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## Portfolio Exercise: Starbucks
<br>
<img src="https://opj.ca/wp-content/uploads/2018/02/New-Starbucks-Logo-1200x969.jpg" width="200" height="200">
<br>
<br>
#### Background Information
The dataset you will be provided in this portfolio exercise was originally used as a take-home assignment provided by Starbucks f... | github_jupyter |
[Index](Index.ipynb) - [Next](Widget List.ipynb)
# Simple Widget Introduction
## What are widgets?
Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox, etc.
## What can they be used for?
You can use widgets to build **interactive GUIs** for your ... | github_jupyter |
```
import numpy as np
from pandas import Series, DataFrame
import pandas as pd
from sklearn import preprocessing, tree
from sklearn.metrics import accuracy_score
# from sklearn.model_selection import train_test_split, KFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import KFold
d... | github_jupyter |
```
import warnings
import collections
import os
import pandas as pd # manage data
import pickle as pk # load and save python objects
import numpy as np # matrix operations
import matplotlib.pyplot as plt
import unidecode # Deal with codifications
import regex # use regular expresions
from email.header import Header, d... | github_jupyter |
```
from sklearn import *
from sklearn import datasets
from sklearn import linear_model
from sklearn import metrics
from sklearn import cross_validation
from sklearn import tree
from sklearn import neighbors
from sklearn import svm
from sklearn import ensemble
from sklearn import cluster
from sklearn import model_selec... | github_jupyter |
# Matplotlib
Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.
... | github_jupyter |
##### Copyright 2018 The TF-Agents 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 a... | github_jupyter |
# `Практикум по программированию на языке Python`
<br>
## `Занятие 2: Пользовательские и встроенные функции, итераторы и генераторы`
<br><br>
### `Мурат Апишев (mel-lain@yandex.ru)`
#### `Москва, 2021`
### `Функции range и enumerate`
```
r = range(2, 10, 3)
print(type(r))
for e in r:
print(e, end=' ')
for ind... | github_jupyter |
# Visualizing and Analyzing Jigsaw
```
import pandas as pd
import re
import numpy as np
```
In the previous section, we explored how to generate topics from a textual dataset using LDA. But how can this be used as an application?
Therefore, in this section, we will look into the possible ways to read the topics as ... | github_jupyter |
```
%pylab inline
import re
from pathlib import Path
import pandas as pd
import seaborn as sns
datdir = Path('data')
figdir = Path('plots')
figdir.mkdir(exist_ok=True)
mpl.rcParams.update({'figure.figsize': (2.5,1.75), 'figure.dpi': 300,
'axes.spines.right': False, 'axes.spines.top': False,
... | github_jupyter |
```
#uncomment this to install the library
# !pip3 install pygeohash
```
## Libraries and auxiliary functions
```
#load the libraries
from time import sleep
from kafka import KafkaConsumer
import datetime as dt
import pygeohash as pgh
#fuctions to check the location based on the geo hash (precision =5)
#function to c... | github_jupyter |
<a href="https://www.kaggle.com/aaroha33/text-summarization-attention-mechanism?scriptVersionId=85928705" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a>
<font size="+5" color=Green > <b> <center><u>
<br>Text Summarization
<b... | github_jupyter |
```
%matplotlib inline
```
# Brainstorm CTF phantom tutorial dataset
Here we compute the evoked from raw for the Brainstorm CTF phantom
tutorial dataset. For comparison, see [1]_ and:
http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomCtf
References
----------
.. [1] Tadel F, Baillet S, Mosher JC, Pantazis... | github_jupyter |
# Ensemble Learning
## Initial Imports
```
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from pathlib import Path
from collections import Counter
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import confusion_matrix
from imblearn.metrics import cla... | github_jupyter |
<a href="https://colab.research.google.com/github/olgOk/XanaduTraining/blob/master/Xanadu3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
pip install pennylane
pip install torch
pip install tensorflow
pip install sklearn
pip install pennylane-q... | github_jupyter |
# Regular Expressions
Regular expressions are text-matching patterns described with a formal syntax. You'll often hear regular expressions referred to as 'regex' or 'regexp' in conversation. Regular expressions can include a variety of rules, from finding repetition, to text-matching, and much more. As you advance in ... | github_jupyter |
# PTN Template
This notebook serves as a template for single dataset PTN experiments
It can be run on its own by setting STANDALONE to True (do a find for "STANDALONE" to see where)
But it is intended to be executed as part of a *papermill.py script. See any of the
experimentes with a papermill script to get sta... | github_jupyter |
# Recommender Systems 2018/19
### Practice 4 - Similarity with Cython
### Cython is a superset of Python, allowing you to use C-like operations and import C code. Cython files (.pyx) are compiled and support static typing.
