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```
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA,TruncatedSVD,NMF
from sklearn.preprocessing import Normalizer
import argparse
import time
import pickle as pkl
def year_binner(year,val=10):
return year - year%val
def dim_reduction(df,rows):
df_svd = TruncatedSVD(n_components=300,... | github_jupyter |
# Neural Networks for Regression with TensorFlow
> Notebook demonstrates Neural Networks for Regression Problems with TensorFlow
- toc: true
- badges: true
- comments: true
- categories: [DeepLearning, NeuralNetworks, TensorFlow, Python, LinearRegression]
- image: images/nntensorflow.png
## Neural Network Regression... | github_jupyter |
# Análise de Dados com Python
Neste notebook, utilizaremos dados de automóveis para analisar a influência das características de um carro em seu preço, tentando posteriormente prever qual será o preço de venda de um carro. Utilizaremos como fonte de dados um arquivo .csv com dados já tratados em outro notebook. Caso ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/NAIP/ndwi.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="https://nbvi... | github_jupyter |
## Release the Kraken!
```
# The next library we're going to look at is called Kraken, which was developed by Université
# PSL in Paris. It's actually based on a slightly older code base, OCRopus. You can see how the
# flexible open-source licenses allow new ideas to grow by building upon older ideas. And, in
# this ... | github_jupyter |
```
# @title Copyright & License (click to expand)
# Copyright 2021 Google LLC
#
# 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 r... | github_jupyter |
RMinimum : Full - Test
```
import math
import random
import queue
```
Testfall : $X = [0, \cdots, n-1]$, $k$
```
# User input
n = 2**10
k = 2**5
# Automatic
X = [i for i in range(n)]
# Show Testcase
print(' Testcase: ')
print('=============================')
print('X = [0, ..., ' + str(n - 1) + '... | github_jupyter |
# Advanced topics
The following material is a deep-dive into Yangson, and is not necessarily representative of how one would perform manipulations in a production environment. Please refer to the other tutorials for a better picture of Rosetta's intended use. Keep in mind that the key feature of Yangson is to be abl... | github_jupyter |
# Planning Search Agent
Notebook version of the project [Implement a Planning Search](https://github.com/udacity/AIND-Planning) from [Udacity's Artificial Intelligence Nanodegree](https://www.udacity.com/course/artificial-intelligence-nanodegree--nd889) <br>
**Goal**: Solve deterministic logistics planning problems f... | github_jupyter |
```
import pandas as pd
df = pd.read_csv('queryset_CNN.csv')
print(df.shape)
print(df.dtypes)
preds = []
pred = []
for index, row in df.iterrows():
doc_id = row.doc_id
author_id = row.author_id
import ast
authorList = ast.literal_eval(row.authorList)
candidate = len(authorList)
algo ... | github_jupyter |
```
# -*- coding: utf-8 -*-
#
# 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, so... | github_jupyter |
# Boltzmann Machine
## Downloading the dataset
### ML-100K
```
# !wget "http://files.grouplens.org/datasets/movielens/ml-100k.zip"
# !unzip ml-100k.zip
# !ls
```
### ML-1M
```
# !wget "http://files.grouplens.org/datasets/movielens/ml-1m.zip"
# !unzip ml-1m.zip
# !ls
```
## Importing the libraries
```
import ... | github_jupyter |
<div style='background: #FF7B47; padding: 10px; border: thin solid darblue; border-radius: 5px; margin-bottom: 2vh'>
# Session 01 - Notebook
Like most session notebooks in this course, this notebook is divided into two parts. Part one is a 'manual' that will allow you to code along with the new code that we intro... | github_jupyter |
# Autobatching log-densities example
[](https://colab.sandbox.google.com/github/google/jax/blob/master/docs/notebooks/vmapped_log_probs.ipynb)
This notebook demonstrates a simple Bayesian inference example where autobatching makes user code eas... | github_jupyter |
<a href="https://colab.research.google.com/github/agungsantoso/deep-learning-v2-pytorch/blob/master/intro-to-pytorch/Part%201%20-%20Tensors%20in%20PyTorch%20(Exercises).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction to Deep Learning ... | github_jupyter |
## **University of Toronto - CSC413 - Neural Networks and Deep Learning**
## **Programming Assignment 4 - StyleGAN2-Ada**
This is a self-contained notebook that allows you to play around with a pre-trained StyleGAN2-Ada generator
Disclaimer: Some codes were borrowed from StyleGAN official documentation on Githu... | github_jupyter |
```
#all_slow
```
# Tutorial - Migrating from Lightning
> Incrementally adding fastai goodness to your Lightning training
We're going to use the MNIST training code from Lightning's 'Quick Start' (as at August 2020), converted to a module. See `migrating_lightning.py` for the Lightning code we are importing here.
