text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
<a href="https://colab.research.google.com/github/NicoEssi/Machine_Learning_scikit-learn/blob/master/KNN_Classification_Demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# K-Nearest Neighbors (K-NN) - Demo
---
One of the most popular machine l... | github_jupyter |
```
import datetime
lipidname = "OPPE"
tail = "CCCC CDCC"
link = "G G"
head = "C E"
description = "; A general model Phosphosatidylethanolamine (PE) lipid \n; C18:1(9c) oleic acid, and C18:0 palmitic acid\n"
modeledOn="; This topology follows the standard Martini 2.0 lipid definitions and building block rule... | github_jupyter |
# Problem 2: Logistic Regression and LDA
You are hired by a tour and travel agency which deals in selling holiday packages. You are provided details of 872 employees of a company. Among these employees, some opted for the package and some didn't. You have to help the company in predicting whether an employee will opt ... | github_jupyter |
```
from keras.preprocessing import sequence
from keras.models import Sequential, Model, load_model, model_from_yaml
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.datasets import imdb
import tensorflow as tf
from ker... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Ma... | github_jupyter |
# Importing libraries
```
import pandas as pd
import tensorflow as tf
import cv2
from google.colab import drive
drive.mount('/content/drive')
```
# Getting images and masks
There are 60 images with 4 classes.
Masks have $0$ in pixels with background and Label $[1,2,3,4]$ in their channels depending on class.
```
i... | github_jupyter |
# Tutorial: Adding a new image-classification model
```
import os
import distiller
import torch.nn as nn
from distiller.models import register_user_model
import distiller.apputils.image_classifier as classifier
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d... | github_jupyter |
<table width="100%">
<tr style="border-bottom:solid 2pt #009EE3">
<td style="text-align:left" width="10%">
<a href="biosignalsnotebooks.dwipynb" download><img src="../../images/icons/download.png"></a>
</td>
<td style="text-align:left" width="10%">
<a href="https://my... | github_jupyter |
<a href="https://colab.research.google.com/github/anitamezzetti/ML_finance/blob/main/generate_dataset_multiple%20maturities.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import os
from time import time
import matplotlib.pypl... | github_jupyter |
# Image features 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 website.*
We have see... | github_jupyter |
# Riskfolio-Lib Tutorial:
<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__
<br>__[Orenji](https://www.orenj-i.net)__
<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__
<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__
<a href='https://ko-fi.com/B0B833SXD' target='... | github_jupyter |
___
<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
___
# Linear Regression with Python
** This is mostly just code for reference. Please watch the video lecture for more info behind all of this code.**
Your neighbor is a real estate agent and wants some help predicting housing price... | github_jupyter |
# Python Tutorial
Eine minimale Einführung in Python für Studierende mit Programmiererfahrung die keinen Anspruch auf Vollständigkeit erhebt.
Ausführlichere Einführungen und Tutorials finden sich an zahlreichen Stellen im Internet. Beispielsweise
* [Learn Python the Hard Way](http://learnpythonthehardway.org/)
* [S... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "-1"
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.auto import tqdm
import torch
from torch import nn
import gin
import pickle
import io
from sparse_causal_model_learner_rl.trainable.gumbel_switch import Wit... | github_jupyter |
# import library
```
import pandas as pd
import numpy as np
import pickle
from sklearn import preprocessing
from imblearn.under_sampling import NearMiss
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier, AdaBoos... | github_jupyter |
# Прогнозирование временного ряда выработки электроэнергии
Имеются два временных ряда: первый — это среднесуточная выработка электроэнергии ветряной установкой.
Другой — среднесуточная выработка электроэнергии при помощи дизельного генератора. Оба показателя измеряются в кВт⋅ч.
