.stanford.edu/sentiment/) corpus. This dataset contains hand written digits and their labels. See the [saved version](https:/... | github_jupyter |
# Mask R-CNN - Train on Shapes Dataset
This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be ... | github_jupyter |
# BLU05 - Learning Notebook - Part 2 of 3 - SARIMAX
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from random import gauss
from random import seed
from statsmodels.tsa.seasonal import seasonal_decompose
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
seed(... | github_jupyter |
# Learning
In this notebook we'll discuss what it means for a computer to learn.
## Function
The term function is often not clearly defined and has many different uses.
In programming, we usually write functions where we kno... | github_jupyter |
# Building symbolic metamodels
A symbolic metamodel takes as an input a machine learning model, and outputs a symbolic equation describing its response surface as illustrated in the Figure below. This notebook provides the steps needed for building a symbolic metamodel for an XGBoost model fitted to the "UCI absenteei... | github_jupyter |
# Homework # 1: Audio Classification
In this work you will master all the basic skills with audio applied to the problem of classification.
You will:
* 🔥 master `torchaudio` as a library for working with audio in conjunction with `torch`
* 🔊 try out the different feature representations of the audio signal in pract... | github_jupyter |
```
%matplotlib inline
import os
import cv2
import time
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from weapons.CTC_0a import ctc_recog_model
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
def parse_filename(filename):
"""
vertices: se, sw, nw, ne
lp_indices: indices in province... | github_jupyter |
```
import pandas as pd
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("OnlineNewsPopularity.csv", skipinitialspace=True)
df.info()
import re
REGEX = re.compile("http://mashable.com/([0-9]{4}/[0-9]{2}/[0-9]{2})/([-a-z0-9_]+)/")
def parse_url(url):
date, slug = REGEX.find... | github_jupyter |
## Crypto Arbitrage
In this program, you'll take on the role of an analyst at a high-tech investment firm. The vice president (VP) of your department is considering arbitrage opportunities in Bitcoin and other cryptocurrencies. As Bitcoin trades on markets across the globe, can you capitalize on simultaneous price dis... | github_jupyter |
# The GDSCTools library
Nore that in this notebook, we need the following code but this may not be required in a script or a standard Python shell if pylab is already loaded.
## Regression methods
Currently (v0.15), we have 3 classes available that implements 3 regression methods namely:
- GDSCElasticNet
- GDSCRidg... | github_jupyter |
# 全卷积网络
:label:`sec_fcn`
如 :numref:`sec_semantic_segmentation`中所介绍的那样，语义分割是对图像中的每个像素分类。
*全卷积网络*（fully convolutional network，FCN）采用卷积神经网络实现了从图像像素到像素类别的变换 :cite:`Long.Shelhamer.Darrell.2015`。
与我们之前在图像分类或目标检测部分介绍的卷积神经网络不同，全卷积网络将中间层特征图的高和宽变换回输入图像的尺寸：这是通过在 :numref:`sec_transposed_conv`中引入的*转置卷积*（transposed convolution）实现的。... | github_jupyter |
# First you need to mount your Google Drive so that the contents are available for use here. To do this, run the cell below and follow the instructions (press enter after inserting the code)
```
from google.colab import drive
drive.mount('/content/drive')
```
# Then change the current directory so it's pointing to th... | github_jupyter |
# Listes
Les listes en Python sont un ensemble ordonnés d'objets. Les objets peuvent être de type variés. Une liste peux contenir une liste.
## Une liste est une séquence
* Une liste est délimité par des crochets `[]`
* Les éléments sont séparé par une virgule `,`
* Un élément peut être accédé par son indice `L[1]`
*... | github_jupyter |
# Basic Imports
```
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
```
# Load traning data
```
training_data = np.load('traning_data.npy',allow_pickle=True)
```
# Structure of network
```
class Net(nn.Module):
def __init__(self):
super().__i... | github_jupyter |
# Intro to Jupyter notebooks and Python for data science
Check out the keyboard shortcuts by going to help -> keyboard shortcuts.
I frequently use `esc` to exit a cell, `a` to add a cell above, `b` to add a cell below, `enter` to edit a cell, `shift+enter` to run a cell, arrow keys to go up and down between cells, `c... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader... | github_jupyter |
# 3D Multi-organ Segmentation with UNETR (BTCV Challenge)
# PyTorch Lightning Tutorial
Prepared by: **Ali Hatamizadeh** and **Yucheng Tang**.
