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# Update attachment keywords in ArcGIS Enterprise
<table class="tfo-notebook-buttons" align="right">
<td>
<a target="_blank" href="https://www.arcgis.com/home/item.html?id=a02d62ef8b4e456d86b755b15dfb8204">Try it live</a>
</td>
<td>
<a target="_blank" href="https://github.com/Esri/Survey123-tools/tree/ma... | github_jupyter |
#this code reads in the NDBC buoy data at 1 hour and 10 minute resolution
the first notebook section is just some subroutines I write to read in the data and mask it correctly
```
#import libraries
import numpy.ma as MA
import datetime as dt
from datetime import datetime, timedelta
import xarray as xr
import numpy as ... | github_jupyter |
# Eliminating Outliers
Eliminating outliers is a big topic. There are many different ways to eliminate outliers. A data engineer's job isn't necessarily to decide what counts as an outlier and what does not. A data scientist would determine that. The data engineer would code the algorithms that eliminate outliers from... | github_jupyter |
```
%pylab inline
import matplotlib.pyplot as plt
import pickle
import numpy as np
import os
import sys
sys.path.append('../../code/scripts')
import fit_scaling_law
import plotting as p
```
# 1. aggregate data
```
acc_key = '1 - auc_roc'
acc_keys = ['auc_roc', 'acc']
group_names_r = [r' age $< 55$', r' age $\geq 55... | github_jupyter |
```
import sys, os, glob
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sw_plotting import change_bar_width
from sw_utilities import tukeyTest
# Make a folder if it is not already there to store exported figures
!mkdir ../jupyter_figures
# Data wrang... | github_jupyter |
# Imports
```
import numpy as np
from sklearn import metrics
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error , mean_squared_error , mean_absolute_percentage_error
import tensorflow as tf
from tensorflow.keras.layers i... | github_jupyter |
#Strings
Strings are used in Python to record text information, such as name. Strings in Python are actually a *sequence*, which basically means Python keeps track of every element in the string as a sequence. For example, Python understands the string "hello' to be a sequence of letters in a specific order. This mean... | github_jupyter |
<a href="https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Reproducing experimental results of LUKE on Open Entity Using Hugging Face Transforme... | github_jupyter |
```
dbutils.widgets.text("entityName", "", "Entity Name")
dbutils.widgets.text("dataSourceName", "", "Data Source Name")
dbutils.widgets.text("version", "", "Version")
dbutils.widgets.text("inputPath", "", "Input path")
dbutils.widgets.text("inputContainer", "", "Input container")
dbutils.widgets.text("outputPath", "",... | github_jupyter |
# Spatial Data
Overview of today's topics:
- Working with shapefiles, GeoPackages, CSV files, and rasters
- Projection
- Geometric operations
- Spatial joins
- Web mapping
- Spatial indexing
```
import ast
import contextily as cx
import folium
import geopandas as gpd
import matplotlib.pyplot as plt
impor... | github_jupyter |
```
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.utils import resample
import datetime as dt
from sklearn.metrics import confusion_matrix
import seaborn as sns
sns.set()
file = '/home/roscon/Desktop/Data_latest/Model/report/d/0.xlsx'
data = pd.read_excel(file,sheet_name=0)
data['Manual label'] ... | github_jupyter |
```
%matplotlib inline
```
What is PyTorch?