Помимо данных двух временных рядов име... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Time-Series-Models" data-toc-modified-id="Time-Series-Models-1"><span class="toc-item-num">1 </span>Time Series Models</a></span><ul class="toc-item"><li><span><a href="#Chicago-Gun-Data" data-to... | github_jupyter |
This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, fig... | github_jupyter |
# DMI Deaths Classification
```
import pandas as pd
import numpy as np
from time import time
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import BernoulliNB
from sklearn.svm import SVC
from sklearn.decomposition import Truncat... | github_jupyter |
<a href="https://colab.research.google.com/github/kenextra/IBM-MLCert/blob/main/Webservice_Deploy_Diagram.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%%bash
pip install diagrams --quiet
%%bash
pip show diagrams
from diagrams import Cluster, ... | github_jupyter |
# Computational and Numerical Methods
## Group 16
### Set 6 (10-09-2018): Lagrange and Newton Interpolation
#### Vidhin Parmar 201601003
#### Parth Shah 201601086
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
from scipy.interpolate import lagrange as... | github_jupyter |
<a href="https://colab.research.google.com/github/whitejetyeh/RBMrecommend/blob/master/RBMrecommend(July_19).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# RBMrecommend
Restrictive Boltzmann Machine (RBM) is an unsupervised learning, which autom... | github_jupyter |
<a href="https://www.peqnp.com"><img border="0" alt="PEQNP" src="https://raw.githubusercontent.com/maxtuno/SATX/main/SAT-X.jpg" width="640" height="400">
# SAT-X
## [](https://pepy.tech/project/satx)
### The constraint modeling language for SAT solvers
SAT-X is a language f... | github_jupyter |
# Create dataset with pairs of anti-glioblastoma drugs - nanoparticles
```
import pandas as pd
import numpy as np
import feather
```
## Modify datasets
Read initial data for drugs:
```
df_d = pd.read_csv('./datasets/drug(neuro).csv')
df_d.shape
# remove duplicates in drugs data
print('Before:', df_d.shape)
df_d.dro... | github_jupyter |
# McsPyDataTools Tutorial for files from MCS Headstages with IMU<a id='Top'></a>
This tutorial shows the handling of IMU data collected from an MCS Headstage wearing an *Inertial Measurement Unit*
- <a href='#Gyrosocope Data'>Gyroscope Data</a>
- <a href='#Accelerometer Data'>Accelerometer Data</a>
- <a href='#6DoF-E... | github_jupyter |
# Lists, Tuples and Dictionaries
# Creating a list
- A list is a data structure in Python
- It is a sequence of elements
- A list is mutable, or changeable
The markers of a list are ``[]``
```
# create an empty list
empty_list = []
# create a list of strings
summer_months = ["June", "July", "August"]
# create a m... | github_jupyter |
```
%cd ../..
import ROOT
import numpy as np
import matplotlib.pyplot as plt
from melp import Detector
mu3e_detector = Detector.initFromROOT("run42_20000.root")
file = ROOT.TFile("run42_20000_sorted_test.root")
ttree_mu3e = file.Get("mu3e")
ttree_mu3e_mc = file.Get("mu3e_mchits")
ttree_mu3e.GetEntry(1199)
# 1199 : 36, ... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
#export
from nb_003 import *
import nb_002
import operator
from random import sample
from torch.utils.data.sampler import Sampler
DATA_PATH = Path('data')
PATH = DATA_PATH/'caltech101' # http://www.vision.caltech.edu/Image_Datasets/Caltech101/
```
# Caltech 101
## Create vali... | github_jupyter |
<h1>Pandas Playbook</h1>
This Notebook lets you develop some intuition of the key features of *pandas* in a *query-like* way, with a minimum of python syntax:
* Basic properties and functions
* Frequent used operations: slicing, selection, insertion, deletion and aggregation
*pandas* is a library for data explora... | github_jupyter |
```
# Importing All necessary modules for model_training
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
# fetching data set from keras
(X_train_full,Y_train_full),(X_test,Y_test) = keras.datasets.mnist.load_data()
# Showing the shapes of tabl... | github_jupyter |
```
# !wget https://huseinhouse-storage.s3-ap-southeast-1.amazonaws.com/bert-bahasa/singlish.txt
with open('singlish.txt') as fopen:
singlish = fopen.read().split('\n')
len(singlish)
import re
from tqdm import tqdm
import cleaning
def preprocessing(string):
string = re.sub(
'http\S+|www.\S+',
... | 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=SocialStudies/Globalization/communicati... | github_jupyter |
# Sparse Ordinary Autoencoder
From an example on the [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html), but with sparsity added.