```
import time
import numpy as np
```
### Let's implement something simple
```
def isPrime... | github_jupyter |
# 15 PDEs: Solution with Time Stepping
## Heat Equation
The **heat equation** can be derived from Fourier's law and energy conservation (see the [lecture notes on the heat equation (PDF)](https://github.com/ASU-CompMethodsPhysics-PHY494/PHY494-resources/blob/master/15_PDEs/15_PDEs_LectureNotes_HeatEquation.pdf))
$$
\... | github_jupyter |
# Build a sklearn Pipeline for a to ML contest submission
In the ML_coruse_train notebook we at first analyzed the housing dataset to gain statistical insights and then e.g. features added new,
replaced missing values and scaled the colums using pandas dataset methods.
In the following we will use sklearn [Pipelines](... | github_jupyter |
# Running the Direct Fidelity Estimation (DFE) algorithm
This example walks through the steps of running the direct fidelity estimation (DFE) algorithm as described in these two papers:
* Direct Fidelity Estimation from Few Pauli Measurements (https://arxiv.org/abs/1104.4695)
* Practical characterization of quantum ... | github_jupyter |
# Gujarati with CLTK
See how you can analyse your Gujarati texts with <b>CLTK</b> ! <br>
Let's begin by adding the `USER_PATH`..
```
import os
USER_PATH = os.path.expanduser('~')
```
In order to be able to download Gujarati texts from CLTK's Github repo, we will require an importer.
```
from cltk.corpus.utils.impor... | github_jupyter |
# Project 3: Implement SLAM
---
## Project Overview
In this project, you'll implement SLAM for robot that moves and senses in a 2 dimensional, grid world!
SLAM gives us a way to both localize a robot and build up a map of its environment as a robot moves and senses in real-time. This is an active area of research... | github_jupyter |
### In this notebook we investigate a designed simple Inception network on PDU data
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
```
### Importing the libraries
```
import torch
import torch.nn as nn
import torch.utils.data as Data
from torch.autograd import Function, Variable
from torch.optim impor... | github_jupyter |
### Import all needed package
```
import os
import ast
import numpy as np
import pandas as pd
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense, Activation, LSTM, Dropout
from keras.utils import to_categorical
from keras.datasets import mnist
from sklearn.preprocessing imp... | github_jupyter |
<a href="https://colab.research.google.com/github/tjido/woodgreen/blob/master/Woodgreen_Data_Science_%26_Python_Nov_2021_Week_3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<h1>Welcome to the Woodgreen Data Science & Python Program by Fireside An... | github_jupyter |
<h1>CREAZIONE MODELLO SARIMA REGIONE SARDEGNA
```
import pandas as pd
df = pd.read_csv('../../csv/regioni/sardegna.csv')
df.head()
df['DATA'] = pd.to_datetime(df['DATA'])
df.info()
df=df.set_index('DATA')
df.head()
```
<h3>Creazione serie storica dei decessi totali della regione Sardegna
```
ts = df.TOTALE
ts.head()... | github_jupyter |
```
##%overwritefile
##%file:src/compile_out_file.py
##%noruncode
def getCompout_filename(self,cflags,outfileflag,defoutfile):
outfile=''
binary_filename=defoutfile
index=0
for s in cflags:
if s.startswith(outfileflag):
if(len(s)>len(outfileflag)):
... | github_jupyter |
# Sentiment analysis with support vector machines
In this notebook, we will revisit a learning task that we encountered earlier in the course: predicting the *sentiment* (positive or negative) of a single sentence taken from a review of a movie, restaurant, or product. The data set consists of 3000 labeled sentences, ... | github_jupyter |
# Transfer Learning Template
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os, json, sys, time, random
import numpy as np
import torch
from torch.optim import Adam
from easydict import EasyDict
import matplotlib.pyplot as plt
from steves_models.steves_ptn import Steves_Prototypical_Network
... | github_jupyter |
# Logistic Regression on 'HEART DISEASE' Dataset
Elif Cansu YILDIZ
```
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import col, countDistinct
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler, MinMaxScaler, IndexToString
from pysp... | github_jupyter |
# 一个完整的机器学习项目
```
import os
import tarfile
import urllib
import pandas as pd
import numpy as np
from CategoricalEncoder import CategoricalEncoder
```
# 下载数据集
```
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = "../datasets/housing"
HOUSING_URL = DOWNLOAD_ROOT + HOUSING_... | github_jupyter |
# Recommending Movies: Retrieval
Real-world recommender systems are often composed of two stages:
1. The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this model is to efficiently weed out all candidates that the user is not i... | github_jupyter |
## Instructions
Please make a copy and rename it with your name (ex: Proj6_Ilmi_Yoon). All grading points should be explored in the notebook but some can be done in a separate pdf file.