`... | github_jupyter |
```
# Import Required Libraries
try:
import tensorflow as tf
import os
import random
import numpy as np
from tqdm import tqdm
from skimage.io import imread, imshow
from skimage.transform import resize
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
... | 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](h... | github_jupyter |
# Retail Demo Store Messaging Workshop - Amazon Pinpoint
In this workshop we will use [Amazon Pinpoint](https://aws.amazon.com/pinpoint/) to add the ability to dynamically send personalized messages to the customers of the Retail Demo Store. We'll build out the following use-cases.
- Send new users a welcome email af... | github_jupyter |
```
# !pip install graphviz
```
To produce the decision tree visualization you should install the graphviz package into your system:
https://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft
```
# Run one of these in case you have problems with graphviz... | github_jupyter |
## Interpreting Ensemble Compressed Features
**Gregory Way, 2019**
The following notebook will assign biological knowledge to the compressed features using the network projection approach. I use the model previously identified that was used to predict TP53 inactivation.
I observe the BioBombe gene set enrichment scor... | github_jupyter |
# Algoritmos de Otimização
No Deep Learning temos como propósito que nossas redes neurais aprendam a aproximar uma função de interesse, como o preço de casas numa regressão, ou a função que classifica objetos numa foto, no caso da classificação.
No último notebook, nós programos nossa primeira rede neural. Além disso... | github_jupyter |
# 07 - Serving predictions
The purpose of the notebook is to show how to use the deployed model for online and batch prediction.
The notebook covers the following tasks:
1. Test the `Endpoint` resource for online prediction.
2. Use the custom model uploaded as a `Model` resource for batch prediciton.
3. Run a the bat... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
# MNIST With SET
This is an example of training an SET network on the MNIST dataset using synapses, pytorch, and torchvision.
```
#Import torch libraries and get SETLayer from synapses
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, trans... | github_jupyter |
# Regression Errors
Let's talk about errors in regression problems. Typically, in regression, we have a variable $y$ for which we want to learn a model to predict. The prediction from the model is usually denoted as $\hat{y}$. The error $e$ is thus defined as follows
- $e = y - \hat{y}$
Since we have many pairs of t... | github_jupyter |
# UCI Dodgers dataset
```
import pandas as pd
import numpy as np
import os
from pathlib import Path
from config import data_raw_folder, data_processed_folder
from timeeval import Datasets
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (20, 10)
dataset_collection_name = "Dodgers"
so... | github_jupyter |
# Train a ready to use TensorFlow model with a simple pipeline
```
import os
import sys
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import matplotlib.pyplot as plt
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append("../..")
