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# (Temporary) Notebook that Compares Quant results to the SIMs that Mark created
```
import multiprocessing as mp
import numpy as np
#import multiprocess as mp # A fork of multiprocessing that uses dill rather than pickle
import yaml # pyyaml library for reading the parameters.yml file
import os
import matplotlib.py... | github_jupyter |
# Seasonal Autoregressive Integrated Moving Average with Explanatory Variable (SARIMAX)
The <a href="https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average">ARIMA</a> model is a generalisation of an ARMA model that can be applied to non-stationary time series.
The SARIMAX model is an modified and exte... | github_jupyter |
#
```
%load_ext autoreload
%autoreload 2
from ramprate.load_dataset import load_epacems, load_epa_crosswalk
from ramprate.build_features import uptime_events, calc_distance_from_downtime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
## CEMS Processing
```
# all stat... | github_jupyter |
# Debugging strategies
You will get errors in your scripts. This is not a bad thing! It's just part of the process -- the error messages will help guide you to the solution. The key is to not get discouraged.
A typical development pattern: Write some code. Run it. See what errors break your script. Throw in some `pri... | github_jupyter |
# Imports
```
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.externals import joblib
import torch
import torchvision
import torchvisi... | github_jupyter |
# Video Pipeline Details
This notebook goes into detail about the stages of the video pipeline in the base overlay and is written for people who want to create and integrate their own video IP. For most regular input and output use cases the high level wrappers of `HDMIIn` and `HDMIOut` should be used.
Both the input... | github_jupyter |
```
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torch.utils.data.sampler import SubsetRandomSampler
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import torchvision.models
from PIL imp... | github_jupyter |
# Week 1 Homework and/or In-Class Activity
## Working with jupyter notebooks and Python fundamentals
### In the next cell, do the following:
1. Create markdown cell
2. Create a level 1 header with the text "Week 1" as the header text
3. Create a level 2 header with the text "Learning Jupyter Notebooks and Basic Python... | github_jupyter |
```
import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
from IPython import display
import torchsummary as ts
import num... | github_jupyter |
# Exploring S1-NRB data cubes
## Introduction
**Sentinel-1 Normalised Radar Backscatter**
Sentinel-1 Normalised Radar Backscatter (S1-NRB) is a newly developed Analysis Ready Data (ARD) product for the European Space Agency that offers high-quality, radiometrically terrain corrected (RTC) Synthetic Aperture Radar (... | github_jupyter |
# Lecture 3
## Differentiation I:
### Introduction and Interpretation
```
import numpy as np
##################################################
##### Matplotlib boilerplate for consistency #####
##################################################
from ipywidgets import interact
from ipywidgets import FloatSlider
fro... | github_jupyter |
# Descriptive Statistics
1. Descriptive Statistics and Graphs
2. Number of Tweets (Total)
3. Number of Tweets (Time Series)
4. Gender Distribution
5. Language Distribution
6. Follower Counts
7. Client Usage (Android, iPhone, web etc.)
# Jupyter Notebook Style
Let's make this thing look nice.
```
from IPython.core.di... | github_jupyter |
Copyright 2019 The Dopamine Authors.
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 writing, software... | github_jupyter |
```
import tsflex
print(tsflex.__version__)
```
## Get the data
```
from tsflex.utils.data import load_empatica_data
df_tmp, df_acc, df_gsr, df_ibi = load_empatica_data(["tmp", "acc", "gsr", "ibi"])
from pandas.tseries.frequencies import to_offset
data = [df_tmp, df_acc, df_gsr, df_ibi]
for df in data:
print("T... | github_jupyter |
# Kestrel+Model
### A [Bangkit 2021](https://grow.google/intl/id_id/bangkit/) Capstone Project
Kestrel is a TensorFlow powered American Sign Language translator Android app that will make it easier for anyone to seamlessly communicate with people who have vision or hearing impairments. The Kestrel model builds on the ... | github_jupyter |
# Welcome to `bruges`
This notebook accompanies [a blog post on agilegeoscience.com](http://www.agilegeoscience.com/blog/).
