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**Introduction to Python**<br/>
Prof. Dr. Jan Kirenz <br/>
Hochschule der Medien Stuttgart
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Import-data" data-toc-modified-id="Import-data-1"><span class="toc-item-num">1 </span>Import data</a></... | github_jupyter |
## Series
```
import pandas as pd
import numpy as np
import random
first_series = pd.Series([1,2,3, np.nan ,"hello"])
first_series
series = pd.Series([1,2,3, np.nan ,"hello"], index = ['A','B','C','Unknown','String'])
series
#indexing the Series with custom values
dict = {"Python": "Fun", "C++": "Outdated","Coding":"H... | github_jupyter |
## Convolutional Neural Network Using SVM as Final Layer
```
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
config.gpu_options.allow_growth = True
session = InteractiveSession(config=confi... | github_jupyter |
# [Module 2.2] 세이지 메이커 인퍼런스
본 워크샵의 모든 노트북은 `conda_python3` 추가 패키지를 설치하고 모두 이 커널 에서 작업 합니다.
- 1. 배포 준비
- 2. 로컬 앤드포인트 생성
- 3. 로컬 추론
---
이전 노트북에서 인퍼런스 테스트를 완료한 티펙트를 가져옵니다.
```
%store -r artifact_path
```
# 1. 배포 준비
```
print("artifact_path: ", artifact_path)
import sagemaker
sagemaker_session = sagemaker.Session... | github_jupyter |
# IPython.display
youtube url for learning https://www.youtube.com/watch?v=YPgImo9kcbg&list=PLoTScYm9O0GFVfRk_MmZt0vQXNIi36LUz&index=12
```
from IPython.display import IFrame, YouTubeVideo, SVG, HTML
```
## Display Web page
```
IFrame("https://matplotlib.org/examples/color/named_colors.html", width=800, height=300)... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from plotnine import *
```
Leitura e visualização dos dados:
```
#carregar os dados no dataframe
df = pd.read_csv('movie_metadata.csv')
df.head()
df.shape
df.dtypes
list(df.columns)
```
Análise Exploratória
```
df['color'].value_counts()
... | github_jupyter |
# MNIST digit recognition Neural Network
---
# 1. Imports
---
```
import pandas as pd
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense
```
# 2. Understanding the data
---
## 2.1. Load the dataset and ... | github_jupyter |
# 使用PyNative进行神经网络的训练调试体验
[](https://gitee.com/mindspore/docs/blob/master/docs/notebook/mindspore_debugging_in_pynative_mode.ipynb)
## 概述
在神经网络训练过程中，数据是否按照自己设计的神经网络运行，是使用者非常关心的事情，如何去查看数据是怎样经过神经网络，并产生变化的呢？这时候需要AI框架提供一个功能，方便使用者将计算图中的... | github_jupyter |
```
import urllib.request
import json
import glob
import pandas as pd
import numpy as np
import datetime
```
Get data from sensors
```
# this cell gets data
URL = "http://165.227.244.213:8881/luftdatenGet/22FQ8dJEApww33p31935/9d93d9d8cv7js9sj4765s120sllkudp389cm/"
response = urllib.request.urlopen(URL)
data = json.l... | github_jupyter |
<a href="https://colab.research.google.com/github/RachitBansal/AppliancePower_TimeSeries/blob/master/ARIMA_Ukdale.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/drive',force_remount=True)
fro... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder, OrdinalEncoder
from sklearn.pipeline import make_pipeline
from category_en... | github_jupyter |
<a href="https://colab.research.google.com/github/AlsoSprachZarathushtra/Quick-Draw-Recognition/blob/master/(3_1)Stroke_LSTM_Skatch_A_Net_ipynb_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Connect Google Drive
```
from google.colab import dri... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
# Desafio 5
Neste desafio, vamos praticar sobre redução de dimensionalidade com PCA e seleção de variáveis com RFE. Utilizaremos o _data set_ [Fifa 2019](https://www.kaggle.com/karangadiya/fifa19), contendo originalmente 89 variáveis de mais de 18 mil jogadores do _game_ FIFA 2019.