*Graded questions will be listed with "Q:" followed by the corresponding points.*
You will be submitting **a pdf** file containin... | github_jupyter |
##### Copyright 2018 The TF-Agents 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 a... | github_jupyter |
## Querying Nexus knowledge graph using SPARQL
The goal of this notebook is to learn the basics of SPARQL. Only the READ part of SPARQL will be exposed.
## Prerequisites
This notebook assumes you've created a project within the AWS deployment of Nexus. If not follow the Blue Brain Nexus [Quick Start tutorial](https:... | github_jupyter |
```
import pandas as pd
import numpy as np
from tqdm import tqdm
tqdm.pandas()
import os, time, datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, f1_score, roc_curve, auc
import lightgbm as lgb
import xgboost as xgb
def format_time(elapsed):
'''
Takes a ti... | github_jupyter |
# Example usage of the O-C tools
## This example shows how to construct and fit with MCMC the O-C diagram of the RR Lyrae star OGLE-BLG-RRLYR-02950
### We start with importing some libraries
```
import numpy as np
import oc_tools as octs
```
### We read in the data, set the period used to construct the O-C diagram ... | github_jupyter |
```
# load libraries
import xarray as xr
import numpy as np
from argopy import DataFetcher as ArgoDataFetcher
from datetime import datetime, timedelta
import pandas as pd
# User defined functions:
def get_argo_region_data(llon,rlon,llat,ulat,depthmin,depthmax,time_in,time_f):
"""Function to get argo data fo... | github_jupyter |
# Consensus Optimization
This notebook contains the code for the toy experiment in the paper [The Numerics of GANs](https://arxiv.org/abs/1705.10461).
```
%load_ext autoreload
%autoreload 2
import tensorflow as tf
from tensorflow.contrib import slim
import numpy as np
import scipy as sp
from scipy import stats
from m... | github_jupyter |
# Getting Started with Tensorflow
```
import tensorflow as tf
# Create TensorFlow object called tensor
hello_constant = tf.constant('Hello World!')
with tf.Session() as sess:
# Run the tf.constant operation in the session
output = sess.run(hello_constant)
print(output);
A = tf.constant(1234)
B = tf.const... | github_jupyter |
<a href="https://colab.research.google.com/github/bhuwanupadhyay/codes/blob/main/ipynbs/reshape_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
pip install pydicom
# Import tensorflow
import logging
import tensorflow as tf
import keras.bac... | github_jupyter |
## 20 Sept 2019
<strong>RULES</strong><br>
<strong>Date:</strong> Level 2 heading ## <br>
<strong>Example Heading:</strong> Level 3 heading ###<br>
<strong>Method Heading:</strong> Level 4 heading ####
### References
1. [Forester_W._Isen;_J._Moura]_DSP_for_MATLAB_and_La Volume II(z-lib.org)
2. H. K. Dass, Advanced E... | github_jupyter |
# langages de script – Python
## Modules et packages
### M1 Ingénierie Multilingue – INaLCO
clement.plancq@ens.fr
Les modules et les packages permettent d'ajouter des fonctionnalités à Python
Un module est un fichier (```.py```) qui contient des fonctions et/ou des classes.
<small>Et de la documentation bien sû... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
EXPERIMENT = 'bivariate_power'
TAG = ''
df = pd.read_csv(f'./results/{EXPERIMENT}_results{TAG}.csv', sep=', ', engine='python')
plot_df = df
x_var_rename_dict = {
'sample_size': '# Samples',
'Number of environments... | github_jupyter |
```
### Python's Dir Function ###
def attributes_and_methods(inp):
print("The Attributes and Methods of a {} are:".format(type(inp)))
print(dir(inp))
# Change x to be any different type
# to get different results for that data type
x = 'abc'
attributes_and_methods(x)
### Contextmanager Example ###
# one way... | github_jupyter |
<img src="https://github.com/pmservice/ai-openscale-tutorials/raw/master/notebooks/images/banner.png" align="left" alt="banner">
# Working with Watson Machine Learning
This notebook should be run in a Watson Studio project, using **Default Python 3.7.x** runtime environment. **If you are viewing this in Watson Studio... | github_jupyter |
# This Notebook uses a Session Event Dataset from E-Commerce Website (https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store and https://rees46.com/) to build an Outlier Detection based on an Autoencoder.