If you are running this locally, you need to install [`bruges`](https://github.com/agile-geoscience/bruges) first:
pip install bruges
This notebook also requires [`welly`](https://github.... | github_jupyter |
```
from estruturas.pilha import *
from estruturas.fila import *
from estruturas.deque import *
from estruturas.pilha_dinamica import *
from estruturas.fila_dinamica import *
from estruturas.lista import *
from estruturas.arvore import *
from estruturas.arvore_binaria import *
from cyjupyter import Cytoscape
import... | github_jupyter |
##### Copyright 2020 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 |
```
# slow down a bit when hacking something together, e.g. I forgot to add a simple function call
# tuple unpacking is nice, but cannot be done in a nested list comprehension
# don't forget .items in for k,v in dict.items()
# use hashlib for md5 encodings
# multiline list comprehensions don't need extra parentheses,... | github_jupyter |
# Autonomous Driving - Car Detection
Welcome to the Week 3 programming assignment! In this notebook, you'll implement object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: [Redmon et al., 2016](https://arxiv.org/abs/1506.02640) and [Redmon and Far... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
```
# default_exp diffdrive
# hide
from fastcore.all import *
```
# Differential Drive Chapter
Some code to create and display maps/likelihoods in Chapter 4.
```
# export
import gtsam
import math
import PIL
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
```
## Pinhole Figures
```
... | github_jupyter |
## _*H2 excited states from NumPyEigensolver*_
This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state and excited state energies of the Hydrogen (H2) molecule over a range of inter-atomic distances. This notebook utilizes the fact that when two_qubit_reduction is used with the parity mapp... | github_jupyter |
We show that linear_model.Lasso provides the same results for dense and sparse data and that in the case of sparse data the speed is improved.
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](h... | github_jupyter |
# An Inventory of the Shared Datasets in the LSST Science Platform
<br>Owner(s): **Phil Marshall** ([@drphilmarshall](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@drphilmarshall)), **Rob Morgan** ([@rmorgan10](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@rmorgan10))... | github_jupyter |
# 17. Random Forest and Gradient Boosted Trees Classifier
[](https://colab.research.google.com/github/rhennig/EMA6938/blob/main/Notebooks/17.RandomForest.ipynb)
Previously, we used a Decision Tree Classifier to learn the fcc, bcc, and hcp cryst... | github_jupyter |
# Deep multimodal Two Stream Action Recognition
Import Deep Learning streams (Video and Pulse)
```
import numpy as np
from streams.rgbi3d import rgbi3d
from streams.cnn_lstm import cnn_lstm
from streams.two_stream import two_stream
```
Firstly, we obtain the action list with the name and identifier that this solutio... | github_jupyter |
# Traduzione
Una delle forze motrici che ha permesso lo sviluppo della civiltà umana è la capacità di comunicare reciprocamente. Nella maggior parte delle attività umane, la comunicazione è fondamentale.
L'intelligenza artificiale (IA) può aiutare a semplificar... | github_jupyter |
```
from lxml import etree
import pandas as pd
from collections import Counter
import os
import glob
import re
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter
from numpy import array
import numpy as np
wdir = "/home/jose/Dropbox/biblia/tb/"
file = "TEIBible" # "*.xml"
outdir = "/hom... | github_jupyter |
```
import sys
import pathlib
import numpy as np
import pandas as pd
sys.path.insert(0, "../../scripts")
from utils import load_data
from pycytominer.cyto_utils import infer_cp_features
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sklearn.decomposition import PCA
from tensorflow import... | github_jupyter |
Deep Learning
=============
Assignment 2
------------
Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).
The goal of this assignment is to progressively train deep... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
# SIT742: Modern Data Science
**(Week 07: Big Data Platform (II))**
---
- Materials in this module include resources collected from various open-source online repositories.