> Obs.: Por favor, não modifique o ... | github_jupyter |
```
import sys
import numpy as np # linear algebra
from scipy.stats import randint
import matplotlib.pyplot as plt # this is used for the plot the graph
%matplotlib inline
from tqdm import notebook
import tensorflow as tf
from scipy import stats
from scipy.interpolate import interp1d
```
### Simulate data
```
np.ra... | github_jupyter |
```
# 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 applicable law or agreed to in writing, software
# distributed und... | github_jupyter |
```
! rm visualising_the_results/*
```
# Visualising the results
In this tutorial, we demonstrate the plotting tools built-in to `bilby` and how to extend them. First, we run a simple injection study and return the `result` object.
```
import bilby
import matplotlib.pyplot as plt
%matplotlib inline
time_duration = ... | github_jupyter |
# Arbitrarily high order accurate explicit time integration methods
1. Chapter 5: ADER and DeC
1. [Section 1.1: DeC](#DeC)
1. [Section 1.2: ADER](#ADER)
## Deferred Correction (Defect correction/ Spectral deferred correction)<a id='DeC'></a>
Acronyms: DeC, DEC, DC, SDC
References: [Dutt et al. 2000](https:/... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
# Data Retriving and Pre-processing
**Importing Libraries**
```
# ALL THE IMPORTS NECESSARY
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from geopy.distance import great_circle as vc
import math as Math
```
**Retriving Dat... | github_jupyter |
```
import numpy as np
import sklearn
import os
import pandas as pd
import scipy
from sklearn.linear_model import LinearRegression
import sklearn
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
from torchvision import datasets, transforms
import copy
#!pi... | github_jupyter |
# Accumulation Distribution Line (ADL)
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:accumulation_distribution_line
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# yfinance is used to fetch data
import yfi... | github_jupyter |
# Plagiarism Detection, Feature Engineering
In this project, you will be tasked with building a plagiarism detector that examines an answer text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided, source text.
Your first ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
warnings.filterwarnings('ignore')
```
## Introduction
```
from IPython.display import YouTubeVideo
YouTubeVideo(id="BYOK12I9vgI", width="100%")
```
In this chapter, we will look at bipartite grap... | github_jupyter |
```
# Internal python libraries
import numpy as np
import matplotlib.pyplot as plt
# Esto controla el tamaño de las figuras en el script
plt.rcParams['figure.figsize'] = (10, 10)
import ipywidgets as ipw
from ipywidgets import widgets, interact_manual
from IPython.display import Image
# Esto es para poder correr to... | github_jupyter |
# Predict Model
The aim of this notebook is to assess how well our [logistic regression classifier](../models/LR.csv) generalizes to unseen data. We will accomplish this by using the Matthew's Correlation Coefficient (MCC) to evaluate it's predictive performance on the test set. Following this, we will determine which... | github_jupyter |
```
from kbc_pul.project_info import project_dir as kbc_e_metrics_project_dir
import os
from typing import List, Dict, Set, Optional
import numpy as np
import pandas as pd
from artificial_bias_experiments.evaluation.confidence_comparison.df_utils import ColumnNamesInfo
from artificial_bias_experiments.known_prop_sc... | github_jupyter |
```
!pip install google_images_download
#Imports
import tensorflow as tf
import keras
from google.colab import drive
import os
from fastai.vision import *
from fastai.metrics import error_rate
import re
from google_images_download import google_images_download
# Start off with Mounting Drive Locally
drive.mount('/conte... | github_jupyter |
```
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
The raw code for this IPython notebook is by default hidden for easier rea... | github_jupyter |
```
# %gui qt
import numpy as np
import mne
import pickle
import sys
import os
# import matplotlib
from multiprocessing import Pool
from tqdm import tqdm
import matplotlib.pyplot as plt
# import vispy
# print(vispy.sys_info())
# BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# sys.path.append(B... | github_jupyter |
# Cross Industry Standart Process for Data Mining
In this section we are going to analise Boston AIRBNB Data Set. We are looking to help people on cleaning datasets e how to deal with some especific data. In this post we are going to cover all the subjects bellow:
1. Business Understanding: Understand the problem
2... | github_jupyter |
# Hyperparameter Optimization [xgboost](https://github.com/dmlc/xgboost)
What the options there're for tuning?
* [GridSearch](http://scikit-learn.org/stable/modules/grid_search.html)
* [RandomizedSearch](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html)
All right!