================
It’s a Python-based scientific computing package targeted at two sets of
audiences:
- A replacement for NumPy to use the power of GPUs
- a deep learning research platform that provides maximum flexibility
and speed
Getting Started
---------------
#... | github_jupyter |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, CuDNNLSTM, CuDNNGRU, BatchNormalization, LocallyConnected2D, ... | github_jupyter |
```
print ('Shreyans')
```
# Session 1: Introduction to Tensorflow
<p class='lead'>
Creative Applications of Deep Learning with Tensorflow<br />
Parag K. Mital<br />
Kadenze, Inc.<br />
</p>
<a name="learning-goals"></a>
# Learning Goals
* Learn the basic idea behind machine learning: learning from data and discover... | github_jupyter |
# `xarray-leaflet`
`xarray-leaflet` ist eine xarray-Erweiterung für das Plotten von gekachelten Karten. Sowohl [xarray](http://xarray.pydata.org/) als auch [Leaflet](ipyleaflet.ipynb) können mit Datenfragmenten arbeiten, `xarray` durch [Dask Chunks](https://docs.dask.org/en/latest/array-chunks.html) und Leaflet durch ... | github_jupyter |
#### 분산 강화학습을 DQN을 이용하여 구현해보겠습니다. <br>기본적인 방식은 다음과 같습니다. <br>
1. Replay Buffer: Actor로부터 data를 받고, Learner에게 data를 전달하는 역할
2. Parameter Server: Learner로부터 parameter를 받고, Actor에게 paramter를 전달하는 역할.
3. Learner: Replay Buffer로 부터 데이터를 받아 학습을 진행하고, Parameter Server로 Learner 모델의 parameter를 전달하는 역할.
4. Acto... | github_jupyter |
Computes a Bayesian Ridge Regression on a synthetic dataset.
See [Bayesian Ridge Regression](http://scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression) for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient weights are slightly shifted t... | github_jupyter |
```
# Load the data
# Clean the data
# Feature Enginnering
# Preproccessing
# Modelling
# RandomSearching
# GridSearchings
import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn.preprocessing import (
StandardScaler,
RobustScaler,
MinMaxScaler,
MaxA... | github_jupyter |
# Arize Tutorial: SHAP Value For Every Model
Let's get started on using Arize! ✨
Arize helps you visualize your model performance, understand drift & data quality issues, and share insights learned from your models.
**SHAP (SHapley Additive exPlanations)** is a game theoretic approach to explain the output of any ma... | github_jupyter |
```
#test277 BB123789
import cv2
import numpy as np
import matplotlib.pyplot as plt
import skimage
import urllib
import os
from scipy.interpolate import make_interp_spline, BSpline
import matplotlib.gridspec as gridspec
from numpy import percentile
import gc
def extractFrame(mp4DIR,frmID):
vid = cv2.VideoCapture(... | github_jupyter |
# Practice data structures
We will create a data structure to hold our Germplasm data (I have updated it to be a little bit more complex... now a germplasm may hold TWO alleles - i.e. one germplasm has connections to more than one gene)
Represent these data in Python - create a **single variable** that contains all o... | github_jupyter |
# Walkthough of Vamb from the Python interpreter
The Vamb pipeline consist of a series of tasks each which have a dedicated module:
1) Parse fasta file and get TNF of each sequence, as well as sequence length and names (module `parsecontigs`)
2) Parse the BAM files and get abundance estimate for each sequence in the... | github_jupyter |
# 우직한 방식의 확률 계산<br>Brute Force Probability
What if we mobilize computers' massive processing power and memory capacity to compute probabilities by, let's say, generate all possible cases?<br>
컴퓨터의 방대한 처리 능력과 기역 용량을 확률 계산에 사용하기 위해, 이를테면, 모든 경우를 발생시켜본다면 어떨까?
