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# Turning Points and Inflection Points
This example will walk the user through implementing DCF fits to data sets with turning points and inflection points. It builds on the details in the 'Simple Example Code' and uses the 'constraints' keyword argument introduced there. The 'constraints' keyword argument is used to ... | 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 a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression.
**You will learn how to:**
- Implemen... | github_jupyter |
# 6. Weight Initializations & Activation Functions
## Recap of Logistic Regression
<img src="./images/cross_entropy_final_4.png" alt="deeplearningwizard" style="width: 900px;"/>
## Recap of Feedforward Neural Network Activation Function
<img src="./images/logistic_regression_comparison_nn5.png" alt="deeplearningwizar... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/abhigoogol/Autoencoders-using-Pytorch/blob/master/Autoencoder_CIFAR10,_Denoising_MNIST.ipynb)
```
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_ab... | github_jupyter |
# Generating Useful Wikidata Files
This notebook generates files that contain derived data that is useful in many applications. The input to the notebook is the full Wikidata or a subset of Wikidata. It also works for arbitrary KGs as long as they follow the representation requirements of Wikidata:
- the *instance of... | github_jupyter |
<img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="35%" align="right" border="0"><br>
# Python for Finance
**Analyze Big Financial Data**
O'Reilly (2014)
Yves Hilpisch
<img style="border:0px solid grey;" src="http://hilpisch.com/python_for_finance.png" alt="Python for Finance" width="30%" a... | github_jupyter |
# 範例 : 計程車費率預測
https://www.kaggle.com/c/new-york-city-taxi-fare-prediction
# [作業目標]
- 使用並觀察特徵組合, 在計程車費率預測競賽的影響
# [作業重點]
- 仿造範例並參考今日課程內容, 使用經緯度一圈的長度比的概念造出新特徵, 觀察有什麼影響 (In[6], Out[6])
- 只使用上面所造的這個新特徵, 觀察有什麼影響 (In[7], Out[7])
```
# 做完特徵工程前的所有準備
import pandas as pd
import numpy as np
import datetime
from sklearn.preproc... | github_jupyter |
##### Copyright 2020 The TensorFlow IO 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 |
```
import os
import numpy as np
import pandas as pd
% run FeatureTrace.ipynb
def getFeatureProfiles(filePaths):
featureObj = FeatureTrace
featureProfiles = dict()
for f in filePaths:
if not os.path.isdir(f):
fName = os.path.basename(f)
# featureProfile = featureObj(f).getOri... | github_jupyter |
# Interpret Models
You can use Azure Machine Learning to interpret a model by using an *explainer* that quantifies the amount of influence each feature contribues to the predicted label. There are many common explainers, each suitable for different kinds of modeling algorithm; but the basic approach to using them is t... | github_jupyter |
# AI Explanations: Explaining a tabular data model
## Overview
In this tutorial we will perform the following steps:
1. Build and train a Keras model.
1. Export the Keras model as a TF 1 SavedModel and deploy the model on Cloud AI Platform.
1. Compute explainations for our model's predictions using Explainable AI on... | github_jupyter |
# 신경망 성능 개선
신경망의 예측 성능 및 수렴 성능을 개선하기 위해서는 다음과 같은 추가적인 고려를 해야 한다.
* 오차(목적) 함수 개선: cross-entropy cost function
* 정규화: regularization
* 가중치 초기값: weight initialization
* Softmax 출력
* Activation 함수 선택: hyper-tangent and ReLu
## 기울기와 수렴 속도 문제
일반적으로 사용하는 잔차 제곱합(sum of square) 형태의 오차 함수는 대부분의 경우에 기울기 값이 0 이므로 (near-zero gr... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Distributed CNTK using custom docker images
In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training.
## Prerequisites
* Unde... | github_jupyter |
<!-- dom:TITLE: Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning and convolutional networks -->
# Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning and convolutional networks
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Departmen... | github_jupyter |
# Assignment 6 - Boosting
## Before you begin
Remember to:
1. Make your own copy of the notebook by pressing the "Copy to drive" button.
2. Expend all cells by pressing **Ctrl+[**
### Your IDs
✍️ Fill in your IDs in the cell below:
```
## %%%%%%%%%%%%%%% Your code here - Begin %%%%%%%%%%%%%%%
## Fill in your IDs ... | github_jupyter |
```
#G4
from google.colab import drive
drive.mount('/content/gdrive')
cp gdrive/My\ Drive/fingerspelling5.tar.bz2 fingerspelling5.tar.bz2
!tar xjf fingerspelling5.tar.bz2
cd dataset5
#remove depth files
import glob
import os
import shutil
# get parts of image's path
def get_image_parts(image_path):
"""Given a ful... | github_jupyter |
```
from googleapiclient.discovery import build # для получения информации по API
import petl as etl # для загрузки и обработки данных
import pandas as pd # для выгрузки таблицы в postgresql
import sqlalchemy # для создания подключения к базе данных
import re # регулярные выражения
youTubeApiKey = 'BIrandomsimbolsandno... | github_jupyter |
```
import RegTomoRecon as rtr
import numpy as np
%matplotlib notebook
from matplotlib import pyplot as plt
```
## Import data and tilt series
In this example we simulate data from a simple blob phantom for demonstrative purposes. Here we use the axis ordering etc. which is default in astra. The following example give... | github_jupyter |
# Lab 2: Classification
### Machine Learning 1, September 2015
* The lab exercises should be made in groups of two, three or four people.