This tutorial demonstrates how MONAI can be used in conjunction with PyTorch Lightning framework to construct a training workflow of UNETR on multi-organ segmentation task us... | github_jupyter |
# 引入所需模块
```
import re
import json
import requests
import numpy as np
import scipy.integrate as spi
import matplotlib.pyplot as plt
```
## 获取丁香园数据计算治愈率、死亡率
```
url = 'https://3g.dxy.cn/newh5/view/pneumonia'
response = requests.get(url)
origin = json.loads(re.search(
r'window.getStatisticsService = ({.*?})', res... | github_jupyter |
# FloPy shapefile export demo
The goal of this notebook is to demonstrate ways to export model information to shapefiles.
This example will cover:
* basic exporting of information for a model, individual package, or dataset
* custom exporting of combined data from different packages
* general exporting and importing of... | github_jupyter |
```
import cv2 as cv
import matplotlib.pylab as plt
import xmltodict
import glob
import json
import warnings
warnings.filterwarnings('ignore')
# Load image
img = cv.imread('/home/idl/Downloads/openlogo/JPEGImages/1034987742.jpg')
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
plt.imshow(img);
files = glob.glob('/home/idl/Do... | github_jupyter |
<table width="100%"> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="35%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by Abuzer Yak... | github_jupyter |
# Building your Deep Neural Network: Step by Step
Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!
- In this notebook, you will implement all the functio... | github_jupyter |
<center>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Logistic Regression with Python
Estimated time needed: **25** minutes
## Objectives
After completing this la... | github_jupyter |
# SNC Introduction
The aim of this document is to introduce the concepts needed to describe a stochastic network and its dynamics, and to explain how such network can be controlled.
Note that the discussion related to how to optimally control a stochastic network is out of the scope of this document. This topic is d... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Understanding-Expressions-in-Aerospike" data-toc-modified-id="Understanding-Expressions-in-Aerospike-1"><span class="toc-item-num">1 </span>Understanding Expressions in Aerospike</a></span><ul cl... | github_jupyter |
<a href="https://colab.research.google.com/github/yhatpub/yhatpub/blob/main/notebooks/fastai/lesson10_classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Fastai Lesson 10 on YHat.pub
This notebook picks up from [Fastai Fastbook 10 Text... | github_jupyter |
```
#!/usr/bin/env python3
# --------------------------------------------------------------
# Author: Mahendra Data - mahendra.data@dbms.cs.kumamoto-u.ac.jp
# License: BSD 3 clause
# --------------------------------------------------------------
# Mount Google Drive
from google.colab import drive
drive.mount("/content/... | github_jupyter |
# Chapter 8: Transformations
This Jupyter notebook is the Python equivalent of the R code in section 8.8 R, pp. 373 - 375, [Introduction to Probability, 2nd Edition](https://www.crcpress.com/Introduction-to-Probability-Second-Edition/Blitzstein-Hwang/p/book/9781138369917), Blitzstein & Hwang.
----
```
import matplo... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
from numpy import *
from IPython.html.widgets import *
from IPython.display import display
import matplotlib.pyplot as plt
from IPython.core.display import clear_output
```
# Gini coefficient
Gini coefficient is a measure of statistical dispersion. For the K... | github_jupyter |
# Smoothing
## Install packages
```
import sys
!{sys.executable} -m pip install -r requirements.txt
import cvxpy as cvx
import numpy as np
import pandas as pd
import time
import os
import quiz_helper
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (14, 8)
``... | github_jupyter |
<a href="https://colab.research.google.com/github/harshatejas/pytorch_custom_object_detection/blob/main/Training.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!git clone https://github.com/harshatejas/pytorch_custom_object_detection.git
%cd /c... | github_jupyter |
# Discretisers
Examples on how to use variable discretisation transformers available in Feature-engine.
For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:
Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing
Data as an End of Semester Regression Project, Jou... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
dataset=pd.read_csv('BankNote_Authentication.csv')
print(dataset.columns... | github_jupyter |
```
#Define libraries
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, BatchNormalization, Flatten
from sklearn.model_selection import KFold
from keras.utils import multi_gpu_model
#from sklearn.cross_validation import StratifiedKFol... | github_jupyter |
```
import numpy as np
import pickle # not necessary
import cv2 # Computer vision to convert image to array
from os import listdir
# Please make sure to google below subjects
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from... | github_jupyter |
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