## 주사위 확률 예<br>An example of die roll probability
* 다음 비디오 ... | github_jupyter |
## 7.3 高级功能
在本节中，我们将介绍除直线以外的复杂功能，如:非规则曲线、复杂函数绘图、区域填充、填写标签等等。
### 7.3.1 矩形、圆形、曲线
我们可以通过`\draw (x,y) rectangle (w,h);`的方式绘制一个矩形，其左下角坐标位于点($x$,$y$)处，长度为$w$，高度为$h$。类似地，我们也可以通过`\draw (x,y) circle [radius=r];`的方式绘制一个圆形，其圆心落在点($x$,$y$)处，半径为$r$。除此之外，我们可以通过`\draw (x,y) arc [radius=r, start angle=a1, end angle=a2]`的方式绘制一条弧线，... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.integrate import odeint
plt.style.use('ggplot')
```
# Read example Tarland data
```
# Download Tarland data into a Pandas dataframe
data_url = r'https://raw.githubusercontent.com/JamesSample/enviro_mod_notes/mast... | github_jupyter |
## 今天的範例，帶著大家一起如何找到好特徵
```
# library
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import math
import statistics
import seaborn as sns
from IPython.display import display
import sklearn
print(sklearn.__version__)
#如果只有 0.19 記得要更新至 最新版本
%matplotlib inline
# 特徵選取會用到的函數
f... | github_jupyter |
## Our Mission ##
Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'.
In this mission we will be using the Naive Bayes algorithm to cr... | github_jupyter |
```
import os
from glob import glob
import psutil
import numpy as np
import matplotlib.pyplot as plt
from decode_trf import decode_trf
from mosaiq_field_export import Delivery
config = {
"linac_logfile_data_directory": "S:\\Physics\\Programming\\data\\LinacLogFiles",
"machine_types": {
"elekta-agilit... | github_jupyter |
# EHR Project Extract/Transform/Load
```
# from __future__ import absolute_import, division, print_function, unicode_literals
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow_probability as tfp
# import tensorflow_data_validation as tfdv # blursed library
impor... | github_jupyter |
```
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from model import Stage2Model, FaceModel, SelectNet_resnet, SelectNet
from helper_funcs import affine_crop, stage2_pred_softmax, calc_centroid, affine_mapback
import os
import torch
class ModelEnd2End(nn.Module):
def __init__(self):
... | github_jupyter |
## Questionário 31 (Q31)
Orientações:
- Registre suas respostas no questionário de mesmo nome no SIGAA.
- O tempo de registro das respostas no questionário será de 10 minutos. Portanto, resolva primeiro as questões e depois registre-as.
- Haverá apenas 1 (uma) tentativa de resposta.
- Submeta seu arquivo-fonte (util... | github_jupyter |
# Analysis of the Cicero corpus & comparison to other authors and works
This notebook was used to develop a talk I gave at the Cicero Digitalis Conference on Feb 25, 2021
video here: https://www.youtube.com/watch?v=tJwmXZHZ924
```
import os.path
from collections import Counter
from glob import glob
import inspect
impo... | github_jupyter |
```
%matplotlib inline
import seaborn
import numpy, scipy, matplotlib.pyplot as plt, librosa, IPython.display, urllib
```
# Homework Part 1: Understanding Audio Features through Sonification
*There is no written component to be submitted for this part, Part 1.* This section is intended to acquaint you with Python, th... | github_jupyter |
# Python Vorbereitungen
Dieses Dokument beinhaltet eine Einführung in die für das Praktikum wichtigsten Python-Befehle. Das Dokument wird einige Beispiele enthalten.
Die wichtigesten Befehle werden aber von euch selbst erarbeitet.
## Vorwissen von Pythoneinführung
Ich erwarte, dass Ihr euch (zumindest) die Folien vo... | github_jupyter |
# Know your customer (KYC) - [Lead Scoring]
## Marketing a new product to customers
In this short note we discuss **customer targeting** through **telemarketing phone calls** to sell **long-term deposits**. More specifically, within a campaign, the human agents execute phone calls to a list of clients to sell the dep... | github_jupyter |
<table align="center">
<td align="center"><a target="_blank" href="http://introtodeeplearning.com">
<img src="https://i.ibb.co/Jr88sn2/mit.png" style="padding-bottom:5px;" />
Visit MIT Deep Learning</a></td>
<td align="center"><a target="_blank" href="https://colab.research.google.com/github/aamini/in... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, date
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
!ls ../data/csv/
```
# Load all data at once
```
conditions = pd.read_csv("../data/cs... | github_jupyter |
```
## Name: Chandni Patel
## ID: A20455322
## CS 512 - Fall 2020
## Non-planar Camera Calibration
import numpy as np
import random
import math
import cv2
np.set_printoptions(formatter={'float': "{0:.4f}".format})
```
## Non-Planar Camera Calibration
```
#input point pairs
def GetFilePoint(filename):
point_3... | github_jupyter |
# Analysis and Results Visualization
This script describes the procedure to request an analysis to the Viking Analytics' MultiViz Analytics Engine (MVG) service.