```
from keras.layers import Input, Dense
from keras.models import Model
from keras import regularizers
from keras.regularizers import Regularizer
from keras import ba... | github_jupyter |
# Project 1: Trading with Momentum
## Instructions
Each problem consists of a function to implement and instructions on how to implement the function. The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the unit test... | github_jupyter |
<h2> Shrink datasets down to mz, try to classify them, then try to combine features </h2>
You'll have to... mean center and standardize all your features?
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing
from IPython.display import di... | github_jupyter |
# Data For this notebook:
**combined_sig_df.pkl**
https://storage.googleapis.com/issue_label_bot/notebook_files/combined_sig_df.pkl
**feat_df.csv** https://storage.googleapis.com/issue_label_bot/notebook_files/feat_df.csv
```
import pandas as pd
from inference import InferenceWrapper, pass_through
from tensorflow.ke... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, transforms
from time import time
USE_CUDA = torch.cuda.is_available()
USE_CUDA
de... | github_jupyter |
# Aggregations
Aggregations are an important step while processing dataframes and tabular data
in general. And therefore, they should be as simple as possible to implement.
Some notable data aggregation semantics are provided by pandas, spark and the SQL
language.
When designing an aggregation API method, the followi... | github_jupyter |
```
import sys
import time
from contextlib import contextmanager
import lightgbm as lgb
sys.path = ['../'] + sys.path
from src.parser import *
from src.train import *
from src.util import TimeSeriesSplit
from src.event_level_model import prep_events
from src.dataset_helper import *
@contextmanager
def timer(name):
... | github_jupyter |
# FINN - End-to-End Flow
-----------------------------------------------------------------
In this notebook, we will show how to take a simple, binarized, fully-connected network trained on the MNIST data set and take it all the way down to a customized bitfile running on a PYNQ board.
This notebook is quite lengthy... | github_jupyter |
To open this notebook in Google Colab and start coding, click on the Colab icon below.
<table style="border:2px solid orange" align="left">
<td style="border:2px solid orange ">
<a target="_blank" href="https://colab.research.google.com/github/neuefische/ds-meetups/blob/main/01_Python_Workshop_Revisiting_Some_Fu... | github_jupyter |
# API calls: Twitter
This notebook introduces a simple yet powerful package developed by University of Munich researchers and called **TwitterSearch**. One can easily install it by opening the command prompt and running the following command:
```
pip install twittersearch
```
The package provides the opportunity to ea... | github_jupyter |
# SP LIME
## Regression explainer with boston housing prices dataset
```
from sklearn.datasets import load_boston
import sklearn.ensemble
import sklearn.linear_model
import sklearn.model_selection
import numpy as np
from sklearn.metrics import r2_score
np.random.seed(1)
#load example dataset
boston = load_boston()
... | github_jupyter |
<a href="https://colab.research.google.com/github/timeseriesAI/timeseriesAI/blob/master/tutorial_nbs/00b_How_to_use_numpy_arrays_in_fastai2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
created by Ignacio Oguiza - email: oguiza@gmail.com
## How t... | github_jupyter |
If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.
```
#! pip install datasets transformers seqeval
```
If you're opening this notebook locally, make sure your environment has an install from the last version of those l... | github_jupyter |
# Decison Trees
First we'll load some fake data on past hires I made up. Note how we use pandas to convert a csv file into a DataFrame:
```
import numpy as np
import pandas as pd
from sklearn import tree
input_file = "PastHires.csv"
df = pd.read_csv(input_file, header = 0)
df.head()
```
scikit-learn needs everythin... | github_jupyter |
# JAX Quickstart
[](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/quickstart.ipynb)
**JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research.**
... | github_jupyter |
In this notebook we will look how we can use Cython to generate a faster callback and hopefully shave off some running time from our integration.