from batchflow impo... | github_jupyter |
```
%pip install bs4
%pip install lxml
%pip install nltk
%pip install textblob
import urllib.request as ur
from bs4 import BeautifulSoup
```
## STEP 1: Read data from HTML and parse it to clean string
```
#We would extract the abstract from this HTML page article
articleURL = "https://www.washingtonpost.com/news/the-... | github_jupyter |
# Just-in-time Compilation with [Numba](http://numba.pydata.org/)
## Numba is a JIT compiler which translates Python code in native machine language
* Using special decorators on Python functions Numba compiles them on the fly to machine code using LLVM
* Numba is compatible with Numpy arrays which are the basis of m... | github_jupyter |
```
import sys
sys.path.append('../input/shopee-competition-utils')
sys.path.insert(0,'../input/pytorch-image-models')
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
import a... | github_jupyter |
# 7. előadás
*Tartalom:* Függvények, pár további hasznos library (import from ... import ... as szintaktika, time, random, math, regex (regular expressions), os, sys)
### Függvények
Találkozhattunk már függvényekkel más programnyelvek kapcsán.
De valójában mik is azok a függvények? A függvények:
• újrahasználh... | github_jupyter |
<a href="https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/phase_diagram.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Copyright 2020 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
y... | github_jupyter |
### Genarating names with character-level RNN
In this notebook we are going to follow the previous notebook wher we classified name's nationalities based on a character level RNN. This time around we are going to generate names using character level RNN. Example: _given a nationality and three starting characters we w... | github_jupyter |
# Shashank V. Sonar
## Task 5: Exploratory Data Analysis - Sports
### Step -1: Importing the required Libraries
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
from sklearn.cluster import KMeans
from sklearn import datasets
import warnings
warnings... | github_jupyter |
# Random Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Independent Processes
The independence of random signals is ... | github_jupyter |
# Model Development V1
- This is really more like scratchwork
- Divide this into multiple notebooks for easier reading
**Reference**
- http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html
```
import json
import pickle
from pymongo import MongoClient
import numpy as np
import pandas as pd
from... | github_jupyter |
```
import numpy as np
import pandas as pd
from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau
from datetime import d... | github_jupyter |
```
from neo4j import GraphDatabase
import json
with open('credentials.json') as json_file:
credentials = json.load(json_file)
username = credentials['username']
pwd = credentials['password']
```
### NOTE ❣️
* BEFORE running this, still need to run `bin\neo4j console` to enable bolt on 127.0.0.1:7687
* When the ... | github_jupyter |
This notebook shows the MEP quickstart sample, which also exists as a non-notebook version at:
https://bitbucket.org/vitotap/python-spark-quickstart
It shows how to use Spark (http://spark.apache.org/) for distributed processing on the PROBA-V Mission Exploitation Platform. (https://proba-v-mep.esa.int/) The sample in... | github_jupyter |
```
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.model_selection import KFold
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import cross_val_score
%matplotlib i... | github_jupyter |
# Title
**Exercise: B.1 - MLP by Hand**
# Description
In this exercise, we will **construct a neural network** to classify 3 species of iris. The classification is based on 4 measurement predictor variables: sepal length & width, and petal length & width in the given dataset.
<img src="../img/image5.jpeg" style="wi... | github_jupyter |
# Code for Chapter 1.
In this case we will review some of the basic R functions and coding paradigms we will use throughout this book. This includes loading, viewing, and cleaning raw data; as well as some basic visualization. This specific case we will use data from reported UFO sightings to investigate what, if a... | github_jupyter |
# Attempting to load higher order ASPECT elements
An initial attempt at loading higher order element output from ASPECT.