It shows how to query for results of single-asset or asset-population analyses.
In addition, it presents some examples of how to visualize the results availa... | github_jupyter |
```
!pip install -r requeriments.txt
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline
def preprocessing(data, train=True):
# Drop features
data = data.drop(['StartTime', 'SrcAddr', 'Sport', 'DstAddr', 'Dport'], axis=1)
... | github_jupyter |
# Multiclass Support Vector Machine exercise
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course we... | github_jupyter |
# Building your Recurrent Neural Network - Step by Step
Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy.
Recurrent Neural Networks (RNN) are very effective for Natural Language Processing and other sequence tasks because they have "memory". T... | github_jupyter |
```
! nvidia-smi
! cat /proc/cpuinfo
! pip install fastcore --upgrade -qq
! pip install fastai --upgrade -qq
! pip install transformers --upgrade -qq
! pip install datasets --upgrade -qq
! pip install pytorch_lightning --upgrade -qq
! pip install wandb --upgrade -qq
! pip install ohmeow-blurr --upgrade -qq
! pip instal... | github_jupyter |
```
%matplotlib inline
from macrospin import *
from macrospin import crystal, demag, energy, normalize, plot
import numpy as np
from __future__ import division
from mpl_toolkits.mplot3d import Axes3D, proj3d
from matplotlib import rcParams
from matplotlib import pylab as plt
rcParams['font.size'] = 16
```
# Crysta... | github_jupyter |
# Convolutional Neural Networks
In the [previous notebook](./pytorchIntro.ipynb) we have seen how you can train a neural network with pytorch. Next we will learn about the torchvision package and how you can use it to classify images. As our challenge for this notebook, we will use the [Dogs vs. Cats](https://www.kagg... | github_jupyter |
```
import h5py
import scipy.io
import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
matFilename = ('C:\\Users\\mopu\\Machine Learning\\Battery Cycle Life Capacity Prediction\\batteryDischargeData.mat')
f = h5py.File(matFilename,'r')
```
# Dataset
The dataset cont... | github_jupyter |
# CIC Darknet 2020
We will be using the darknet dataset from Canada Institute of Cyber Security. Our goal is to work with the data to categorize darknet traffic.
Steps we will take include:
1. Load data
2. Analyze data
1. Cleaning the data
2. Data Analysis
3. Visualize data
4. Split data into train-test set
5.... | github_jupyter |
## Support Vector Machine
SVM is a type of supervised machine learning classification algorithm
In case of linearly separable data in two dimensions, a typical machine learning algorithm tries to find a line that divides the data in such a way that the misclassification error can be minimized.