```
import json
import numpy as np
from scipy2017codegen.odesys import ODEsys
from scipy2017codegen.chem import mk_rsys
```
The `ODEsys` class and convenience functions fro... | github_jupyter |
Thus far we have seen the following data types:
* integer (counting number) - `int`.
* floating point number (number with decimal point) - `float`.
* string (text) - `str`.
* boolean (True or False value) - `bool`.
In data analysis, we often want to collect together several numbers, or
strings, into a *sequence*. Th... | github_jupyter |
```
%matplotlib inline
# Assuming we are in the notebooks directory, we need to move one up:
%cd ../..
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
sns.set(font_scale=1.8)
sns.set_style('ticks')
df = pd.read_csv('./data/participa... | github_jupyter |
<div style="background:#F5F7FA; height:100px; padding: 2em; font-size:14px;">
<span style="font-size:18px;color:#152935;">Want to do more?</span><span style="border: 1px solid #3d70b2;padding: 15px;float:right;margin-right:40px; color:#3d70b2; "><a href="https://ibm.co/wsnotebooks" target="_blank" style="color: #3d70b2... | github_jupyter |
# <div align="center">Dealing with Imbalanced Data</div>
---------------------------------------------------------------------
you can Find me on Github:
> ###### [ GitHub](https://github.com/lev1khachatryan)
<img src="asset/main.jpg" />
Imbalanced classes are a common problem in machine learning classification wher... | github_jupyter |
# Electrode State of Health
This notebook demonstrates some utilities to work with electrode State of Health (also sometimes called electrode stoichiometry), using the algorithm from Mohtat et al [1]
[1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specif... | github_jupyter |
## Paramnormal Activity
Perhaps the most convenient way to access the functionality of `paramnormal` is through the `activity` module.
Random number generation, distribution fitting, and basic plotting are exposed through `activity`.
```
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
from numpy.... | github_jupyter |
# Model Training
In this notebook, we'll train a LightGBM model using Amazon SageMaker, so
we have an example trained model to explain.
You can bring also bring your own trained models to explain. See the
customizing section for more details.
<p align="center">
<img src="https://github.com/awslabs/sagemaker-explai... | github_jupyter |
# Word2Vec SAT
Load the SAT analogies data and the pruned Word2Vec model (based on GoogleNews-vectors-negative300.bin.gz from https://code.google.com/archive/p/word2vec/), but with only the words that appear in the analogies saved.
```
import numpy
import json
from scipy.spatial.distance import cosine
# Load the mode... | github_jupyter |
# **ESRGAN with TF-GAN on TPU**
### **Overview**
This notebook demonstrates the E2E process of data loading, preprocessing, training and evaluation of the [ESRGAN](https://arxiv.org/abs/1809.00219) model using TF-GAN on TPUs. To understand the basics of TF-GAN and explore more features of the library, please visit [TF... | github_jupyter |
# TITANIC SURVIVAL PREDICTION
<p align="center">
<img width="600" height="325" src="https://www.gifcen.com/wp-content/uploads/2021/09/titanic-gif-1.gif">
</p>
RMS Titanic was a British passenger liner operated by the White Star Line that sank in the North Atlantic Ocean on 15 April 1912, after striking an iceberg du... | github_jupyter |
# Boxfill Tutorial
This is an in-depth introduction to the boxfill graphics method in VCS. It breaks down each of the important attributes of the graphics method, how to use them, and what effects they have on your plots.
## Setup
This is just the normal setup for a plot in VCS; we get the data, retrieve a variable,... | github_jupyter |
```
%run Import_Library.ipynb
def avg_digits_text(data):
"""
Purpose:
- Count the number of digits in each doc in data
- Compute the average
Arg:
data: array of text
Return:
an average of number of digits in data
"""
avg = []
num_re = re.compi... | github_jupyter |
# Example Model Servers with Seldon
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](seldon_core_setup.ipynb#Setup-Cluster) with [Ambassador Ingress](seldon_core_setup.ipynb#Ambassador) and [Seldon Core](seldon_core_setup.ipynb#Install-Seldon-Core). Instructions [also online](./seldon_core_setup.html).