* The deadline is October 4th (Sunday) 23:59.
* Assignment should be sent to Philip Versteeg (p.j.j.p.versteeg@uva.nl). The subject line of your email should be "lab\#\_lastname1\_... | github_jupyter |
<img src="images/ipython_logo.png">
# Jupyter and IPython overview
In this day, we will cover the core parts of Jupyter and IPython, including how to use the various frontends, the Jupyter notebook, and how the IPython kernel goes beyond the plain Python language.
**Jupyter** includes the notebook interface and vari... | github_jupyter |
<a href="https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_9_2_GAN_Optimization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install livelossplot --quiet
#@title The Imports
from tensorflow.keras.da... | github_jupyter |
## Creating a composite image from multiple PlanetScope scenes
In this exercise, you'll learn how to create a composite image (or mosaic) from multiple PlanetScope satellite images that cover an area of interest (AOI). We'll use `rasterio`, along with its vector-data counterpart `fiona`, to do this.
### Step 1. Aquir... | github_jupyter |
# 1D harmonic oscillator physics-informed neural network (PINN)
This notebook contains the code to reproduce the plots presented in my blog post ["So, what is a physics-informed neural network?"](https://benmoseley.blog/my-research/so-what-is-a-physics-informed-neural-network/).
Please read the post for more details... | 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 |
# 3.5 Dictionaries
## 3.5.5 Dictionarys vergleichen & sortieren
Im Anschluss dieser Übungseinheit kannst du ...
+ Dictionarys nach ihren Keys, Values und Key-Value-Paaren auf verschiedene Weisen miteinander vergleichen
+ Dictionarys in Sets umwandeln und damit Set-Funktionen zum Vergleich von Dictionarys heranziehen... | github_jupyter |
# Python Parsing with NLTK
**(C) 2017-2021 by [Damir Cavar](http://damir.cavar.me/)**
**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-notebooks).
**License:** [Creative Commons Attribution-ShareAlike 4.0 International License](https... | github_jupyter |
```
import numpy as np
import pandas as pd
from PIL import Image, ImageEnhance
import torchvision.transforms as transforms
import os
from tqdm import tqdm
# the folder from 256_ObjectCategories.tar file
train_dir = '/home/ubuntu/data/256_ObjectCategories/'
# a folder where resized and split data will be stored
data_... | github_jupyter |
# Introducción
En este documento vamos a expandir una serie de consultas utilizando dos modelos basados en word embeddings: Word2Vec$^{[1]}$ y Glove$^{[2]}$. Al principio, se explicará brevemente como funciona cada uno de estos modelos y sus respectivas librerías de Python. Posteriormente, se realizará una pequeña comp... | github_jupyter |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.eager import context
from tensorflow.python.framework import common_shapes
from tensorflow.python... | github_jupyter |
### Pandas
Pandas is a Python library which makes working with large datasets convenient; it utilizes an object called a DataFrame, which stores data in a CSV like format. A Pandas DataFrame allows you to perform Exploratory Data Analysis and run various models for both supervised and unsupervised learning.
```
impor... | github_jupyter |
# Setup
```
import sys
import os
import re
import collections
import itertools
import bcolz
import pickle
sys.path.append('../../lib')
sys.path.append('../')
import numpy as np
import pandas as pd
import gc
import random
import smart_open
import h5py
import csv
import json
import functools
import time
import string
... | github_jupyter |
```
from notebook.services.config import ConfigManager
cm = ConfigManager()
cm.update('livereveal', {
'width': 1024,
'height': 768,
'scroll': True,
})
import pandas as pd
import pylab as plt
import pystan
import seaborn as sns
import numpy as np
from matplotlib.transforms import Transform
from m... | github_jupyter |
# teex
### Generating image data with g.t. saliency maps
Let's explore the 'seneca' method for generating artificial images with available ground truth saliency maps. It was presented in [Evaluating local explanation methods on ground truth, Riccardo Guidotti, 2021](https://www.researchgate.net/publication/346916247_... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
# EvolvePy Example 4 - Logger
In this example, we will show how to user Loggers to store all optimization history.