For higher dimension ... | github_jupyter |
#### Copyright 2017 Google LLC.
```
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```
# Data Gather
We need lots of data to provide a meaningful prior - at a minimum we need:
[ID, epsilon, epsilon_err, numax, numax_err, dnu, dnu_err, BP_RP, BP_RP_err, Teff, Teff_err]
First we need a set of star catalogues to work from.... | github_jupyter |
*Contenuti*
===
- [La libreria NumPy](#La-libreria-NumPy)
- [Gli array](#Gli-array)
- [Costruzione](#Costruzione)
- [Accesso ai singoli elementi](#Accesso-ai-singoli-elementi)
- [*shape*, *size* e *ndim*](#shape,-size-e-ndim)
- [*Esercizio 1*](#Esercizio-1)
- [Slicing e accesso a singole dim... | github_jupyter |
# Derivation of Expectation and Variance of Power from Thermal Noise
## Preliminaries
For the general likelihood of the 2D power (or even 1D power), one needs to know the contribution to the power (and its uncertainty) from thermal noise. In fact, of course, the thermal noise is added in a non-Gaussian manner (same a... | github_jupyter |
## Introduction
This notebook is part of the workshop "Mathematics of Deep Learning" run
by Aggregate Intellect Inc. ([https://ai.science](https://ai.science)), and is released
under 'Creative Commons Attribution-NonCommercial-ShareAlike CC
BY-NC-SA" license. This material can be altered and distributed for
non-commer... | github_jupyter |
```
%matplotlib inline
from preamble import *
```
# Representing Data and Engineering Features
## Categorical Variables
### One-Hot-Encoding (Dummy variables)
```
import pandas as pd
# The file has no headers naming the columns, so we pass header=None and provide the column names explicitly in "names"
data = pd.rea... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
# Read The Dataset
data = pd.read_csv('data/google_stock.csv', index_col='Date', parse_dates=['Date']).copy()
data.tail()
# Check For NAN Values OR Missing Values
data.isna().a... | github_jupyter |
Now we run this a second time, on the second (`b`) feature table that has removed all epithets with fewer than 27 representative documents. The results are better (overall F1 score for decision tree is `0.44`, random forest is `0.47`; in `a` these were `0.33` and `0.40`, respectively).
```
import os
from sklearn impor... | github_jupyter |
##### Copyright 2018 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 |
# Machine Learning Engineer Nanodegree
## Supervised Learning
## Project 2: Building a Student Intervention System
Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional funct... | github_jupyter |
```
# look at tools/set_up_magics.ipynb
yandex_metrica_allowed = True ; get_ipython().run_cell('# one_liner_str\n\nget_ipython().run_cell_magic(\'javascript\', \'\', \'// setup cpp code highlighting\\nIPython.CodeCell.options_default.highlight_modes["text/x-c++src"] = {\\\'reg\\\':[/^%%cpp/]} ;\')\n\n# creating magics\... | github_jupyter |
# Lesson 3 Demo 2: Focus on Primary Key
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/1200px-Cassandra_logo.svg.png" width="250" height="250">
### In this demo we are going to walk through the basics of creating a table with a good Primary Key in Apache Cassandra, inserting ro... | github_jupyter |
```
# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
#
# 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 applica... | github_jupyter |
```
import pickle
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
unpickle = lambda filename: pickle.Unpickler(open(filename, 'rb'), encoding = 'latin1').load()
data_b... | github_jupyter |
# Pybind11 (partial) code generator
This is an attempt for a simple code generator to generate python binding for C++ libraries using [Pybind11](https://pybind11.readthedocs.io/en/stable/).
It is completely based on the ideas and code presented in the excelent article [implementing a code generator with libclang](htt... | github_jupyter |
# Video using the Base Overlay
The PYNQ-Z1 board contains a HDMI input port, and a HDMI output port connected to the FPGA fabric of the Zynq® chip. This means to use the HDMI ports, HDMI controllers must be included in a hardware library or overlay.
The base overlay contains a HDMI input controller, and a HDMI Outpu... | github_jupyter |
# Wissenschaftliches Python Tutorial
Nachdem wir uns im Python Tutorial um die Grundlagen gekümmert haben, wollen wir uns nun mit einigen Bibliotheken beschäftigen, die das wissenschaftliche Arbeiten erleichtern. Diese sind
* [Numpy](http://www.numpy.org/) für effiziente Berechnungen auf strukturierten Daten