- You are free to use, change and distribute this package.
- If you found any issue/bug for this document, please submit an issue at [tulip-lab/s... | github_jupyter |
In this notebook, we'll learn how to use GANs to do semi-supervised learning.
In supervised learning, we have a training set of inputs $x$ and class labels $y$. We train a model that takes $x$ as input and gives $y$ as output.
In semi-supervised learning, our goal is still to train a model that takes $x$ as input and... | github_jupyter |
```
# Train neural network to predict significant wave height from SAR spectra.
# Train with heteroskedastic regression uncertainty estimates.
# Author: Peter Sadowski, Dec 2020
import os, sys
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # Needed to avoid cudnn bug.
import numpy as np
import h5py
import tensorflow... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
## 5.3 季節など周期性で売り上げ予測(時系列分析)
### 共通事前準備
```
# 日本語化ライブラリ導入
!pip install japanize-matplotlib | tail -n 1
# 共通事前処理
# 余分なワーニングを非表示にする
import warnings
warnings.filterwarnings('ignore')
# 必要ライブラリのimport
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# matplotlib日本語化対応
import japanize_matplotlib
... | github_jupyter |
```
import os,datetime,re
from utils import News, Site
class EngENNews(News):
def __init__(self, tag, base_url):
'''
<parameter>
tag (bs4.element.Tag) : single topic object
'''
self.tag = tag
self.base_url = base_url
self.summary()
# this should be overri... | github_jupyter |
## Calculate skill of a MJO Index of S2S models as function of daily lead time
```
# linting
%load_ext nb_black
%load_ext lab_black
import xarray as xr
xr.set_options(display_style="html")
import numpy as np
import matplotlib.pyplot as plt
from climpred import HindcastEnsemble
import climpred
```
IRIDL hosts variou... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
## Exponential Moving Average
t_i = alpha * t_{i-1} + (1 - alpha) * s_i, with a value of alpha = 0.99
```
import os
os.chdir(os.path.join(os.getcwd(), '..'))
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from src.model import mean_teacher
from keras.applications.resnet50 ... | github_jupyter |
<a href="https://colab.research.google.com/github/Data-Science-and-Data-Analytics-Courses/UniMelb---Database-Systems-Information-Modelling-INFO90002_2019_SM1/blob/master/Resources.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Clone remote
```
i... | github_jupyter |
### String Problems
##### https://leetcode.com/explore/challenge/card/30-day-leetcoding-challenge/528/week-1/3283/
##### Q.1. Given a non-empty array of integers, every element appears twice except for one. Find that single one.
```
from typing import List
"""
For O(1) space complexity use math operation or XOR.
a^a ... | github_jupyter |
# Data validation
* https://adventofcode.com/2020/day/4
We get to validate passports. Part 1 asks us to validate the fields; there are a number of required fields, and one optional. This is mostly a parsing task, however.
The data for each passport is separated from the next by a blank line, so we just split the who... | 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 havin... | github_jupyter |
# Getting to know qubits through QEC
### James R. Wootton, IBM Quantum
## Introduction
Back in 2018 I worked as a quantum error correction researcher at the University of Basel. IBM had just put a 16 qubit device online, and I wanted to see how well it could implement the basics of QEC. So I ran repetition codes.
