code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
train = pd.read_csv('titanic_train.csv')
test = pd.read_csv('titanic_test.csv')
train.head(5)
train.info()
sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis', cbar=False)
sns.heatmap(test.isnul... | github_jupyter |
## 4. Training
In this part of notebook we will try to train the model using feature extracted dataset and do model evaluation to see how well it predicts churn
### Setup Prerequisite
```
!pip install pyspark
from google.colab import drive
drive.mount('/content/drive')
```
### Import Needed Library, Initialize Spar... | github_jupyter |
# PyCitySChools Solution
* Submitted by: Farshad Esnaashari
* Data Analytics Bootcap
* M-W session
```
# Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
school_data_to_load = "Resources/schools_complete.csv"
student_data_to_load = "Resources/students_complete.csv"
# Read ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, fixed
import ipywidgets as widgets
```
Zasymuluj wahadlo matematyczne rozwiazując numerycznie rownanie różniczkowe je opisujace
(rownież dla dużych wychyleń).
$$
\frac{d^2x}{dt^2} + \frac{g}{l} sin(x) = 0
$$
```
g = 9.81
l = 1
d... | github_jupyter |
# 基本程序设计
- 一切代码输入，请使用英文输入法
```
print('joker is bad man')
```
## 编写一个简单的程序
- 圆公式面积： area = radius \* radius \* 3.1415
### 在Python里面不需要定义数据的类型
```
radius = 100 # 定义变量
area = radius * radius * 3.14 # 普通代码，* 代表乘法
print(area) # 最后打印出结果
```
## 控制台的读取与输入
- input 输入进去的是字符串
- eval
- 在jupyter用shift + tab 键可以跳出解释文档
```
... | github_jupyter |
```
import musicntd.scripts.hide_code as hide
```
# From padding to subdivision
As evoked in the 1st notebook, in previous experiments, every bar of the tensor was zero-padded if it was shorter than the longest bar of the song.
This fix is not satisfactory, as it creates null artifacts at the end of most of the slic... | github_jupyter |
```
#######################################################################
# Copyright (C) #
# 2016-2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
# 2016 Tian Jun(tianjun.cpp@gmail.com) #
# 2016 Artem Oboturov(obotur... | github_jupyter |
## inference in simple model using synthetic data
population size 10^6, inference window 2x4 = 8 days, to be compared with ``-win5`` analogous notebook
```
%env OMP_NUM_THREADS=1
%matplotlib inline
import numpy as np
import os
import pickle
import pprint
import time
import pyross
import matplotlib.pyplot as plt
impor... | github_jupyter |
```
'''
Notebook to specifically study correlations between ELG targets and Galactic foregrounds
Much of this made possible and copied from script shared by Anand Raichoor
Run in Python 3; install pymangle, fitsio, healpy locally: pip install --user fitsio; pip install --user healpy; git clone https://github.com/eshe... | github_jupyter |
```
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
$('div.prompt').hide();
} else {
$('div.input').show();
$('div.prompt').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:... | github_jupyter |
# First Last - Homework 4
* Use the `Astropy` units and constants packages to solve the following problems.
* Do not hardcode any constants!
* Unless asked, your units should be in the simplest SI units possible
```
import numpy as np
from astropy import units as u
from astropy import constants as const
from astropy... | github_jupyter |
# Swish-based classifier with data augmentation and stochastic weght-averaging
- Swish activation, 4 layers, 100 neurons per layer
- Data is augmentaed via phi rotations, and transvers and longitudinal flips
- Model uses a running average of previous weights
- Validation score use ensemble of 10 models weighted by loss... | github_jupyter |