* [Matp... | github_jupyter |
### Компиляция и линковка.
<br />
##### Hello world одним файлом: компиляция и линковка
Напишем программу `hello_world.cpp` одним файлом:
```c++
#include <cstdio>
void print_hello_world()
{
std::puts("hello world!");
}
int main()
{
print_hello_world();
return 0;
}
```
Сборка С++ - программ делится на... | github_jupyter |
<a href="https://colab.research.google.com/github/daveluo/opencitiesaichallenge-stac/blob/master/challengestac_browser_modify.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install pystac
from pystac import (Catalog, CatalogType, Item, Ass... | github_jupyter |
<a href="https://colab.research.google.com/github/Nburkhal/DS-Unit-2-Kaggle-Challenge/blob/master/assignment_kaggle_challenge_4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science, Unit 2: Predictive Modeling
# Kaggle Challen... | github_jupyter |
```
import numpy as np
import pandas as pd
import tensorflow as tf
from transformers import *
import tokenizers
import tensorflow.keras.backend as K
from sklearn.model_selection import StratifiedKFold
MAX_LEN = 192
PATH = '../input/tf-roberta/'
tokenizer = tokenizers.ByteLevelBPETokenizer(
vocab_file=PATH+'vocab-... | github_jupyter |
# Nonuniform sensitivity
## Background
Not all pixels in a camera have the same sensitivity to light: there are
intrinsic differences from pixel-to-pixel. Vignetting, a dimming near the
corners of an image caused by the optical system to which the camera is
attached, and dust on optical elements such as filters, the ... | github_jupyter |
# SST in Bavi, Mayas, Haishen
Authors
* [Dr Chelle Gentemann](mailto:gentemann@faralloninstitute.org) - Farallon Institute, USA
## In Feb 2020 a GRL [paper](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091430) came out connecting 3 closely occuring Typhoons near Korea to the California wildfires
... | github_jupyter |
# Tutorial: Conceptos básicos de Tensorflow
En este tutorial veremos algunos conceptos importantes para poder comenzar a utilizar tensorflow para tareas de deep learning.
## Tensores
Un **tensor** es un arreglo multidimensional con elementos del mismo tipo (dtype). En escencia, un tensor de tensorflow es muy similar e... | github_jupyter |
```
# default_exp callback.core
```
# Callback
> Miscellaneous callbacks for timeseriesAI.
```
#export
from tsai.imports import *
from tsai.utils import *
from tsai.data.preprocessing import *
from tsai.data.transforms import *
from tsai.models.layers import *
from fastai.callback.all import *
#export
import torch.... | github_jupyter |
### 背景
来源百度百科：
#### 【什么是滑脱】
腰椎滑脱 是由于先天性发育不良、创伤、劳损等原因造成相邻椎体骨性连接异常而发生的上位椎体与下位椎体部分或全部滑移，表现为腰骶部疼痛、坐骨神经受累、间歇性跛行等症状的疾病。
在所有的腰椎滑脱中，由峡部崩裂引起的滑脱约占15%，退行性腰椎滑脱约占35%。在我国腰椎滑脱的发病年龄多在20~50岁，占85%；男性明显多于女性，男女之比为 29：1。腰椎滑脱最常见的部位是 L4~L5 及 L5~S1，其中腰5椎体发生率为82~90% 。滑脱的椎体可引起或加重椎管狭窄，刺激或挤压神经，引起腰痛、下肢痛、下肢麻木、甚至大小便功能障碍等症状。另外，滑脱后腰背肌的保护性收缩可引起腰背肌劳损... | github_jupyter |
# Named Entity Recognition in Mandarin on the MSRA/SIGHAN2006 Dataset
---
[Github](https://github.com/eugenesiow/practical-ml/blob/master/notebooks/Named_Entity_Recognition_Mandarin_MSRA.ipynb) | More Notebooks @ [eugenesiow/practical-ml](https://github.com/eugenesiow/practical-ml)
---
Notebook to train/fine-... | github_jupyter |
<a href="https://colab.research.google.com/github/nephylum/DS-Unit-2-Linear-Models/blob/master/module3-ridge-regression/DS9_assignment_regression_classification_3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, S... | github_jupyter |
```
import mxnet as mx
from mxnet import ndarray as nd
from easydict import EasyDict as edict
import numpy as np
import os
from tqdm import tqdm
import skimage.io as io
import tensorflow as tf
import tensorflow.contrib.slim as slim
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
sess = tf.Session(con... | github_jupyter |
# 2.1 Sampling
[Preparation](#Preparation)
Constants
Functions
[2018-03-23 - 2018-04-27 Online campaign - 1.52.2](#2018-03-23---2018-04-27-Online-campaign---1.52.2)
[2018-04-27 - 2018-07-05 Online campaign - 1.60](#2018-04-27---2018-07-05-Online-campaign---1.60)
[2018-04-10 - 2018-04-28 Playtest - 1.52.2 & 1.60](... | github_jupyter |
# RidgeClassifier with MinMaxScaler & Power Transformer
This Code template is for the Classification tasks using RidgeClassifier with MinMaxScaler feature scaling technique and PowerTransformer as Feature Transformation Technique in a pipeline.