... | github_jupyter |
<a href="https://colab.research.google.com/github/wesleybeckner/python_foundations/blob/main/notebooks/solutions/SOLN_S3_Functions_and_Debugging.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Python Foundations, Session 3: Functions and Debugging... | github_jupyter |
# Interactive question answering with OpenVINO
This demo shows interactive question answering with OpenVINO. We use [small BERT-large-like model](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/bert-small-uncased-whole-word-masking-squad-int8-0002) distilled and quantized to INT8 on SQuAD v1... | github_jupyter |
# What are `Periodogram` objects?
*Lightkurve* has a class specifically for dealing with periodograms of time series data. This can be useful for finding the periods of variable stars. Below is a quick example of how to find the period of an [eclipsing binary star](https://en.wikipedia.org/wiki/Binary_star#Eclipsing_b... | github_jupyter |
# Métricas
Una métrica es una función que define una distancia entre cada par de elementos de un conjunto. Para nuetro caso, se define una función de distancia entre los valores reales ($y$) y los valores predichos ($\hat{y}$).
Defeniremos algunas métricas bajo dos tipos de contexto: modelos de regresión y modelos de... | github_jupyter |
```
%matplotlib inline
import datetime
import matplotlib
import matplotlib.pyplot as plt
import tokio
import tokio.tools
import tokio.config
matplotlib.rcParams.update({'font.size': 14})
```
## Define input parameters
To generate a Lustre activity heat map, you must define the start time, end time, and file system of... | 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 |
# 解析解
对于多元市场的分布，论文中使用的还是Monte Carlo Simulation的方法
```
import numpy as np
import scipy as sp
N = 2
Mu = np.zeros((N, 1))
r = .6
Sigma = (1-r) * np.eye(N) + r * np.ones((N,N))
J = int(1e6)
p = np.ones((N,1)) / J
dd = np.random.multivariate_normal(Mu.reshape(N), Sigma, size=int(J/2))
X = np.ones((J,1)) * Mu.T + np.con... | github_jupyter |
# Sentiment / Customer Intention Analysis
Design & map SQL Alchemy tables with relational database's tables
## 1/ Import libraries
```
import pandas as pd
import numpy as np
import csv
from sqlalchemy import Column, String, Integer, ForeignKey, DateTime, func, Boolean, MetaData, Table, Float
from sqlalchemy.dialect... | github_jupyter |
# 1.1 chABSA-datasetから訓練、テストデータを作成
- 本ファイルでは、chABSA-datasetのデータを使用して、感情分析（0：ネガティブ、1：ポジティブ）を2値クラス分類するためのデータファイル（tsv)を作成します。
- 下記サイトからchABSA-dataset.zipをダウンロードして解凍します。
https://github.com/chakki-works/chABSA-dataset
データファイルは230個、文章データは2813個あります。
```
import pandas as pd
from pathlib import Path
import json
d... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn import preprocessing
from sklearn.linear_model import Log... | github_jupyter |
<font size="6" color="#0047b2" face="verdana"> <B>Notebook 03: Conditional and Booleans</B></font>
------------------------------------
You are invited to your friend's birthday party and you want to bring a cake. **If you have 3 or more eggs** you will bake a cake, **otherwise** you will get a cake from the shop.
W... | github_jupyter |
Welcome to the exercises for day 3 (these accompany the day 3 tutorial notebook on booleans and conditionals, available [here](https://www.kaggle.com/colinmorris/learn-python-challenge-day-3))
As always **be sure to run the setup code below** before working on the questions (and if you leave this notebook and come bac... | github_jupyter |
```
#we may need some code in the ../python directory and/or matplotlib styles
import sys
import os
sys.path.append('../python/')
#set up matplotlib
os.environ['MPLCONFIGDIR'] = '../mplstyles'
print(os.environ['MPLCONFIGDIR'])
import matplotlib as mpl
from matplotlib import pyplot as plt
#got smarter about the mpl con... | github_jupyter |
# Activity Analysis Demonstration script
This notebook draws from the demo 1 file of ActivityAnalysisToolbox_2.1
This notebook demonstrates the use of the activity analysis python library, built on pandas, scipy, numpy, and math libraries.