Xgboost h... | github_jupyter |
##### Copyright 2020 The TensorFlow Hub 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... | github_jupyter |
# Open, Re-usable Deep Learning Components on the Web
## Learning objectives
- Use [ImJoy](https://imjoy.io/#/) web-based imaging components
- Create a JavaScript-based ImJoy plugin
- Create a Python-based ImJoy plugin
*See also:* the [I2K 2020 Tutorial: ImJoying Interactive Bioimage Analysis
with Deep Learning, Im... | github_jupyter |
# MULTI-LABEL TEXT CLASSIFICATION FOR STACK OVERFLOW TAG PREDICTION
```
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClas... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel('results fdu.xlsx')
df2 = pd.read_excel('data trait category II 10 Mar 2021 all data - plain text.xlsx')
df2.columns
mean_N = df2.groupby(['Y_category2'])['Code'].count()
mean_N
mean_ = df2.groupby(['Y_category2','design.1']... | github_jupyter |
# Autonomous driving - Car detection
Welcome to your week 3 programming assignment. You will learn about 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 Farhadi, 2016 (htt... | github_jupyter |
## Modules
```
from sklearn import metrics
import scikitplot as skplt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model, decomposition, datasets
from... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# BSSN Time-Evolution Equations for the Gauge Fields $\alph... | github_jupyter |
<a href="https://colab.research.google.com/github/Aditya-Singla/Banknote-Authentication/blob/master/Banknote_authentication.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Importing the libraries**
```
import pandas as pd
import numpy as np
```
... | github_jupyter |
# A Char-RNN Implementation in Tensorflow
*This notebook is slightly modified from https://colab.research.google.com/drive/13Vr3PrDg7cc4OZ3W2-grLSVSf0RJYWzb, with the following changes:*
* Main parameters defined at the start instead of middle
* Run all works, because of the added upload_custom_data parameter
* Traini... | github_jupyter |
# Simulations
In this notebook we will show four methods for incorporating new simulations into Coba in order of easy to hard:
1. From an Openml.org dataset with **OpenmlSimulation**
2. From local data sets with **CsvSimulation**, **ArffSimulation**, **LibsvmSimulation**, and **ManikSimulation**.
3. From Python functio... | 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 |
(tune-mnist-keras)=
# Using Keras & TensorFlow with Tune
```{image} /images/tf_keras_logo.jpeg
:align: center
:alt: Keras & TensorFlow Logo
:height: 120px
:target: https://keras.io
```
```{contents}
:backlinks: none
:local: true
```
## Example
```
import argparse
import os
from filelock import FileLock
from tenso... | github_jupyter |
# Proper randomization is important...
I changed the code of the MPI calibration code to do a global randomization (and not a randomization within each operation).
```
import os
import numpy
import pandas
from extract_archive import extract_zip, aggregate_dataframe
archive_names = {'nancy_2018-07-24_1621460.zip' : 'n... | github_jupyter |
#### Implementation of Distributional paper for 1-dimensional games, such as Cartpole.
- https://arxiv.org/abs/1707.06887
<br>
Please note: The 2 dimensional image state requires a lot of memory capacity (~50GB) due to the buffer size of 1,000,000 as in DQN paper.
So, one might want to train an a... | github_jupyter |
#Document retrieval from wikipedia data
#Fire up GraphLab Create
```
import graphlab
```
#Load some text data - from wikipedia, pages on people
```
people = graphlab.SFrame('people_wiki.gl/')
```
Data contains: link to wikipedia article, name of person, text of article.
```
people.head()
len(people)
```
#Explor... | github_jupyter |
# Bootstrap
## Import and settings
In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`.
```
import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
print([np.__version__, pd.__version__, graph... | github_jupyter |
```
# HIDDEN
from datascience import *
from prob140 import *
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
from scipy import stats
```
## Sums of IID Samples ##
After the dry, algebraic discussion of the previous section it is a relief to finally be able to com... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import dataframe_image as dfi
import matplotlib.ticker
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
sns.set()
matplotlib.rcParams['figure.f... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import os
import n... | github_jupyter |
<img align="right" src="images/tf-small.png" width="128"/>
<img align="right" src="images/phblogo.png" width="128"/>
<img align="right" src="images/dans.png"/>
---
Start with [convert](https://nbviewer.jupyter.org/github/annotation/banks/blob/master/programs/convert.ipynb)
---
# Getting data from online repos
We sh... | github_jupyter |
```
#hide
from fastscript.core import *
```
# fastscript
> A fast way to turn your python function into a script.