### Required Packages
```
!pip install imblearn --q
import warnings
i... | github_jupyter |
$ \newcommand{\ket}[1]{\left|{#1}\right\rangle}
\newcommand{\bra}[1]{\left\langle{#1}\right|} $
$\newcommand{\au}{\hat{a}^\dagger}$
$\newcommand{\ad}{\hat{a}}$
$\newcommand{\bu}{\hat{b}^\dagger}$
$\newcommand{\bd}{\hat{b}}$
# Cat state encoding
The main goal is to find control pulses which will realise the state transf... | github_jupyter |
# Tracklist Generator: Data Preparation
This notebook contains the code for the data processing of the 1001Tracklists dataset. We will take tracklist data and dictionaries containing co-occurrence information for songs and artists, and produce filtered sparse matrices to be used in recommendation models. We will also u... | github_jupyter |
```
from scapy.all import *
from pprint import pprint
import sys
import numpy as np
import os
import dpkt
import matplotlib.pyplot as plt
KEYLEN=8
UNCOMPRESSED_PKT_SIZE = 1000
COMPRESSED_PKT_SIZE = 977.6
MAX_LINE_RATE =10e9
def read_pcap_with_pkt(out_dir, dst_mac_is_ts = True, try_compare_counters = True):
i... | github_jupyter |
# `git`, `GitHub`, `GitKraken` (continuación)
<img style="float: left; margin: 15px 15px 15px 15px;" src="http://conociendogithub.readthedocs.io/en/latest/_images/Git.png" width="180" height="50" />
<img style="float: left; margin: 15px 15px 15px 15px;" src="https://c1.staticflickr.com/3/2238/13158675193_2892abac95_z.... | github_jupyter |
```
#%pip install -I git+https://github.com/qiskit-community/may4_challenge.git@0.4.30
#packages
from qiskit import QuantumCircuit, Aer, execute
from may4_challenge.ex4 import get_unitary,check_circuit, submit_circuit
import numpy as np
from IPython.core.display import display, HTML
from qiskit.visualization import *
d... | github_jupyter |
```
from IPython.display import HTML
# Cell visibility - COMPLETE:
tag = HTML('''<style>
div.input {
display:none;
}
</style>''')
display(tag)
# #Cell visibility - TOGGLE:
# tag = HTML('''<script>
# code_show=true;
# function code_toggle() {
# if (code_show){
# $('div.input').hide()
# } else {
# ... | github_jupyter |
# Interface
Java is a typed language, even if you don't explicitly write a type
the compiler you compute the type of every variables
Once you start to want to mix several records, you need to declare
common type between records, such type are known as interface
## The problem
let say we have a Square and Rectangle, an... | github_jupyter |
## Quantium Task1
#### By Samuel Waweru, Mechatronic Engineering Undergraduate 2025.
Analysis of Customer Data and data visualization.
Quantium’s retail analytics team :Chips and their purchasing behaviour within the region.