The VTU files have elements with a VTU type of `VTK_LAGRANGE_HEXAHEDRON` (VTK ID number 72, https://vtk.org/doc/nightly/html/classvtkLagrangeHexahedron.html#details), corresponding to 2nd order (q... | github_jupyter |
# Индекс поиска
```
import numpy as np
import pandas as pd
import datetime
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
matplotlib.style.use('ggplot')
%matplotlib inline
```
### Описание:
Индекс строится на основе кризисных дескрипторов, взятых из [статьи Столбова.]... | github_jupyter |
```
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import random
import backwardcompatibilityml.loss as bcloss
import backwardcompatibilityml.scores as scores
# Initialize random seed
random.seed(123)
torch.manual_seed(... | github_jupyter |
```
import numpy as np
import os
from astropy.table import Table
from astropy.cosmology import FlatLambdaCDM
from matplotlib import pyplot as plt
from astropy.io import ascii
from astropy.coordinates import SkyCoord
import healpy
import astropy.units as u
import pandas as pd
import matplotlib
import pyccl
from scipy i... | github_jupyter |
```
from __future__ import print_function
import sys
import numpy as np
from time import time
import matplotlib.pyplot as plt
from tqdm import tqdm
import math
import struct
import binascii
sys.path.append('/home/xilinx')
from pynq import Overlay
from pynq import allocate
def float2bytes(fp):
packNo = struct.pac... | github_jupyter |
**NOTE: An version of this post is on the PyMC3 [examples](https://docs.pymc.io/notebooks/blackbox_external_likelihood.html) page.**
<!-- PELICAN_BEGIN_SUMMARY -->
[PyMC3](https://docs.pymc.io/index.html) is a great tool for doing Bayesian inference and parameter estimation. It has a load of [in-built probability dis... | github_jupyter |
# 📈 Panel HighMap Reference Guide
The [Panel](https://panel.holoviz.org) `HighMap` pane allows you to use the powerful [HighCharts](https://www.highcharts.com/) [Maps](https://www.... | github_jupyter |
# Classification metrics
Author: Geraldine Klarenberg
Based on the Google Machine Learning Crash Course
## Tresholds
In previous lessons, we have talked about using regression models to predict values. But sometimes we are interested in **classifying** things: "spam" vs "not spam", "bark" vs "not barking", etc.
Log... | github_jupyter |
# Multi-panel detector
The AGIPD detector, which is already in use at the SPB experiment, consists of 16 modules of 512×128 pixels each. Each module is further divided into 8 ASICs (application-specific integrated circuit).
<img src="AGIPD.png" width="300" align="left"/> <img src="agipd_geometry_14_1.png" width="420"... | github_jupyter |
tgb - 6/12/2021 - The goal is to see whether it would be possible to train a NN/MLR outputting results in quantile space while still penalizing them following the mean squared error in physical space.
tgb - 4/15/2021 - Recycling this notebook but fitting in percentile space (no scale_dict, use output in percentile uni... | github_jupyter |
```
import json
import glob
import re
import malaya
tokenizer = malaya.preprocessing._SocialTokenizer().tokenize
def is_number_regex(s):
if re.match("^\d+?\.\d+?$", s) is None:
return s.isdigit()
return True
def detect_money(word):
if word[:2] == 'rm' and is_number_regex(word[2:]):
return ... | github_jupyter |
```
image_shape = (56,64,1)
train_path = "D:\\Projects\\EYE_GAME\\eye_img\\datav2\\train\\"
test_path = "D:\\Projects\\EYE_GAME\\eye_img\\datav2\\test\\"
import os
import pandas as pd
from glob import glob
import numpy as np
import matplotlib as plt
from matplotlib.image import imread
import seaborn as sns
from te... | github_jupyter |
# ism Import and Plotting
This example shows how to measure an impedance spectrum and then plot it in Bode and Nyquist using the Python library [matplotlib](https://matplotlib.org/).
```
import sys
from thales_remote.connection import ThalesRemoteConnection
from thales_remote.script_wrapper import PotentiostatMode,Th... | github_jupyter |
#### Omega and Xi
To implement Graph SLAM, a matrix and a vector (omega and xi, respectively) are introduced. The matrix is square and labelled with all the robot poses (xi) and all the landmarks (Li). Every time you make an observation, for example, as you move between two poses by some distance `dx` and can relate t... | github_jupyter |
<a href="https://colab.research.google.com/github/bitprj/Bitcamp-DataSci/blob/master/Week1-Introduction-to-Python-_-NumPy/Intro_to_Python_plus_NumPy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img src="https://github.com/bitprj/Bitcamp-DataSci/... | github_jupyter |
Move current working directory, in case for developing the machine learning program by remote machine or it is fine not to use below single line.