Part of [fast.ai](https://www.fast.ai)'s toolkit for delightful developer experiences. Written by Jeremy Howard.
## Install
`pip install fastscript`
## Overview
Sometimes, you want to create a quick ... | github_jupyter |
# Text Mining DocSouth Slave Narrative Archive
---
*Note:* This is the first in [a series of documents and notebooks](https://jeddobson.github.io/textmining-docsouth/) that will document and evaluate various machine learning and text mining tools for use in literary studies. These notebooks form the practical and crit... | github_jupyter |
This dataset is derived from [Kaggle Website](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews/downloads/imdb-dataset-of-50k-movie-reviews.zip/1)!
-------------------------------------------
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
imp... | github_jupyter |
# Predict Fraud transaction
1. In this document, I will use the transaction data, which include details about each transaction, to predict whether this transaction is fraud or not. The model will be able to applied to future transaction data.
2. I will visualize the data and perform data cleaning before building the fr... | github_jupyter |
**Training a RNN to synthesize English text character by character**
Herein I have trained a vanilla RNN with outputs using the text from the book The Globlet of Fire by J.K. Rowling.*
The following implementation will train a recurrent neural network (RNN) that shows how the evolution of the text synthesized by my ... | github_jupyter |
```
%matplotlib inline
```
# Use source space morphing
This example shows how to use source space morphing (as opposed to
SourceEstimate morphing) to create data that can be compared between
subjects.
<div class="alert alert-danger"><h4>Warning</h4><p>Source space morphing will likely lead to source spaces that ar... | github_jupyter |
# MLP Classification with SUBJ Dataset
<hr>
We will build a text classification model using MLP model on the SUBJ Dataset. Since there is no standard train/test split for this dataset, we will use 10-Fold Cross Validation (CV).
## Load the library
```
import tensorflow as tf
import pandas as pd
import numpy as np
i... | github_jupyter |
## install prerequisite
```
from utility.preprocessing1 import processing,load_pickle,get_augmentaion,train_test_split
from models.model1 import padding,train_model,load_model,infer,DiagnosisDataset
DATA_SIZE=10000
BASE_PATH=f'data/{DATA_SIZE}'
FILE = f"{BASE_PATH}/AdmissionsDiagnosesCorePopulatedTable.txt"
```
## Ru... | github_jupyter |
```
!pip install exoplanet
import exoplanet as xo
exoplanet.utils.docs_setup()
print(f"exoplanet.__version__ = '{exoplanet.__version__}'")
!pip install lightkurve
import numpy as np
import lightkurve as lk
import matplotlib.pyplot as plt
from astropy.io import fits
#1 Download TPF
lc_file = lk.search_lightcurve('WAS... | github_jupyter |
## 1. The NIST Special Publication 800-63B
<p>If you – 50 years ago – needed to come up with a secret password you were probably part of a secret espionage organization or (more likely) you were pretending to be a spy when playing as a kid. Today, many of us are forced to come up with new passwords <em>all the time</em... | 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 |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Get-information-from-GFF-file" data-toc-modified-id="Get-information-from-GFF-file-1"><span class="toc-item-num">1 </span>Get information from GFF file</a></span><ul class="toc-item"><li><span><a... | github_jupyter |
```
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import torch
from torch import optim
from torch.autograd import Variable
import numpy as np
import os
import math
from torch import nn
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import itertools
i... | github_jupyter |
# Facial Keypoint Detection
This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces. The first step in any challenge like this will be to load and visualize the data you'll be working ... | github_jupyter |
# Simple Use Cases
Simulus is a discrete-event simulator in Python. This document is to demonstrate how to run simulus via a few examples. This is not a tutorial. For that, use [Simulus Tutorial](simulus-tutorial.ipynb). All the examples shown in this guide can be found under the `examples/demos` directory in the simu... | 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 |
<a href="https://colab.research.google.com/github/hBar2013/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments/blob/master/module2-intermediate-linear-algebra/Kim_Lowry_Intermediate_Linear_Algebra_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"... | github_jupyter |
```
%matplotlib inline
import adaptive
import matplotlib.pyplot as plt
import pycqed as pq
import numpy as np
from pycqed.measurement import measurement_control
import pycqed.measurement.detector_functions as det
from qcodes import station
station = station.Station()