```
#importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as ... | github_jupyter |
```
import numpy as np
from bqplot import *
np.random.seed(0)
size = 100
x_data = range(size)
y_data = np.cumsum(np.random.randn(size) * 100.0)
y_data_2 = np.cumsum(np.random.randn(size))
```
## Miscellaneous Properties
```
y_sc = LinearScale()
ax_x = Axis(label='Test X', scale=y_sc, grid_lines='solid')
ax_y = Axis(... | github_jupyter |
# States for MDP
States from paper
http://www.ijmlc.org/vol5/515-C003.pdf
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import cluster
from sklearn.svm import SVC
from sklearn.metrics import roc_auc_score, roc_curve
... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Speed-Detection" data-toc-modified-id="Speed-Detection-1"><span class="toc-item-num">1 </span>Speed Detection</a></div><div class="lev2 toc-item"><a href="#Algorithm" data-toc-modified-id="Algorithm-11"><span class="toc-item-num">1.1 </s... | github_jupyter |
```
# -- Tensorflow -- #
import tensorflow as tf
from tensorflow.keras.layers import (
Softmax,
Dense,
AdditiveAttention,
MultiHeadAttention,
Layer,
LayerNormalization,
Dropout,
Embedding
)
from tensorflow.keras import (
Sequential,
Model
)
```
# Transformer Pipeline
```
@dat... | github_jupyter |
<h1> Epitope Prediction </h1>
This tutorial illustrates the use of epytope to predict HLA-I/II epitopes and how to analyze results. epytope offers a long list of epitope prediction methods and was designed in such a way that extending epytope with your favorite method is easy.
This tutorial will entail:
- Simple epit... | github_jupyter |
```
import pandas as pd
import numpy as np
import re
import os
import utils
import string
pd.options.display.max_columns = 100
pd.options.display.max_rows = 1000
data_dir = "data/"
files = ["H-1B_Disclosure_Data_FY16.xlsx",
"H-1B_Disclosure_Data_FY15_Q4.xlsx",
"H-1B_FY14_Q4.xlsx",
... | github_jupyter |
# Basic training functionality
```
from fastai.basic_train import *
from fastai.gen_doc.nbdoc import *
from fastai import *
from fastai.vision import *
```
[`basic_train`](/basic_train.html#basic_train) wraps together the data (in a [`DataBunch`](/basic_data.html#DataBunch) object) with a pytorch model to define a [`... | github_jupyter |
# TensorFlow script mode training and serving
Script mode is a training script format for TensorFlow that lets you execute any TensorFlow training script in SageMaker with minimal modification. The [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk) handles transferring your script to a SageMaker train... | github_jupyter |
# The Correlation Coefficient
The correlation coefficient measures the extent to which the relationship between two variables is linear. Its value is always between -1 and 1. A positive coefficient indicates that the variables are directly related, i.e. when one increases the other one also increases. A negative coeff... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import random as rand
from matplotlib.animation import FuncAnimation
from IPython import display
N=int(input("Enter number of steps: "))
line_width=input("Enter line width for the plot(Use smaller number for larger steps): ")
if N>=1000:
case=input("Type 'yes' fo... | github_jupyter |
```
!git clone https://github.com/twintproject/twint.git
%cd twint
!pip3 install . -r requirements.txt
%cd twint
import twint
import os
tweets_file_path = "./tweet"
def export_tweets(username):
if os.path.isfile(tweets_file_path):
return
c = twint.Config()
c.Username = username
c.Store_csv =... | github_jupyter |
# AUTOMATIC TEXT SUMMARIZATION
# Some Use Cases
## Extraction based Text Summarization using NLTK
#Source: Stackabuse.com (Usman Malik)
```
!pip install lxml
import nltk
... | github_jupyter |
# Reusable Embeddings
**Learning Objectives**
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
## Introduction
In this notebook, we will implement text models to recognize the probable source (Github, Tech-... | github_jupyter |
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