```
%cd /tmp/pycharm_project_881
import numpy as np
import pandas as pd
def sigmoid(x):
return 1/(1+np.exp(-x))
def softmax(x):
x = x - x.max(axis=1, keepdims=True... | github_jupyter |
## Fashion Item Recognition with CNN
> Antonopoulos Ilias (p3352004) <br />
> Ndoja Silva (p3352017) <br />
> MSc Data Science AUEB
## Table of Contents
- [Data Loading](#Data-Loading)
- [Hyperparameter Tuning](#Hyperparameter-Tuning)
- [Model Selection](#Model-Selection)
- [Evaluation](#Evaluation)
```
import gc
i... | github_jupyter |
```
%matplotlib inline
# Packages
import os, glob, scipy, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Project directory
base_dir = os.path.realpath('..')
print(base_dir)
# Project-specific functions
funDir = os.path.join(base_dir,'Code/Functions')
print(funDir)
... | github_jupyter |
```
import sys
sys.path.append('../src')
from mcmc_norm_learning.algorithm_1_v4 import to_tuple
from mcmc_norm_learning.rules_4 import get_log_prob
from pickle_wrapper import unpickle
import pandas as pd
import yaml
import tqdm
from numpy import log
with open("../params_nc.yaml", 'r') as fd:
params = yaml.safe_loa... | github_jupyter |
# Data Management with OpenACC
This version of the lab is intended for C/C++ programmers. The Fortran version of this lab is available [here](../../Fortran/jupyter_notebook/openacc_fortran_lab2.ipynb).
You will receive a warning five minutes before the lab instance shuts down. Remember to save your work! If you are a... | github_jupyter |
```
import sys
sys.path.insert(1, '../functions')
import importlib
import numpy as np
import nbformat
import plotly.express
import plotly.express as px
import pandas as pd
import scipy.optimize as optimization
import food_bank_functions
import food_bank_bayesian
import matplotlib.pyplot as plt
import seaborn as sns
fro... | github_jupyter |
```
import sys
import keras
import tensorflow as tf
print('python version:', sys.version)
print('keras version:', keras.__version__)
print('tensorflow version:', tf.__version__)
```
# 6.3 Advanced use of recurrent neural networks
---
## A temperature-forecasting problem
### Inspecting the data of the Jena weather da... | github_jupyter |
# Improving Data Quality
**Learning Objectives**
1. Resolve missing values
2. Convert the Date feature column to a datetime format
3. Rename a feature column, remove a value from a feature column
4. Create one-hot encoding features
5. Understand temporal feature conversions
## Introduction
Recall that machine l... | github_jupyter |
# Books Recommender System
This is the second part of my project on Book Data Analysis and Recommendation Systems.
In my first notebook ([The Story of Book](https://www.kaggle.com/omarzaghlol/goodreads-1-the-story-of-book/)), I attempted... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import gaussian_kde, chi2, pearsonr
SMALL_SIZE = 16
MEDIUM_SIZE = 18
BIGGER_SIZE = 20
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsiz... | github_jupyter |
# WGAN
元論文 : Wasserstein GAN https://arxiv.org/abs/1701.07875 (2017)
WGANはGANのLossを変えることで、数学的に画像生成の学習を良くしよう!っていうもの。
通常のGANはKLDivergenceを使って、Generatorによる確率分布を、生成したい画像の生起分布に近づけていく。だが、KLDでは連続性が保証されないので、代わりにWasserstain距離を用いて、近似していこうというのがWGAN。
Wasserstain距離によるLossを実現するために、WGANのDiscriminatorでは最後にSigmoid関数を適用しない。つまり、Lossも... | github_jupyter |
```
#hide
# default_exp script
```
# Script - command line interfaces
> 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.