```
## Setting up the mock device
Measurements are... | github_jupyter |
```
import numpy as np
import pandas as pd
import time
import psutil
import matplotlib.pyplot as plt
import numpy as np
# We create a very simple data set with 5 data items in it.
size= 5
# mu, sigma = 100, 5000 # mean and standard deviation
# error=np.random.normal(mu, sigma, size)
x1 = np.arange(0, size)
# x2 = n... | github_jupyter |
## Purpose: Get the stats for pitching per year (1876-2019).
```
# import dependencies.
import time
import pandas as pd
from splinter import Browser
from bs4 import BeautifulSoup as bs
!which chromedriver
# set up driver.
executable_path = {"executable_path": "/usr/local/bin/chromedriver"}
browser = Browser("chrome", ... | github_jupyter |
<div class="alert alert-block alert-info">
<font size="6"><b><center> Section 2</font></center>
<br>
<font size="6"><b><center> Fully-Connected, Feed-Forward Neural Network Examples </font></center>
</div>
# Example 1: A feedforward network with one hidden layer using torch.nn and simulated data
In developing (and tr... | github_jupyter |
# Convolution Nets for MNIST
### TelescopeUser: 10-class classification problem
<img src="imgs/mnist_plot.png"
style="float: left; margin-right: 1px;" width="500" height="400" />
Deep Learning models can take quite a bit of time to run, particularly if GPU isn't used.
In the interest of time, you could sample... | github_jupyter |
# Exploring datastructures for dataset
A Pandas exploration. Find the best datastructure to explore and transform the dataset (both training and test dataframes). Use case:
- find all numerical features (filtering)
- transform all numerical features (e.g. take square)
- replace NaN values for a numerical feature
- plot... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
**Jupyter** allows you to write and run Python code through an interactive web browser interface.
Each Jupyter **notebook** is a series of **cells** that can have Python code or text.
The cell below contains Python code to carry out some simple arithmatic. You can run the code by selecting the cell and holding _shift... | github_jupyter |
## 10.4 딥러닝 기반 Q-Learning을 이용하는 강화학습
- 관련 패키지 불러오기
```
# 기본 패키지
import numpy as np
import random
from collections import deque
import matplotlib.pyplot as plt
# 강화학습 환경 패키지
import gym
# 인공지능 패키지: 텐서플로, 케라스
# 호환성을 위해 텐스플로에 포함된 케라스를 불러옴
import tensorflow as tf # v2.4.1 at 7/25/2021
from tensorflow import keras # v2.4... | github_jupyter |
#1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
```
!pip install git+https://github.com/google/starthinker
```
#2. Get Cloud Project ID
To run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/mast... | github_jupyter |
```
import time
import pandas as pd
import numpy as np
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.metrics import mean_squared_error
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
fr... | github_jupyter |
# **Swin Transformer: Hierarchical Vision Transformer using Shifted Windows**
**Swin Transformer (ICCV 2021 best paper award (Marr Prize))**
**Authors {v-zeliu1,v-yutlin,yuecao,hanhu,v-yixwe,zhez,stevelin,bainguo}@microsoft.com**
**Official Github**: https://github.com/microsoft/Swin-Transformer
---
**Edited By Su... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
import pickle
import numpy as np
import pandas as pd
import skimage.io as io
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
import keras
from keras.applications import ResNet50
from keras.applications.resnet50 import preprocess_input
fr... | github_jupyter |
```
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/husein/t5/prepare/mesolitica-tpu.json'
os.environ['CUDA_VISIBLE_DEVICES'] = ''
from bigbird import modeling
from bigbird import utils
import tensorflow as tf
import numpy as np
import sentencepiece as spm
vocab = '/home/husein/b2b/sp10m.cased.t5.mode... | github_jupyter |
# Objective
Import the FAF freight matrices provided with FAF into AequilibraE's matrix format
## Input data
* FAF: https://faf.ornl.gov/fafweb/
* Matrices: https://faf.ornl.gov/fafweb/Data/FAF4.4_HiLoForecasts.zip
* Zones System: http://www.census.gov/econ/cfs/AboutGeographyFiles/CFS_AREA_shapefile_010215.zip
* FAF ... | github_jupyter |
## House Prices: Advanced Regression Techniques : Kaggle Competition
### Import Libraries
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```
### Import Data
```
test_df=pd.read_csv('test.csv')
test_df.head()
test_df.shape
```