# 탐구실험용 toy 코드 (Facebook 바비 Question-Answer)
<p>
# +++++++++++++++++++++++++++++++++++++++++++++
# toy 코드의 한계 및 약점은 ? 약점을 보강할 수 있는 방법 ?
<p>
# 영어와 한글 데이터의 부족을 한영 번역기로 try 하며 탐구
<p>
## +++++++++++++++++++++++++++++++++++++++++++++++++++
<p>
## toy 코드를 통해 약점을 알아내고, 데이터를 조작하며... | github_jupyter |
```
import csv
import numpy as np
import os
import pandas as pd
import scipy.interpolate
import sklearn.metrics
import sys
sys.path.append("../src")
import localmodule
if sys.version_info[0] < 3:
from StringIO import StringIO
else:
from io import StringIO
from matplotlib import pyplot as plt
%matplotlib in... | github_jupyter |
# Skip-gram Word2Vec
In this notebook, I'll lead you through using PyTorch to implement the [Word2Vec algorithm](https://en.wikipedia.org/wiki/Word2vec) using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealin... | github_jupyter |
```
import os
from os.path import join, pardir
from collections import Counter
from copy import deepcopy
import numpy as np
from deap import base, creator, algorithms, tools
from dssg_challenge import compute_cost, check_keyboard
RNG_SEED = 0
DATA_DSSG = join(pardir, 'data', 'processed')
rng = np.random.RandomState(R... | github_jupyter |
# Problem Statement
Customer churn and engagement has become one of the top issues for most banks. It costs significantly more to acquire new customers than retain existing. It is of utmost important for a bank to retain its customers.
We have a data from a MeBank (Name changed) which has a data of 7124 customers. ... | github_jupyter |
# **Lab Session : Feature extraction II**
Author: Vanessa Gómez Verdejo (http://vanessa.webs.tsc.uc3m.es/)
Updated: 27/02/2017 (working with sklearn 0.18.1)
In this lab session we are going to work with some of the kernelized extensions of most well-known feature extraction techniques: PCA, PLS and CCA.
As in the p... | github_jupyter |
Practical 1: Sentiment Detection of Movie Reviews
========================================
This practical concerns sentiment detection of movie reviews.
In [this file](https://gist.githubusercontent.com/bastings/d47423301cca214e3930061a5a75e177/raw/5113687382919e22b1f09ce71a8fecd1687a5760/reviews.json) (80MB) you will... | github_jupyter |
```
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
path = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/ni... | github_jupyter |
```
from google.colab import files
files.upload()
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!pip install kaggle
!chmod 600 /root/.kaggle/kaggle.json
!kaggle competitions download -c home-credit-default-risk
!unzip \*.zip -d dataset
!rm -R sample_data
!rm *zip *csv
import os
import gc
import numpy as np
import pand... | github_jupyter |
- 广发证券之《深度学习之股指期货日内交易策略》
- 《宽客人生》
- 《主动投资组合管理》
-
-------------------------------------------------------
量化研报只是应付客户而做的产物，对于实际交易用处不大
策略对于市场的参数时刻都在变化
策略+相应的参数调整才是完整的
策略本身也需要非常强的主观调整 ----------周杰
拿到一个静态的策略并不是一个万能钥匙，对于细节处没多大用处，挣钱完全是靠细节
世界不存在一种一成不变的交易体系能让你永远的挣钱
---------------------------------... | github_jupyter |
## Passing Messages to Processes
As with threads, a common use pattern for multiple processes is to divide a job up among several workers to run in parallel. Effective use of multiple processes usually requires some communication between them, so that work can be divided and results can be aggregated. A simple way to ... | github_jupyter |
```
import urllib.request as urlreq
import urllib.error as urlerr
import urllib.parse as urlparse
import urllib.robotparser as urlrp
from bs4 import BeautifulSoup
import re
import datetime
import time
import sys
sys.path.append('../')
from common.utils import *
url = "http://example.webscraping.com/places/default/view... | github_jupyter |
# End-to-end quantum chemistry VQE using Qu & Co Chemistry
In this tutorial we show how to solve the groundstate energy of a hydrogen molecule using VQE, as a function of the spacing between the atoms of the molecule. For a more detailed discussion on MolecularData generation or VQE settings, please refer to our other ... | github_jupyter |
# Random Forest Project
For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having... | github_jupyter |
```
import photonqat as pq
import numpy as np
import matplotlib.pyplot as plt
```
## Photonqat
基本的なゲート動作と測定を一通り行っています。
```
G = pq.Gaussian(2) # two qumode [0, 1]
G.D(0, 2) # Displacement gate, x to x+2
G.S(0, 1) # X squeeIng gate, r=1
G.R(0, np.pi/4) # pi/4 rotation gate
G.BS(0, 1, np.pi/4) # 50:50 beam splitter
x =... | github_jupyter |
### Simple housing version
* State: $[w, n, M, e, \hat{S}, z]$, where $z$ is the stock trading experience, which took value of 0 and 1. And $\hat{S}$ now contains 27 states.