## Overview
Sometimes, you want to create a quick script, either for yourself, or for others. But in ... | github_jupyter |
# Image Processing Dense Array, JPEG, PNG
> In this post, we will cover the basics of working with images in Matplotlib, OpenCV and Keras.
- toc: true
- badges: true
- comments: true
- categories: [Image Processing, Computer Vision]
- image: images/freedom.png
Images are dense matrixes, and have a certain numbers of... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Unfairness Mitigation with Fairlearn and Azure Machine Learning
*... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
```
# Data Base Generation
### Basic Frame Capture
```
## This is just an example to ilustrate how to display video from webcam##
vid = cv2.VideoCapture(0) # define a video capture object
status = True # Initalize status
while(... | github_jupyter |
# CTW dataset tutorial (Part 1: basics)
Hello, welcome to the tutorial of _Chinese Text in the Wild_ (CTW) dataset. In this tutorial, we will show you:
1. [Basics](#CTW-dataset-tutorial-(Part-1:-Basics)
- [The structure of this repository](#The-structure-of-this-repository)
- [Dataset split](#Dataset-Split)
- ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os
import datetime
import numpy as np
import scipy
import pandas as pd
import torch
from torch import nn
import criscas
from criscas.utilities import create_directory, get_device, report_available_cuda_devices
from criscas.predict_model import *
base_dir = os.path.abspath('... | github_jupyter |
# Procedure for Word Correction Strategy as mentioned in Page 43 in the dissertation report
```
import numpy as np
import pandas as pd
import os
import nltk
import re
import string
from bs4 import BeautifulSoup
from spellchecker import SpellChecker
def read_file(df_new):
print("Started extracting data from file",d... | github_jupyter |
```
from extra import *
import keras
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras import regularizers
from keras.layers import Dense, Dropout, Conv2D, Input, GlobalAveragePooling2D, GlobalMaxPooling2D
from keras.layers import Add, Concatenate, BatchNormalization
import keras.b... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# a)
import sse
Lx, Ly = 8, 8
n_updates_measure = 10000
# b)
spins, op_string, bonds = sse.init_SSE_square(Lx, Ly)
for beta in [0.1, 1., 64.]:
op_string = sse.thermalize(spins, op_string, bonds, beta, n_updates_measure//10)
ns = sse.mea... | github_jupyter |
# Register Client and Create Access Token Notebook
- Find detailed information about client registration and access tokens in this blog post: [Authentication to SAS Viya: a couple of approaches](https://blogs.sas.com/content/sgf/2021/09/24/authentication-to-sas-viya/)
- Use the client_id to create an access token you c... | github_jupyter |
# Sklearn
# Визуализация данных
```
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as sts
import seaborn as sns
from contextlib import contextmanager
sns.set()
sns.set_style("whitegrid")
color_palette = sns.color_palette('deep') + sns.color_palette... | github_jupyter |
#Create the environment
```
from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/My Drive/ESoWC
import pandas as pd
import xarray as xr
import numpy as np
import pandas as pd
from sklearn import preprocessing
import seaborn as sns
#Our class
from create_dataset.make_dataset import CustomDa... | github_jupyter |
## Amazon SageMaker Feature Store: Client-side Encryption using AWS Encryption SDK
This notebook demonstrates how client-side encryption with SageMaker Feature Store is done using the [AWS Encryption SDK library](https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/introduction.html) to encrypt your data ... | github_jupyter |
# Strings
### **Splitting strings**
```
'a,b,c'.split(',')
latitude = '37.24N'
longitude = '-115.81W'
'Coordinates {0},{1}'.format(latitude,longitude)
f'Coordinates {latitude},{longitude}'
'{0},{1},{2}'.format(*('abc'))
coord = {"latitude":latitude,"longitude":longitude}
'Coordinates {latitude},{longitude}'.format(**... | github_jupyter |
# 9. Incorporating OD Veto Data
```
import sys
import os
import h5py
from collections import Counter
from progressbar import *
import re
import numpy as np
import h5py
from scipy import signal
import matplotlib
from repeating_classifier_training_utils import *
from functools import reduce
# Add the path to the parent... | github_jupyter |
###### Name: Deepak Vadithala
###### Course: MSc Data Science
###### Project Name: MOOC Recommender System
##### Notes:
This notebook contains the analysis of the **Google's Word2Vec** model. This model is trained on the news articles.
two variable **(Role and Skill Scores)** is used to predict the course category.