... | github_jupyter |
```
class ListNode:
def __init__(self, x):
self.val = x
self.next = None
```
链表的题目比较单一，因为链表数据结构的特殊性，成员只能通过指针访问，所以根据维护的指针数量可以大致分为几类：
- 单指针
- 双指针
- 多指针
## 单指针
单指针指的是只需要维护单个工作指针用于扫描链表，而该指针通常指向前驱节点。
[Delete Node in a Linked List](https://leetcode.com/problems/delete-node-in-a-linked-list/)。只给出待删除节点的指... | github_jupyter |
# Import the dataset
```
import numpy as np
import pandas as pd
import tensorflow as tf
from keras import Sequential
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Embedding, GlobalMaxPool1D, LSTM, Input
from keras.losses import Bin... | github_jupyter |
```
%matplotlib inline
%precision 2
```
# Nonparametric Latent Dirichlet Allocation
_Latent Dirichlet Allocation_ is a [generative](https://en.wikipedia.org/wiki/Generative_model) model for topic modeling. Given a collection of documents, an LDA inference algorithm attempts to determined (in an unsupervised manner) t... | github_jupyter |
# Formati dati 1 - Introduzione
## [Scarica zip esercizi](../_static/generated/formats.zip)
[Naviga file online](https://github.com/DavidLeoni/softpython-it/tree/master/formats)
In questi tutorial parleremo di formati dei dati:
File testuali:
* File a linee
* CSV
* breve panoramica sui cataloghi open data
* menzio... | github_jupyter |
# Data Structures
In simple terms, It is the the collection or group of data in a particular structure.
## Lists
Lists are the most commonly used data structure. Think of it as a sequence of data that is enclosed in square brackets and data are separated by a comma. Each of these data can be accessed by calling it's... | github_jupyter |
*It's custom ResNet trained demonstration purpose, not for accuracy.
Dataset used is cats_vs_dogs dataset from tensorflow_dataset with **ImageDataGenerator** for data augmentation*
---
### **1. Importing Libraries**
```
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxP... | github_jupyter |
# Welcome to BME 590: Machine Learning in Imaging
## People
<img src="https://bme.duke.edu/sites/bme.duke.edu/files/u12/xhorstmeyer_200x200px.jpg.pagespeed.ic.SLWkDogtxs.webp" alt="Roarke Horstmeyer" width="100"/>
Roarke Horstmeyer - rwh4@duke.edu | Office location: CIEMAS 2569
Office hours: Wednesdays 3:00pm-4:... | github_jupyter |
# EPSchema
# 1. Introduction
This notebook explores the EnergyPlus schema using the EPSchema class in the eprun package.
## 2. Setup
### 2.1. Module Imports
```
from eprun import EPSchema
```
### 2.2. Filepaths
```
fp='Energy+.schema.epJSON'
```
## 3. Reading the schema file
### 3.1. Import
```
schema=EPSchem... | github_jupyter |
# Super Scratcher
This is a simple (but powerful) example of grabbing all the stuff from Rotten Tomato website via given list of movies.
```
from goje_scrapper.goje import *
import pymongo
goje_jaan = GojeScraper()
movie_list = goje_jaan.extract_movie_links(1990,1991)
# movie_list[i][0] = movie name
# movie_list[i][1... | github_jupyter |
# Change directory to wherever you are housing this project
```
import sys
sys.path.append("C:/Users/ahaberlie/Documents/GitHub/MCS/")
```
# Download example radar data
Download data.tar.gz from https://tiny.cc/ + the full manuscript ID for part 1 (case sensitive), and untar and ungzip this into the directory "MCS/m... | github_jupyter |
Hypothesis Testing
==================
Copyright 2016 Allen Downey
License: [Creative Commons Attribution 4.0 International](http://creativecommons.org/licenses/by/4.0/)
```
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from ipywidgets import int... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Optimizing runtime performance on GPT-2 model inference with ONNXRuntime on CPU
In this tutorial, you'll be introduced to how to load a GPT2 model from PyTorch, convert it to ONNX with one step search, and inference it using O... | github_jupyter |
[[source]](../api/alibi.explainers.cfproto.rst)
# Counterfactuals Guided by Prototypes
## Overview
This method is based on the [Interpretable Counterfactual Explanations Guided by Prototypes](https://arxiv.org/abs/1907.02584) paper which proposes a fast, model agnostic method to find interpretable counterfactual exp... | github_jupyter |
```
import pandas as pd
data = pd.read_csv("https://short.upm.es/dyjzp")
data.info()
data.head()
data.describe()
import seaborn as sns
sns.countplot(x='class', data=data)
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
data['class'] = encoder.fit_transform(data['class'])
data.head()
column_names... | github_jupyter |
# Construction of Regression Models using Data
Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Notebook version: 2.0 (Sep 26, 2017)
Changes: v.1.0 - First version. Extracted from regression_intro_knn v.1.0.