* Action: $[c, b, k, q]$ where $q$ only takes 2 value: $1$ or $\frac{1}{2}$
```
from scipy.interpolate import interpn
from multiprocessing impor... | github_jupyter |
# Chainer MNIST Model Deployment
* Wrap a Chainer MNIST python model for use as a prediction microservice in seldon-core
* Run locally on Docker to test
* Deploy on seldon-core running on minikube
## Dependencies
* [Helm](https://github.com/kubernetes/helm)
* [Minikube](https://github.com/kubernetes/miniku... | github_jupyter |
# An Introduction to SageMaker LDA
***Finding topics in synthetic document data using Spectral LDA algorithms.***
---
1. [Introduction](#Introduction)
1. [Setup](#Setup)
1. [Training](#Training)
1. [Inference](#Inference)
1. [Epilogue](#Epilogue)
# Introduction
***
Amazon SageMaker LDA is an unsupervised learning ... | github_jupyter |
```
from __future__ import print_function
import argparse
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from data import get_eval_set
from functools import reduce
import scipy.io as sio
import time
import imageio
impo... | github_jupyter |
```
# Enable importing of utilities
import sys
sys.path.append('..')
%matplotlib inline
```
# Cleaning up imagery for pre and post rainy season
The [previous tutorial](igarrs_chad_01.ipynb) addressed the identifying the extent of the rainy season near Lake Chad. This tutorial will focus on cleaning up optical imagery... | github_jupyter |
<a href="https://colab.research.google.com/github/SaashaJoshi/pennylane-demo-cern/blob/main/1_classical_ml_with_automatic_differentiation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%%capture
# Comment this out if you don't want to install... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from functions import *
%matplotlib inline
```
Per trovare i materiali che compongono i cluster scegliamo di **non eseguire un fit su ogni spettro all'interno di un determinato cluster**, ma **p... | github_jupyter |
# Teste para Duas Médias - ANOVA (Analysis of Variance)
Análise de variância é a técnica estatística que permite avaliar afirmações sobre as médias de populações. A análise visa, fundamentalmente, verificar se existe uma diferença significativa entre as médias e se os fatores exercem influência em alguma variável depe... | github_jupyter |
# Import development libraries
```
import bw2data as bd
import bw2calc as bc
import bw_processing as bwp
import numpy as np
import matrix_utils as mu
```
# Create new project
```
bd.projects.set_current("Multifunctionality")
```
Our existing implementation allows us to distinguish activities and prodducts, though n... | github_jupyter |
___
<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>
___
<center><em>Copyright Pierian Data</em></center>
<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>
# DataFrames
DataFrames are the workhorse of pandas and are ... | github_jupyter |
```
# general tools
import warnings
import requests
import pickle
import math
import re
# visualization tools
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import seaborn as sns
# data preprocessing tools
import pandas as pd
from shapely.geometry import Point
import numpy as np
from scipy.spatial.distanc... | github_jupyter |
# Intro to Hidden Markov Models (optional)
---
### Introduction
In this notebook, you'll use the [Pomegranate](http://pomegranate.readthedocs.io/en/latest/index.html) library to build a simple Hidden Markov Model and explore the Pomegranate API.