```
import mlflow
import numpy as np
import os
import shutil
import pandas as pd
impor... | github_jupyter |
<a href="https://colab.research.google.com/github/rwarnung/datacrunch-notebooks/blob/master/dcrunch_R_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Data crunch example R script**
---
author: sweet-richard
date: Jan 30, 2022
require... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Terrain/srtm_mtpi.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" h... | github_jupyter |
#Introduction to Data Science
See [Lesson 1](https://www.udacity.com/course/intro-to-data-analysis--ud170)
You should run it in local Jupyter env as this notebook refers to local dataset
```
import unicodecsv
from datetime import datetime as dt
enrollments_filename = 'dataset/enrollments.csv'
engagement_filename = ... | github_jupyter |
```
import pandas as pd
import numpy as np
import keras
from keras.models import Sequential,Model
from keras.layers import Dense, Dropout,BatchNormalization,Input
from keras.optimizers import RMSprop
from keras.regularizers import l2,l1
from keras.optimizers import Adam
from sklearn.model_selection import LeaveOneOut
... | github_jupyter |
This notebook contains code for model comparison. Optimal hyperparameters for models are supposed to be already found.
# Imports
```
#imports
!pip install scipydirect
import math
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_... | github_jupyter |
# Apple and Tesla Split on 8/31
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import warnings
warnings.filterwarnings("ignore")
# for fetching data
import yfinance as yf
# input
# Coronavirus 2nd Wave
title = "Apple and Tesla"
symbols = ['AAPL', 'TSLA']
... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 6
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
ori=pd.read_csv('website_data_20190225.csv')
ori.drop(['STATE','DISTRICT','WLCODE... | github_jupyter |
# 2 Dead reckoning
*Dead reckoning* is a means of navigation that does not rely on external observations. Instead, a robot’s position is estimated by summing its incremental movements relative to a known starting point.
Estimates of the distance traversed are usually obtained from measuring how many times the wheels ... | github_jupyter |
## What this code does
In short, it is a reverse meme search, that identifies the source of the meme. It takes an image copypasta, extracts the individual *subimages* and compares it with a database of pictures (the database should be made up of copypastas, which is in TODO)
### TODO
### Clean up the code
There are m... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will use your trained model to generate captions for images in the test dataset.
This notebook **will be graded**.
Feel free to use the links below to navigate the notebook:
- [Step 1](#step1): Get Data Loader for Test Dataset
-... | github_jupyter |
# Purpose: To run the full segmentation using the best scored method from 2_compare_auto_to_manual_threshold
Date Created: January 7, 2022
Dates Edited: January 26, 2022 - changed the ogd severity study to be the otsu data as the yen data did not run on all samples.
*Step 1: Import Necessary Packages*
```
# import ... | github_jupyter |
```
# TensorFlow pix2pix implementation
from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
import os
import time
from matplotlib import pyplot as plt
from IPyt... | github_jupyter |
# Basic Workflow
```
# Always have your imports at the top
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.base import TransformerMixin
from hashlib import sha1 # just for grading purposes
import j... | github_jupyter |
# Lab 11: MLP -- exercise
# Understanding the training loop
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from random import randint
import utils
```
### Download the data and print the sizes
```
train_data=torch.load('../data/fashion-mnist/train_data.pt')
print... | github_jupyter |
```
# Copyright 2020 NVIDIA Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
## Main points
* Solution should be reasonably simple because the contest is only 24 hours long
* Metric is based on the prediction of clicked pictures one week ahead, so clicks are the most important information
* More recent information is more important
* Only pictures that were shown to a user could be clicked, so... | github_jupyter |
This challenge implements an instantiation of OTR based on AES block cipher with modified version 1.0. OTR, which stands for Offset Two-Round, is a blockcipher mode of operation to realize an authenticated encryption with associated data (see [[1]](#1)). AES-OTR algorithm is a campaign of CAESAR competition, it has suc... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import torch
from sklearn.model_selection import train_test_split
from sklearn.ensemble import IsolationForest
cubism_path = "/home/hopkinsl/Downloads/wikiart/wikiart/Cubism"
listdir = os.listdi... | github_jupyter |
### Easy string manipulation
```
x = 'a string'
y = "a string"
if x == y:
print("they are the same")
fox = "tHe qUICk bROWn fOx."