v.1.1 - Compatibility with pyth... | github_jupyter |
# Multiple Linear Regression
## Objectives
After completing this lab you will be able to:
* Use scikit-learn to implement Multiple Linear Regression
* Create a model, train it, test it and use the model
<h1>Table of contents</h1>
<div class="alert alert-block alert-info" style="margin-top: 20px">
<ol>
... | 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, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Concat... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
#Initialize input as a matrix
#Each row is a different training example
#Each column is a different neuron
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1],
[1,1,1]])
#Create output da... | github_jupyter |
##### Copyright 2020 The Cirq Developers
```
#@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 agre... | github_jupyter |
```
# -*- coding: utf-8 -*-
#@author: alison
import re
import string
import pickle
import keras
import numpy as np
import pandas as pd
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from nltk.stem import PorterStemmer, SnowballStemmer
from nltk.tokenize import TweetTokenizer
fr... | github_jupyter |
```
!pip install tensorflow==2.0.0b1
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")
plt.grid(True)
!... | github_jupyter |
En este cuaderno se implementan algunas funciones y algunos segmentos de código que pueden ser útiles para el desarrollo del [Taller 1](https://github.com/andresgm/Herramientas-Computacionales/tree/master/02_taller01) del curso.
Comienzo importando dos librerías que no habíamos usado hasta ahora en el curso.
La lib... | github_jupyter |
# [L&S 88] Open Science -- Project 1, part 1
---
### Instructors Eric Van Dusen and Josh Quan
In this notebook we will be covering different approaches to Exploratory Data Analysis (EDA), exploring how different techniques and approachs can lead to different results and conclusions about data.
We will be exploring ... | github_jupyter |
### Task 1: Importing Libraries
```
import pandas as pd
import numpy as np
import seaborn as sns
from scipy.stats import skew
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use("ggplot")
plt.rcParams['figure.figsize'] = (12, 8)
```
### Task 2: Load the Data
The adverstiting dataset captures sales rev... | github_jupyter |
---
## <span style="color:orange"> Host Multiple TensorFlow Computer Vision Models using SageMaker Multi-Model Endpoint </span>
---
## <span style="color:black">Contents</span>
1. [Background](#Background)
1. [Setup](#Setup)
1. [Train Model 1 - CIFAR-10 Image Classification](#Train-Model-1---CIFAR-10-Image-Classificati... | github_jupyter |
# ✨ REST API Usage
---
This notebook explains how Superwise model KPIs can be consumed and analyzed using REST API calls. There are three main parts:
[**1. Connection**](#1.-Connection) - Initiates the mandatory token-based authentication. [More details here](https://docs.superwise.ai/v0.1/docs/authentication).
[**... | github_jupyter |
# Detrending, Stylized Facts and the Business Cycle
In an influential article, Harvey and Jaeger (1993) described the use of unobserved components models (also known as "structural time series models") to derive stylized facts of the business cycle.
Their paper begins:
"Establishing the 'stylized facts' associat... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data=pd.read_csv('train.csv')
test=pd.read_csv('test.csv')
y=data.SalePrice
y.shape
test.shape
x=data.drop(labels=['SalePrice'],axis=1,)
import seaborn as sns
sns.heatmap(x.isnull())
cdrop=['Alley','FireplaceQu','PoolQC','MiscFeature','Fence']
... | github_jupyter |
# Representing data in memory
A typical program outline calls for us to load data from disk and place into memory organized into data structures. The way we represent data in memory is critical to building programs. This is particularly true with data science programs because processing data is our focus.