<div class="alert alert-block alert-info">
**Note:** You are not require... | github_jupyter |
# Teste Técnico para Ciência de Dados da Keyrus
## 1ª parte: Análise Exploratória
- [x] Tipos de variáveis
- [x] Medidas de posição
- [x] Medidas de dispersão
- [x] Tratamento de Missing Values
- [x] Gráficos
- [x] Análise de Outliers
## 2ª parte: Estatística
- [x] Estatística descritiva
- [x] Identificação das dis... | github_jupyter |
# Testing Web Applications
In this chapter, we explore how to generate tests for Graphical User Interfaces (GUIs), notably on Web interfaces. We set up a (vulnerable) Web server and demonstrate how to systematically explore its behavior – first with hand-written grammars, then with grammars automatically inferred fro... | github_jupyter |
## Ensembl to RefSeq Mapping
The constraint table from gnomAD has duplicate gene ID's - in the example of TUBB3 one gene ID is missannotated. Given out analysis is by transcript, it is probably better to use the transcript table from gnomAD. Howver, gnomAD used ENSEMBL transcripts and we used RefSeq Transcripts. Can m... | github_jupyter |
练习 1：求n个随机整数均值的平方根，整数范围在m与k之间。
```
import random, math
def test():
i = 0
total = 0
average = 0
number = random.randint(m, k)
while i < n:
i += 1
total += number
number = random.randint(m, k)
print('随机数是：', number)
average = int(total/n)
ret... | github_jupyter |
<a href="https://colab.research.google.com/github/parthsaxena1909/Image-Classifer-using-CIFRA10/blob/master/CNN_Keras_imageClassfier.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import tensorflow as tf
import os
import numpy as np
from matp... | github_jupyter |
# Locality Sensitive Hashing
```
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import pairwise_distances
import time
from copy import copy
import matplotlib.pyplot as plt
%matplotlib inline
'''compute norm of a sparse vector
Thanks to: Jaiyam Sharma'''
def... | github_jupyter |
# Classification
```
from nltk.corpus import reuters
import spacy
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics imp... | github_jupyter |
This notebook contains Hovmoller plots that compare the model output over many different depths to the results from the ORCA Buoy data.
```
import sys
sys.path.append('/ocean/kflanaga/MEOPAR/analysis-keegan/notebooks/Tools')
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import netCDF... | github_jupyter |
```
import torch
import torch.utils.data
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import numpy as np
import h5py
from data_utils import get_data
import matplotlib.pyplot as plt
from solver_pytorch import Solver
# Load data from all .mat files, combine them, eliminate EOG sig... | github_jupyter |
```
from sklearn import datasets #sklearn é uma das lib mais utilizadas em ML, ela contém, além dos
#datasets, várias outras funções úteis para a análise de dados
# essa lib será sua amiga durante toda sua carreira
import pandas as pd # importa a lib Pandas. Ess... | github_jupyter |
# Blue Food
Visualizing protein supply and how the practices generating that protein supply affect the ocean using a heirarchical relationship.
Note that this is a parameterized widget; the specification passed to the API will not be renderable without the geostore identifier being inserted.
*Author: Rachel Thoms
<b... | github_jupyter |
Rossler performance experiments
```
import numpy as np
import torch
import sys
sys.path.append("../")
import utils as utils
import NMC as models
import importlib
```
## SVAM
```
# LiNGAM / SVAM performance with sparse data
import warnings
warnings.filterwarnings("ignore")
for p in [10, 50]:
perf = []
for i... | github_jupyter |
#### Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
# Copyright 2019 The TensorFlow Hub Authors. 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.
#... | github_jupyter |
# Polynomial Regression and Cross Validation
For the first assignment we will do something that might seem familiar from *Probability Theory for Machine Learning*; try to fit a polynomial function to a provided dataset. Fitting a function is a quintessential example of *supervised learning*, specifically *regression*,... | github_jupyter |
# Exploring Reddit with the pushshift API
This notebook give you examples of how to use the pushshift API for querying Reddit data.
* Pushshift doc: https://github.com/pushshift/api
* FAQ about Pushshift: https://www.reddit.com/r/pushshift/comments/bcxguf/new_to_pushshift_read_this_faq/
```
import requests
import pa... | github_jupyter |
# CME Smart Stream on Google Cloud Platform Tutorials
## Getting CME Binary Data from CME Smart Stream on Google Cloud Platform (GCP)
This workbook demonstrates the ability to quickly use the CME Smart Stream on GCP solution. Through the examples, we will
- Authenticate using GCP IAM information
- Configure which CM... | github_jupyter |
```
from __future__ import division
%matplotlib inline
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.io as io
import pickle
import scipy.stats
SBJ = 'colin_test2'
prj_dir = '/Volumes/hoycw_clust/PRJ_Error_eeg/'#'/Users/sheilasteiner/Desk... | github_jupyter |