### Step1: Check for missing values
``... | github_jupyter |
## Introduction to Tasks with States
Task might be run for a single set of input values or we can generate multiple sets, that will be called "states". If we want to run our `Task` multiple times we have to provide input that is iterable and specify the way we want to map values of the inputs to the specific states. I... | github_jupyter |
```
import jax.numpy as jnp
from jax import jit, grad, jvp, random
from jax.scipy.stats import multivariate_normal as mvn
from jax.scipy.stats import norm
from scipy.optimize import minimize, NonlinearConstraint
from itertools import product
from jax.config import config
config.update('jax_enable_x64', True)
import ... | github_jupyter |
<a href="https://colab.research.google.com/github/Bluelord/ML_Mastery_Python/blob/main/06_Feature_Selection.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Feature Selction
---
```
from google.colab import drive
drive.mount('/content/drive')
```... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
import os
import timm
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import torch.nn as nn
#define variables specific to this model
subject = 'sub01'
roi = 'F... | github_jupyter |
```
%matplotlib widget
import os
import sys
sys.path.insert(0, os.getenv('HOME')+'/pycode/MscThesis/')
import pandas as pd
from amftrack.util import get_dates_datetime, get_dirname, get_plate_number, get_postion_number,get_begin_index
import ast
from amftrack.plotutil import plot_t_tp1
from scipy import sparse
fro... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.metrics import c... | github_jupyter |
# Analysing the Stroop Effect
## Introduction
The aim of this project was to investigate a classic phenomenon from experimental psychology called the Stroop Effect. The Stroop Effect is a demonstration of interference in the reaction time of a task. The Stroop task investigated for this project was a list of congru... | github_jupyter |
```
import tensorflow as tf
from bayes_tec.bayes_opt.maximum_likelihood_tec import *
import numpy as np
float_type = tf.float64
def test_solve():
import numpy as np
from seaborn import jointplot
import pylab as plt
plt.style.use('ggplot')
freqs = np.linspace(120e6,160e6,20)
tec_conversion ... | github_jupyter |
<a href="https://colab.research.google.com/github/strangelycutlemon/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling/blob/master/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/... | github_jupyter |
# Data Labelling Analysis (DLA) Dataset C
```
#import libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os
print('Libraries imported!!')
#define directory of functions and actual directory
HOME_PATH = '' #home path of the proj... | github_jupyter |
### Problem Statement
Given a linked list with integer data, arrange the elements in such a manner that all nodes with even numbers are placed after odd numbers. **Do not create any new nodes and avoid using any other data structure. The relative order of even and odd elements must not change.**
**Example:**
* `link... | github_jupyter |
# Validation
This notebook contains examples of some of the simulations that have been used to validate Disimpy's functionality by comparing the simulated signals to analytical solutions and signals generated by other simulators. Here, we simulate free diffusion and restricted diffusion inside cylinders and spheres.
... | github_jupyter |
```
!wget -q https://raw.githubusercontent.com/mannefedov/compling_nlp_hse_course/master/data/zhivago.txt
!ls -lh
import re
import string
from collections import Counter
import razdel
import nltk
import rusenttokenize
from pymystem3 import Mystem
from pymorphy2 import MorphAnalyzer
from nltk.stem.snowball import Sno... | github_jupyter |
## Dependencies
```
import json, warnings, shutil
from jigsaw_utility_scripts import *
from transformers import TFXLMRobertaModel, XLMRobertaConfig
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses, layers
from tensorflow.keras.callbacks import EarlyStopping, ModelCheck... | github_jupyter |
# MovieLens
##DUE APRIL 21, 2016
[MovieLens](http://www.movielens.org/) is a website where users can submit ratings for movies that they watch and receive recommendations for other movies they might enjoy. The data is collected and made publicly available for research. We will be working with a data set of 1 million ... | github_jupyter |
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