```
To convert the entire string into upper-case or lower-case, you can use the ``upper()`` or ``lower()`` methods respectively:
```
fox.upper()
fox.lower()
```
A common formatting n... | github_jupyter |
```
import re
import os
import keras.backend as K
import numpy as np
import pandas as pd
from keras import layers, models, utils
import json
def reset_everything():
import tensorflow as tf
%reset -f in out dhist
tf.reset_default_graph()
K.set_session(tf.InteractiveSession())
# Constants for our networks... | github_jupyter |
```
import os, sys
# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
import numpy as np
import imageio
import json
import random
import time
import pprint
import matplotlib.pyplot as plt
import run_nerf
from load... | github_jupyter |
```
from platform import python_version
import tensorflow as tf
print(tf.test.is_gpu_available())
print(python_version())
import os
import numpy as np
from os import listdir
from PIL import Image
import time
import tensorflow as tf
from tensorflow.keras import layers,models,optimizers
from keras import backend as K
im... | github_jupyter |
# AWS Marketplace Product Usage Demonstration - Algorithms
## Using Algorithm ARN with Amazon SageMaker APIs
This sample notebook demonstrates two new functionalities added to Amazon SageMaker:
1. Using an Algorithm ARN to run training jobs and use that result for inference
2. Using an AWS Marketplace product ARN - w... | 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 |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
os.chdir('/Users/gianni/Google Drive/Bas Zahy Gianni - Games/Data')
oc = [
'index', 'subject', 'color', 'gi', 'mi',
'status', 'bp', 'wp', 'response', 'rt',
'time', 'mouse_t', 'mouse_x'
]
fc = [
'subject', 'is_comp', 'color', 'status',
'bp', 'wp... | github_jupyter |
# Planar data classification with one hidden layer
Welcome to your week 3 programming assignment! It's time to build your first neural network, which will have one hidden layer. Now, you'll notice a big difference between this model and the one you implemented previously using logistic regression.
By the end of this ... | github_jupyter |
```
import numpy as np
import datetime
import matplotlib.pyplot as plt
from PIL import Image
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from numpy.linalg import norm
from sklearn.feature_extraction import image
import warnings
warnings.filterwarnings("ign... | github_jupyter |
# Solution Graded Exercise 1: Leaky-integrate-and-fire model
first name: Eve
last name: Rahbe
sciper: 235549
date: 21.03.2018
*Your teammate*
first name of your teammate: Antoine
last name of your teammate: Alleon
sciper of your teammate: 223333
Note: You are allowed to discuss the concepts with your class ma... | github_jupyter |
# Azure ML Training Pipeline for COVID-CXR
This notebook defines an Azure machine learning pipeline for a single training run and submits the pipeline as an experiment to be run on an Azure virtual machine.
```
# Import statements
import azureml.core
from azureml.core import Experiment
from azureml.core import Workspa... | github_jupyter |
# SIT742: Modern Data Science
**(Week 01: Programming Python)**
---
- 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/sit74... | github_jupyter |
# General Equilibrium
This notebook illustrates **how to solve GE equilibrium models**. The example is a simple one-asset model without nominal rigidities.
The notebook shows how to:
1. Solve for the **stationary equilibrium**.
2. Solve for (non-linear) **transition paths** using a relaxtion algorithm.
3. Solve for ... | github_jupyter |
# LeNet Lab
Source: Yan LeCun
## Load Data
Load the MNIST data, which comes pre-loaded with TensorFlow.
You do not need to modify this section.
```
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_... | github_jupyter |
```
import numpy as np
import scipy as sp
import scipy.interpolate
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats
import scipy.optimize
from scipy.optimize import curve_fit
import minkowskitools as mt
import importlib
importlib.reload(mt)
n=4000
rand_points = np.random.uniform(size=(2, n-2))
... | github_jupyter |
# Praca domowa 3
## Ładowanie podstawowych pakietów
```
import pandas as pd
import numpy as np
import sklearn
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.model_selection import StratifiedKFold # used in cr... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Implement depth-first traversals (in-order, pre-order, post-order) on a binary tree.
* [Constraints](#Co... | github_jupyter |
# Generative Adversarial Networks
Throughout most of this book, we've talked about how to make predictions.
In some form or another, we used deep neural networks learned mappings from data points to labels.