#### _Speech Processing Labs 2021: SIGNALS 1: Digital Signals: Sampling and Superposition_
```
## Run this first!
%matplotlib inline
import sys
import matplotlib.pyplot as plt
import numpy as np
import cmath
from matplotlib.animation import FuncAnimation
from IPython.display import HTML
plt.style.use('ggplot')
from ... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
class planet():
"A planet in our solar system"
def __init__(self,semimajor,eccentricity):
self.x = np.zeros(2) #x and y position
self.v = np.zeros(2) #x and y velocity
self.a_g = ... | github_jupyter |
<img src="../../img/logo_amds.png" alt="Logo" style="width: 128px;"/>
# AmsterdamUMCdb - Freely Accessible ICU Database
version 1.0.2 March 2020
Copyright © 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science
## Sequential Organ Failure Assessment (SOFA)
The sequential organ failure assessment score (SOF... | github_jupyter |
<a href="https://colab.research.google.com/github/aubricot/computer_vision_with_eol_images/blob/master/object_detection_for_image_cropping/chiroptera/chiroptera_train_tf2_ssd_rcnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Train Tensorflow Fa... | github_jupyter |
Copyright (c) 2021, 2022 Oracle and/or its affiliates.
Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
## Unix Operations
_Important: The ocifs SDK isn't a one-to-one adaptor of OCI Object Storage and UNIX filesystem operations. It's a set of convenient wrappings... | github_jupyter |
## **Yolov3 Algorithm**
```
import struct
import numpy as np
import pandas as pd
import os
from keras.layers import Conv2D
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import LeakyReLU
from keras.layers import ZeroPadding2D
from keras.layers import UpSampling2D
from kera... | github_jupyter |
```
################################ NOTES ##############################ex
# Lines of code that are to be excluded from the documentation are #ex
# marked with `#ex` at the end of the line. #ex
# #ex
# To ensure ... | github_jupyter |
<a href="https://colab.research.google.com/github/bluesky0960/AI_Study/blob/master/AutoEncoder_Conv(TensorFlow_2).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 오토인코더 (TensorFlow 2)
텐서플로우 2에서 제공하는 고수준 API인 케라스를 이용해, 오토인코더(autoencoder)를 구현한다.
* Go... | github_jupyter |
# Content with notebooks
You can also create content with Jupyter Notebooks. This means that you can include
code blocks and their outputs in your book.
## Markdown + notebooks
As it is markdown, you can embed images, HTML, etc into your posts!
![]... | github_jupyter |
```
import numpy as np
from datetime import datetime, timedelta
import time
# some probabilities should be dynamics, for example:
# buying probability depends on the number of available items
# listing probability increases if user has sold something in the past
# probability of churn increases if user hasn't listed + ... | github_jupyter |
# Introduction to Spark and Python
Let's learn how to use Spark with Python by using the pyspark library! Make sure to view the video lecture explaining Spark and RDDs before continuing on with this code.
This notebook will serve as reference code for the Big Data section of the course involving Amazon Web Services. ... | github_jupyter |
# Recognize named entities on Twitter with LSTMs
In this assignment, you will use a recurrent neural network to solve Named Entity Recognition (NER) problem. NER is a common task in natural language processing systems. It serves for extraction such entities from the text as persons, organizations, locations, etc. In t... | github_jupyter |
<center><img src="http://www.exalumnos.usm.cl/wp-content/uploads/2015/06/Isotipo-Negro.gif" title="Title text" width="30%" /></center>
<hr style="height:2px;border:none"/>
<h1 align='center'> INF-398 Aprendizaje Automático </h1>
<H3 align='center'> Tarea/Taller 1 </H3>
<hr style="height:2px;border:none"/>
# Temas
*... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
# "Monte Carlo 6: Off-Policy Control with Importance Sampling in Reinforcement Learning"
> Find the optimal policy using Weighted Importance Sampling
- toc: true
- branch: master
- badges: false
- comments: true
- hide: false
- search_exclude: true
- metadata_key1: metadata_value1
- metadata_key2: metadata_value2
- im... | github_jupyter |
```
import pandas as pd
from sklearn.model_selection import train_test_split
'''
NOTE: This was done in Google Colab
The data (Minimum Daily Temperatures Dataset) is from
Jason Brownlee's "7 Time Series Datasets for Machine Learning" article:
https://machinelearningmastery.com/time-series-datasets-for-machine-learnin... | github_jupyter |
## This notebook:
- Try deep learning method on content based filtering
----------------------
### 1. Read files into dataframe
### 2. concat_prepare(f_df, w_df)
- Concat f_21, w_21
### 3. store_model(df) - only once for a new dataframe
- Train a SentenceTransformer model
- Save embedder, embeddings, and corpus
###... | github_jupyter |
# GRANDMA/Kilonova-catcher --- KN-Mangrove
The purpose of this notebook is to inspect the ZTF alerts that were selected by the Fink KN-Mangrove filter as potential Kilonova candidates in the period 2021/04/01 to 2021/08/31, and forwarded to the GRANDMA/Kilonova-catcher project for follow-up observations.