First, le... | github_jupyter |
gmail_spam_detection:
- our goal for this competition is to build a spam filter by predicting whether an email message is spam (junk email) or ham (good email). This is a classic data set derived from a *bag-of-words* model applied 4601 email messages collected at Hewlett-Packard Labs. The features consist of the rela... | github_jupyter |
# Amazon SageMaker Neo でコンパイルしたモデルを AWS IoT Greengrass V2 を使ってデバイスにデプロイする
このサンプルノートブックは、エッジ推論を行うために学習済みモデルを Amazon SageMaker Neo でコンパイルして AWS Iot Greengrass V2 を使ってデバイスにデプロイするパイプラインを AWS Step Functions を使って自動化する方法をご紹介します。このノートブックを Amazon SageMaker のノートブックインスタンスで使用する場合は、`conda_tensorflow_p36` のカーネルをご利用ください。
このノートブックでは... | github_jupyter |
```
import setGPU
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="4"
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from scipy import stats
import tensorflow as tf
from pylab import rcParams
import seaborn as sns
from sklearn.model_selection i... | github_jupyter |
<a href="https://colab.research.google.com/github/drc10723/GAN_design/blob/master/GAN_implementations/Basic_GAN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Generative Adversarial Networks
Aim of this notebook is to implement [Generative Advers... | github_jupyter |
# Pandas
Pandas is a powerful, open source Python library for data analysis, manipulation, and visualization. If you're working with data in Python and you're not using pandas, you're probably working too hard!
There are many things to like about pandas: It's well-documented, has a huge amount of community support, i... | github_jupyter |
# Account management
Qiskit Runtime is available on both IBM Cloud and IBM Quantum. The former requires an IBM Cloud account and the latter an IBM Quantum account. If you don't have these accounts, please refer to [01_introduction_ibm_cloud_runtime.ipynb](01_introduc... | github_jupyter |
# AceleraDev DataScience
## Setup
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
```
#lendo os pacotes
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
df = pd.read_csv('train.csv')
```
## Analysis
### Selecao por completude
```
#Criando um d... | github_jupyter |
```
%%html
<style>
.h1_cell, .just_text {
box-sizing: border-box;
padding-top:5px;
padding-bottom:5px;
font-family: "Times New Roman", Georgia, Serif;
font-size: 125%;
line-height: 22px; /* 5px +12px + 5px */
text-indent: 25px;
background-color: #fbfbea;
padding: 10px;
border-sty... | github_jupyter |
```
import sys, os
root_dir = '\\'.join(os.getcwd().split('\\')[:-1])
sys.path.append(root_dir)
from copy import deepcopy
from functools import reduce
from buildingBlocks.Synthesis import Chain
from buildingBlocks.Synthesis.Synthesizer import Synthesizer
from buildingBlocks.default.Tokens import Constant, Si... | github_jupyter |
# Wikipedia Text Generation (using RNN LSTM)
> - 🤖 See [full list of Machine Learning Experiments](https://github.com/trekhleb/machine-learning-experiments) on **GitHub**<br/><br/>
> - ▶️ **Interactive Demo**: [try this model and other machine learning experiments in action](https://trekhleb.github.io/machine-learnin... | github_jupyter |
# Training and Deploying the Fraud Detection Model
In this notebook, we will take the outputs from the Processing Job in the previous step and use it and train and deploy an XGBoost model. Our historic transaction dataset is initially comprised of data like timestamp, card number, and transaction amount and we enriche... | github_jupyter |
# Input Data Preparation
```
from glob import glob
from imageio import imread
from tqdm import tqdm
import tensorflow as tf
import json
import os.path as osp
import numpy as np
import numpy.random as npr
import cv2
import os
from tensorflow.contrib.learn.python.learn.datasets import base
class DetectionDataset(object)... | github_jupyter |
# Maximising the utility of an Open Address
Anthony Beck (GeoLytics), John Daniels (UU), Paul Williams (UU), Dave Pearson (UU), Matt Beare (Beare Essentials)