This kind of learning is called discriminative learning,
as in, we'd like to be able to discriminate between ph... | github_jupyter |
*This notebook is part of course materials for CS 345: Machine Learning Foundations and Practice at Colorado State University.
Original versions were created by Asa Ben-Hur.
The content is availabe [on GitHub](https://github.com/asabenhur/CS345).*
*The text is released under the [CC BY-SA license](https://creativecom... | github_jupyter |
# Lecture 3.3: Anomaly Detection
[**Lecture Slides**](https://docs.google.com/presentation/d/1_0Z5Pc5yHA8MyEBE8Fedq44a-DcNPoQM1WhJN93p-TI/edit?usp=sharing)
This lecture, we are going to use gaussian distributions to detect anomalies in our emoji faces dataset
**Learning goals:**
- Introduce an anomaly detection pro... | github_jupyter |
# Import Necessary Libraries
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.metrics import precision_score, recall_score
# display images
from IPy... | github_jupyter |
## Gaussian Transformation with Scikit-learn
Scikit-learn has recently released transformers to do Gaussian mappings as they call the variable transformations. The PowerTransformer allows to do Box-Cox and Yeo-Johnson transformation. With the FunctionTransformer, we can specify any function we want.
The transformers ... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))
from jupyterthemes import jtplot
jtplot.style()
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
import os
import json
import numpy a... | github_jupyter |
# In-Class Coding Lab: Iterations
The goals of this lab are to help you to understand:
- How loops work.
- The difference between definite and indefinite loops, and when to use each.
- How to build an indefinite loop with complex exit conditions.
- How to create a program from a complex idea.
# Understanding Iterati... | github_jupyter |
# 第8章: ニューラルネット
第6章で取り組んだニュース記事のカテゴリ分類を題材として，ニューラルネットワークでカテゴリ分類モデルを実装する．なお，この章ではPyTorch, TensorFlow, Chainerなどの機械学習プラットフォームを活用せよ．
## 70. 単語ベクトルの和による特徴量
***
問題50で構築した学習データ，検証データ，評価データを行列・ベクトルに変換したい．例えば，学習データについて，すべての事例$x_i$の特徴ベクトル$\boldsymbol{x}_i$を並べた行列$X$と正解ラベルを並べた行列（ベクトル）$Y$を作成したい．
$$
X = \begin{pmatrix}
\boldsy... | github_jupyter |
# Analyse a series
<div class="alert alert-block alert-warning">
<b>Under construction</b>
</div>
```
import os
import pandas as pd
from IPython.display import Image as DImage
from IPython.core.display import display, HTML
import series_details
# Plotly helps us make pretty charts
import plotly.offline as py
imp... | github_jupyter |
# VacationPy
----
#### Note
* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think throug... | github_jupyter |
# SLU07 - Regression with Linear Regression: Example notebook
# 1 - Writing linear models
In this section you have a few examples on how to implement simple and multiple linear models.
Let's start by implementing the following:
$$y = 1.25 + 5x$$
```
def first_linear_model(x):
"""
Implements y = 1.25 + 5*x
... | github_jupyter |
```
#importing libraries
import pandas as pd
import boto3
import json
import configparser
from botocore.exceptions import ClientError
import psycopg2
def config_parse_file():
"""
Parse the dwh.cfg configuration file
:return:
"""
global KEY, SECRET, DWH_CLUSTER_TYPE, DWH_NUM_NODES, \
DWH_NOD... | github_jupyter |
```
import os, sys
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
sys.path.append('../')
import argparse, json
from tqdm import tqdm_notebook as tqdm
import os.path as osp
from data.pointcloud_dataset import load_one_class_under_folder
from utils.dirs import mkdir_and_rename
from utils.tf import reset_tf_graph
opt = {
'd... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import os
import scipy.ndimage as ndi
import skimage.filters as fl
import warnings
from numpy import uint8, int64, float64, array, arange, zeros, zeros_like, ones, mean
from numpy.fft import fft, fft2, ifft, ifft2, fftshift
from math import log2
from scipy.ndimage ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tensorflow as tf
import os
from imageio import imwrite
from tqdm import tqdm
!ls imgs
# no need to resize yet
raw_img = tf.io.read_file('imgs/grundlsee_jesus.jpeg')
content_img = tf.image.decode_image(raw_img)[None, ...]
c... | github_jupyter |
# Task 4: Support Vector Machines
_All credit for the code examples of this notebook goes to the book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" by A. Geron. Modifications were made and text was added by K. Zoch in preparation for the hands-on sessions._
# Setup
First, import a few common modules, en... | github_jupyter |
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