With the oth... | github_jupyter |
# The Perceptron
```
import mxnet as mx
from mxnet import nd, autograd
import matplotlib.pyplot as plt
import numpy as np
mx.random.seed(1)
```
## A Separable Classification Problem
```
# generate fake data that is linearly separable with a margin epsilon given the data
def getfake(samples, dimensions, epsilon):
... | github_jupyter |
# Lecture 2: Introducing Python
CSCI 1360E: Foundations for Informatics and Analytics
## Overview and Objectives
In this lecture, I'll introduce the Python programming language and how to interact with it; aka, the proverbial [Hello, World!](https://en.wikipedia.org/wiki/%22Hello,_World!%22_program) lecture. By the ... | github_jupyter |
```
import numpy as np
import itertools
import math
import scipy
from scipy import spatial
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.patches as patches
from matplotlib import animation
from matplotlib import transforms
from mpl_toolkits.axes_grid1 import make_axes_locatable
import xarray as xr... | github_jupyter |
<a href="https://colab.research.google.com/github/JaccoVeldscholten/SmartDispenser/blob/main/BAVA_Temp_Predictions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<div>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABpQAAAIHCAYAAACR5L9TAAAA... | github_jupyter |
## 1. The World Bank's international debt data
<p>It's not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. <a href="https://www.worldba... | github_jupyter |
```
import pandas as pd
import numpy as np
HUES64_rep1_tfxn1_fs = ["../../../data/02__mpra/01__counts/07__HUES64_rep6_lib1_BARCODES.txt",
"../../../data/02__mpra/01__counts/07__HUES64_rep6_lib2_BARCODES.txt"]
HUES64_rep1_tfxn2_fs = ["../../../data/02__mpra/01__counts/08__HUES64_rep7_lib1_BARCODE... | github_jupyter |
# Preparing and loading your data
This tutorial introduces how SchNetPack stores and loads data.
Before we can start training neural networks with SchNetPack, we need to prepare our data.
This is because SchNetPack has to stream the reference data from disk during training in order to be able to handle large datasets.
... | github_jupyter |
```
### ATOC5860 Application Lab #6 - supervised machine learning
### Coded by Eleanor Middlemas (Jupiter, formerly University of Colorado, elmiddlemas at gmail.com)
### Additional code/commenting by Jennifer Kay (University of Colorado)
### Last updated April 6, 2022
import pandas as pd
import numpy as np
import dat... | github_jupyter |
# Q-PART C-15
```
from pulp import *
import pyomo.environ as pe
import logging
logging.getLogger('pyomo.core').setLevel(logging.ERROR)
from pyomo.environ import *
from math import pi
import warnings
warnings.filterwarnings('ignore')
m = ConcreteModel()
m.a = pe.Set(initialize=[1, 2, 3, 4])
m.demand = pe.Var(m.a, bou... | github_jupyter |
# Building our operators: the Face Divergence
The divergence is the integral of a flux through a closed surface as that enclosed volume shrinks to a point. Since we have discretized and no longer have continuous functions, we cannot fully take the limit to a point; instead, we approximate it around some (finite!) volu... | github_jupyter |
# Synthetic seismogram
This notebook looks at the convolutional model of a seismic trace.
For a fuller example, see [Bianco, E (2004)](https://github.com/seg/tutorials-2014/blob/master/1406_Make_a_synthetic/how_to_make_synthetic.ipynb) in *The Leading Edge*.
First, the usual preliminaries.