Go down for licence and other metadata about this presentation
... | github_jupyter |
[](https://colab.research.google.com/github/EliShayGH/deep-learning-v2-pytorch/blob/master/autoencoder/linear-autoencoder/Simple_Autoencoder_Exercise.ipynb)
# A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the... | github_jupyter |
```
import json
import os
import sys
import fnmatch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from collections import defaultdict
plt.style.use('fivethirtyeight')
WS_REAL = defaultdict(list)
valid_final_season = {}
FEAT = ['Age','WS']
for YR in ran... | github_jupyter |
### Install Dependencies
```
!pip install kaggle contractions
```
### Import Dependencies
```
import os
os.environ['KAGGLE_USERNAME'] = 'spyrosmouselinos'
os.environ['KAGGLE_KEY'] = 'a907fb69eab07900ccb6e1f2874fd343'
import re
import contractions
import numpy as np
import pandas as pd
import nltk
nltk.download('wo... | github_jupyter |
```
import requests
import pandas as pd
import tweepy
import json
import os
```
First, you will need to enable OAuth 2.0 in your App’s auth settings in the Developer Portal to get your client ID. You will also need your callback URL, which can be obtained from your App's auth settings.
```
%env CLIENT_ID your_client_... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
from scipy.signal import fftconvolve
from librosa.core import stft
from librosa.core import istft
from librosa import amplitude_to_db, db_to_amplitude
from librosa.display import specshow
from librosa.output import write_wav
from scip... | github_jupyter |
# Job Listings
```
# Dependencies & Setup
import pandas as pd
import numpy as np
import requests
import json
from os.path import exists
import simplejson as json
# Retrieve Google API Key from config.py
from config_3 import gkey
# File to Load
wc_file = "data/west_coast_job_listings.csv"
ba_file = "data/bay_area_job... | github_jupyter |
# Estatísticas de formatos de arquivo no censo de Diários Oficiais
Agora temos uma funcionalidade no site do [Censo](https://censo.ok.org.br/) que permite baixar os dados do mapeamento.
A partir desses dados, podemos encontrar analisar os formatos de arquivos que estão sendo utilizados nos diários oficiais e identific... | github_jupyter |
## Introduction to the Interstellar Medium
### Jonathan Williams
### Figure 6.15: simple model of heating and cooling in an HII region
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
%matplotlib inline
# scale wavelengths to energy via Hydrogen
E_IP = 13.6 # eV
lambda_IP = 91.2 ... | github_jupyter |
# Getting started with DoWhy: A simple example
This is a quick introduction to the DoWhy causal inference library.
We will load in a sample dataset and estimate the causal effect of a (pre-specified)treatment variable on a (pre-specified) outcome variable.
First, let us add the required path for Python to find the DoW... | github_jupyter |
# Módulos y bibliotecas en Python
Hasta ahora hemos desarrollado programas no muy complicados que cabían en una celda de un Notebook, pero poco a poco se va complicando más la cosa. ¿Qué ocurrirá cuando tengamos varias funciones definidas, datos declarados, Clases (lo veremos el p... | github_jupyter |
# Riddler Battle Royale
> [538's *The Riddler* Asks](http://fivethirtyeight.com/features/the-battle-for-riddler-nation-round-2/): *In a distant, war-torn land, there are 10 castles. There are two warlords: you and your archenemy, with whom you’re competing to collect the most victory points. Each castle has its own ... | github_jupyter |
# Using FFT to do convolution.
[Source code link from StackOverflow](https://stackoverflow.com/questions/40703751/using-fourier-transforms-to-do-convolution?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa)
```
import sys
from scipy import signal
from scipy import linalg
import numpy as np
x ... | github_jupyter |
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