The purpose and origins of activity analysis is explained in:
Upham, F.,... | github_jupyter |
# Basic Tutorial for <font color='green'> Toad</font>
Toad is developed to facilitate the model development of credit risk scorecard particularly. In this tutorial, the basic use of toad will be introduced.
______
______
The tutorial will follow the common procedure of credit risk scorecard model development:
... | github_jupyter |
# Kaggle San Francisco Crime Classification
## Berkeley MIDS W207 Final Project: Sam Goodgame, Sarah Cha, Kalvin Kao, Bryan Moore
### Basic Modeling
### Environment and Data
```
# Import relevant libraries:
import time
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from skle... | github_jupyter |
# Postprocess data
## Anomalies
In ENSO research one most often wants to work with anomaly data. Hence, data where the long time seasonality its removed. Furthermore, it is advisable for regrid all data to a common grid. Here the grid is the 2.5° x 2.5° grid from the NCAR/NCEP reananalysis.
If the data was already p... | github_jupyter |
# [Module 1.3] 체크 포인트를 생성을 통한 스팟 인스턴스 훈련
### 본 워크샵의 모든 노트북은 `conda_python3` 여기에서 작업 합니다.
이 노트북은 아래와 같은 작업을 합니다.
- 체크포인트를 사용하는 방법
- 기본 환경 세팅
- 데이터 세트를 S3에 업로드
- 체크 포인트를 사용한 훈련 시니라오
- 첫 번째 훈련 잡 실행
- 두 번째 훈련 잡 실행
- 훈련 잡 로그 분석
- 모델 아티펙트 저장
---
## 세이지 메이커에서 체크포인트를 사용하는 방법
개발자 가이드 --> [체코 포인트 사용하기](https://docs.... | 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 |
<a href="https://colab.research.google.com/github/gowrithampi/deeplearning_with_pytorch/blob/main/Chapter_5_The_mechanics_of_learning.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deep Learning with Pytorch
## Chapter 5: The Mechanics of Learnin... | github_jupyter |
# Kinesis Data Analytics for SQL Applications
With Amazon Kinesis Data Analytics for SQL Applications, you can process and analyze streaming data using standard SQL. The service enables you to quickly author and run powerful SQL code against streaming sources to perform time series analytics, feed real-time dashboards... | github_jupyter |
# Illustration for custom layer: Softmax_cosine_sim
```
import random
import numpy as np
from random import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.activations import softmax
class SoftmaxCosineSim(keras.layers.Layer):
# =================================================... | github_jupyter |
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/06_MNIST_Estimator_to_TPUEstimator.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## MNIST Estimator to TPUEs... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%aimport fluidsim
from paths import load_df, paths_sim
```
# Compose a function to calculate the shock population of one run
```
from peak_detection import (
detect_shocks,
avg_shock_seperation_1d,
avg_shock_seperation,
avg_shock_seperation_from_shortname
)
df_w ... | github_jupyter |
# setup dataset
```
# import stuff
import os
import numpy as np
import time
import pandas as pd
import torch
import torch.utils.data as data
from itertools import product as product
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Function
fro... | github_jupyter |
# Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function to take an SArray and a degree and retur... | github_jupyter |