```
import numpy as np
im... | github_jupyter |
<a href="https://colab.research.google.com/github/Serbeld/RX-COVID-19/blob/master/Detection5C_NormNew_v2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install lime
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from t... | github_jupyter |
# Models selection
Maintenant que nous avons créé des features grâce à l'étude du domaine métier et à l'EDA, et que nous les avons sélectionnées grâce à Boruta, nous pouvons passer à la phase de sélection du ou des modèles les plus adaptées à notre dataset.
L'EDA soulève quelques interrogations à ce sujet mais le mei... | github_jupyter |
_Lambda School Data Science — Tree Ensembles_
# Decision Trees
### Links
- A Visual Introduction to Machine Learning, [Part 1: A Decision Tree](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/), and [Part 2: Bias and Variance](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/)
- [Decision Trees... | github_jupyter |
# SSD300 Training Tutorial
This tutorial explains how to train an SSD300 on the Pascal VOC datasets. The preset parameters reproduce the training of the original SSD300 "07+12" model. Training SSD512 works simiarly, so there's no extra tutorial for that. The same goes for training on other datasets.
You can find a su... | github_jupyter |
# RidgeRegression with Scale & Power Transformer
This Code template is for the regression analysis using simple Ridge Regression with Feature Rescaling technique Scale and Feature Transformation technique PowerTransformer in a pipeline. Ridge Regression is also known as Tikhonov regularization.
### Required Packages... | github_jupyter |
# Understanding Principal Component Analysis
**Outline**
* [Introduction](#intro)
* [Assumption and derivation](#derive)
* [PCA Example](#example)
* [PCA Usage](#usage)
```
%load_ext watermark
%matplotlib inline
# %config InlineBackend.figure_format='retina'
from matplotlib import pyplot as plt
import pandas as pd
... | github_jupyter |
# Chatbot using Seq2Seq LSTM models
In this notebook, we will assemble a seq2seq LSTM model using Keras Functional API to create a working Chatbot which would answer questions asked to it.
Chatbots have become applications themselves. You can choose the field or stream and gather data regarding various questions. We c... | github_jupyter |
```
from config import *
import mPyPl as mp
from mPyPl.utils.flowutils import *
from mpyplx import *
from pipe import Pipe
from functools import partial
import numpy as np
import cv2
import itertools
from moviepy.editor import *
import pickle
import functools
from config import *
test_names = (
from_json(os.path.... | github_jupyter |
```
#default_exp torch_core
#export
from local.test import *
from local.imports import *
from local.torch_imports import *
from local.core import *
from local.notebook.showdoc import show_doc
#export
if torch.cuda.is_available(): torch.cuda.set_device(int(os.environ.get('DEFAULT_GPU') or 0))
```
# Torch Core
> Basic ... | github_jupyter |
# COMP551: Project 4
```
import pandas as pd
import torch
import torchvision
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
from torch.utils.data import DataLoader, Dataset, TensorDataset
# Load the Drive helper and mount
from google.colab import drive
# This will prompt for auth... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D4_DynamicNetworks/W2D4_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 1: Neural Rate Models
**Week 2, Day 4: Dynamic Ne... | github_jupyter |
# Find \*.tifs with no matching \*.jpg
#### Created on Cinco de Mayo in 2020 by Jeremy Moore and David Armstrong to identify \*.tif images that don't have a matching \*.jpg image for the Asian Art Museum of San Francisco
1. Manually set root_dir_path to the full path of the directory containing your *all_jpgs* and *a... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will learn how to load and pre-process data from the [COCO dataset](http://cocodataset.org/#home). You will also design a CNN-RNN model for automatically generating image captions.
Note that **any amendments that you make to this no... | github_jupyter |
# Automated Machine Learning
**Continuous retraining using Pipelines and Time-Series TabularDataset**
## Contents
1. [Introduction](#Introduction)
2. [Setup](#Setup)
3. [Compute](#Compute)
4. [Run Configuration](#Run-Configuration)
5. [Data Ingestion Pipeline](#Data-Ingestion-Pipeline)
6. [Training Pipeline](#Training... | github_jupyter |
<a href="https://colab.research.google.com/github/pingao2019/DS-Unit-2-Kaggle-Challenge/blob/master/h3Copy_of_LS_DS_223_assignment.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, Sprint 2, Module 3*
---
# Cross... | github_jupyter |
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