... | github_jupyter |
<a href="https://colab.research.google.com/github/sreyaschaithanya/football_analysis/blob/main/Football_1_Plotting_pass_and_shot.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#! git clone https://github.com/statsbomb/open-data.git
from google.... | github_jupyter |
```
%matplotlib inline
```
DCGAN Tutorial
==============
**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`__
Introduction
------------
This tutorial will give an introduction to DCGANs through an example. We
will train a generative adversarial network (GAN) to generate new
celebrities after showing it ... | github_jupyter |
# Extension Input Data Validation
When using extensions in Fugue, you may add input data validation logic inside your code. However, there is standard way to add your validation logic. Here is a simple example:
```
from typing import List, Dict, Any
# partitionby_has: a
# schema: a:int,ct:int
def get_count(df:List[D... | github_jupyter |
##### Copyright 2020 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 |
# Week 4
Yay! It's week 4. Today's we'll keep things light.
I've noticed that many of you are struggling a bit to keep up and still working on exercises from the previous weeks. Thus, this week we only have two components with no lectures and very little reading.
## Overview
* An exercise on visualizing geodata ... | github_jupyter |
# Inference with your model
This is the third and final tutorial of our [beginner tutorial series](https://github.com/awslabs/djl/tree/master/jupyter/tutorial) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to execute your image classifica... | 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](h... | github_jupyter |
```
# Copyright (c) 2020-2021 Adrian Georg Herrmann
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import interpolate
from sklearn.linear_model import LinearRegression
from datetime import datetime
data_root = "../../data"
locations = {
"berlin": ["52.4652025", "13.34... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Pre_data = pd.read_csv("C:\\Users\\2019A00303\\Desktop\\Code\\Airbnb Project\\Data\\PreProcessingAustralia.csv")
Pre_data
Pre_data['Price'].plot(kind='hist', bins=100)
Pre_data['group'] = pd.cut(x=Pre_data['Price'],
bins=[0, 50, 100, 150, 200, ... | github_jupyter |
# KNN(K Nearest Neighbours) for classification of glass types
We will make use of KNN algorithms to classify the type of glass.
### What is covered?
- About KNN algorithm
- Exploring dataset using visualization - scatterplot,pairplot, heatmap (correlation matrix).
- Feature scaling
- using KNN to predict
- Optimizati... | github_jupyter |
<a href="https://colab.research.google.com/github/krakowiakpawel9/machine-learning-bootcamp/blob/master/unsupervised/04_anomaly_detection/01_local_outlier_factor.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### scikit-learn
Strona biblioteki: [ht... | github_jupyter |
```
import numpy as np
import pandas as pd
import glob
import emcee
import corner
import scipy.stats
from scipy.ndimage import gaussian_filter1d
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KernelDensity
f... | github_jupyter |
# Initial data and problem exploration
```
import xarray as xr
import pandas as pd
import urllib.request
import numpy as np
from glob import glob
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import os
import cartopy.feature as cfeature
states_provinces = cfeature.NaturalEarthFeature(
category='cu... | github_jupyter |
# Descriptive analysis for the manuscript
Summarize geotagged tweets of the multiple regions used for the experiment and the application.
```
%load_ext autoreload
%autoreload 2
import os
import numpy as np
import pandas as pd
import yaml
import scipy.stats as stats
from tqdm import tqdm
def load_region_tweets(regio... | github_jupyter |
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