# Examples: Variables and Data Types
## The Interpreter
```
# The interpreter can be used as a calculator, and can also echo or concatenate strings.
3 + 3
3 * 3
3 ** 3
3 / 2 # classic division - output is a floating point number
# Use quotes around strings
'dogs'
# + operator can be used to concatenate strings
'do... | github_jupyter |
# 解析学A 2018
黒木玄
2018年7月30日(月)
次のリンク先で綺麗に閲覧できる:
* http://nbviewer.jupyter.org/github/genkuroki/Calculus/blob/master/Analysis%20A%202018.ipynb
$
\newcommand\eps{\varepsilon}
\newcommand\ds{\displaystyle}
\newcommand\Z{{\mathbb Z}}
\newcommand\R{{\mathbb R}}
\newcommand\C{{\mathbb C}}
\newcommand\QED{\text{□}}
\newco... | github_jupyter |
https://colab.research.google.com/drive/1TlIfecAt51WsskxSknGm_WOMbA6h9tF4
```
!wget https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz
!ls
!tar xzvf UrbanSound8K.tar.gz
!pwd
import pandas as pd
data = pd.read_csv('/content/UrbanSound8K/metadata/UrbanSound8K.csv') # Refer to the location of downloaded file her... | github_jupyter |
```
'''
This notebook filters mapped nanoCOP data to remove:
polyadenylated transcripts,
7SK transcripts,
non-unique reads,
and splicing intermeditates
Data generated in Drexler et al. 2019 (GEO accession: GSE123191)
'''
import os
import sys
import re
import glob
import pysam
import pybedtools... | github_jupyter |
# 中文三元组联合抽取
## 介绍
在这个notebook中我们将使用openue库代码来训练我们自己的三元组联合抽取，使用的基础模型是`bert-base-chinese`，训练分为两步，首先训练关系分类模型，其次训练实体抽取模型。之后联合验证。
## 数据集
在这个数据集中，使用ske数据集，具体例子如下。我们使用代码来读取`train.json`来分析一下数据。
```
import json
with open("../dataset/ske/train.json", "r") as file:
for line in file.readlines():
example = json.loa... | github_jupyter |
# Time Series Forecasting
In this tutorial, we will demonstrate how to build a model for time series forecasting in NumPyro. Specifically, we will replicate the **Seasonal, Global Trend (SGT)** model from the [Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications](https://cran.r-project.org/web/packages... | github_jupyter |
```
conda install -c conda-forge textblob
conda install -c conda-forge wordcloud
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sn
import sklearn
from textblob import TextBlob
from wordcloud import WordCloud
import nltk
nltk.download('vader_lexicon')
df= pd.read_csv(r"C:\U... | github_jupyter |
# CX 4230, Spring 2016: [09] Cellular Automata
The following exercises accompany the class slides on Wolfram's 1-D nearest neighbor cellular automata model. You can download a copy of those slides here: [PDF (0.7 MiB)](https://t-square.gatech.edu/access/content/group/gtc-59b8-dc03-5a67-a5f4-88b8e4d5b69a/cx4230-sp16--0... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
plt.figure(figsize=(7, 5))... | github_jupyter |
# Basic Keras Neural Network for Fashion MNIST
## Prerequisites
- Complete the installation process
- Keras
**VIDEO TUTORIAL:** https://youtu.be/Zq4onPm-h2I
```
import syft.interfaces.keras as keras
import keras as real_keras
from syft import FloatTensor
from syft.interfaces.keras.models import Sequential
from syf... | github_jupyter |
You have already seen [expressions](Expressions).
You saw in [variables](variables) that we often want to give names to the
results of expressions.
Now we get a little more formal about what that looks like in Python.
When Python gives a name to a value, that is an *assignment statement*.
A *statement* is a piece o... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from hold import connection_string
engine = create_engine(f'{connection_string}', encoding='iso-8859-1', connect_args={'connect_timeout': 10})
gtdDF = pd.read_sql_table('global_terrorism', ... | github_jupyter |
```
import forge
from puzzle.puzzlepedia import puzzlepedia
puzzle = puzzlepedia.parse("""
# NB: Column is implied from alpha-order. See #4.
items in {
Alarm, Chills, Dread, Terror, # Children’s Tinned Fears.
Balls, Floss, Biscuits, Mints, # Monster Treats.
Anger, Boredom, Laughter, Sorrow, # Salts.
Collywob... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.