- MemoryStoreLogger
- FileLogger
- Wandblogger
```
import evolvepy
import numpy as np
from matplotlib import pyplot as plt
```
# Fitness function, Generator
We will use the same fitness function and ... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
```
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
from matplotlib.pyplot import plot as plt
torch.manual_seed(1)
from data import *
import cleaningtool as ct
from helpers import *
from model impor... | github_jupyter |
# Train Models
The central goal of machine learning is to train predictive models that can be used by applications. In Azure Machine Learning, you can use scripts to train models leveraging common machine learning frameworks like Scikit-Learn, Tensorflow, PyTorch, SparkML, and others. You can run these training scrip... | github_jupyter |
# Face Deblurring
Trained an End-to-End model for deblurring of images (CelebA) following the work in CNN For Direct Text Deblurring, using Keras. The first layer filter size is adjusted to be approximately equal to the blur kernel size. Pre-Trained model with weights and some images from test set are uploaded.
**Imp... | github_jupyter |
# Quantile Regression Q-Learning
## Imports
```
import gym
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from IPython.display import clear_output
from matplotlib import pyplot as plt
%matplotlib inline
from timeit import default_timer as timer
fro... | github_jupyter |
<a href="https://colab.research.google.com/github/pra17dod/Waste-Segregation/blob/main/model/v3_waste_segregation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os... | github_jupyter |
# Kili Tutorial: Importing assets
In this tutorial, we will walk through the process of using Kili to import assets. The goal of this tutorial is to illustrate some basic components and concepts of Kili in a simple way.
Additionally:
For an overview of Kili, visit https://kili-technology.com. You can also check out ... | github_jupyter |
# Quickstart: Use Cases and Examples with *Interpretable OPE Evaluator*
This notebook demonstrates an example of conducting Interpretable Evaluation for Off-Policy Evaluation (IEOE). We use synthetic logged bandit feedback data generated using [`obp`](https://github.com/st-tech/zr-obp) and evaluate the performance of ... | github_jupyter |
# XRF Monte-carlo simulations
```
from spectrocrunch.materials import multilayer
from spectrocrunch.materials import compoundfromformula
from spectrocrunch.materials import compoundfromdb
from spectrocrunch.materials import compoundfromlist
from spectrocrunch.materials import element
from spectrocrunch.materials impor... | github_jupyter |
# **The Effect of Psychology on B12**
#### İrem Dereli
This project aims to detect a person's B12 level by looking him/her psychology and mental health.
## **Table of Content**
[Problem](#problem)
[Data Understanding](#data_understanding)
[Data Preparation](#data_preparation)
[Modeling](#modeling)
[Eval... | github_jupyter |
<h1 style="font-size:35px;color:deeppink;"> Data Analysis -Case Study 3</h1>
In this case study we will take data of job postings.This case study focuses on wrangling the data and then answer a simple question. Dta present in this is both dirty and untidy
# Step 1: Asking Questions
Q1)How much of the population requi... | github_jupyter |
# Extract indications in clinical trial from clinicaltrials.gov
+ [documentation](https://clinicaltrials.gov/ct2/help/how-read-study "How to read a study record")
```
import bz2
import collections
import itertools
import os
import random
import re
import urllib.parse
import urllib.request
import xml.etree.ElementTree... | github_jupyter |
<a href="https://colab.research.google.com/github/aamini/introtodeeplearning/blob/master/lab3/RL.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<table align="center">
<td align="center"><a target="_blank" href="http://introtodeeplearning.com">
... | github_jupyter |
# Cross Validation
1. [What is Cross Validation](#1)
1. [Methods used for Cross-Validation](#2)
1. [Validation Set Approach](#3)
1. [Leave-P-out cross-validation](#4)
1. [K-Fold Cross-Validation](#5)
1. [Stratified k-fold cross-validation](#6)
1. [Holdout Method](#7)
1. [Comparison of Cross-validation to train/test sp... | github_jupyter |
# Riskfolio-Lib Tutorial:
<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__
<br>__[Orenji](https://www.orenj-i.com)__
<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__
<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__
## Part IV: Bond Portfolio Optimization and Im... | github_jupyter |
```
import pandas as pd
import sklearn.datasets
import numpy as np
import matplotlib.pyplot as plt
from os import environ
environ['TF_FORCE_GPU_ALLOW_GROWTH'] = "true"
from utils import hello_world
hello_world()
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import... | github_jupyter |
# First Order Ego Graph Analysis on Facebook User 2
In this notebook we analyze the first order egograph of a Facebook account with $\sim10^3$ friends.
## Modules
```
# Enable interactive numpy and matplotlib
%pylab inline
# Data Wrangling
import pandas as pd
import numpy as np
# Data Analysis
import powerlaw as ... | github_jupyter |
<a href="https://colab.research.google.com/github/https-deeplearning-ai/tensorflow-1-public/blob/master/C3/W4/ungraded_labs/C3_W4_Lab_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Ungraded Lab: Generating Text with Neural Networks
For this we... | github_jupyter |
# Python Machine Learning
# Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-Learn
### Overview
- [Decision tree learning](#Decision-tree-learning)
- [Maximizing information gain – getting the most bang for the buck](#Maximizing-information-gain-–-getting-the-most-bang-for-the-buck)
- [Buildin... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# %matplotlib inline é uma Magic Function do Jupyter. Sem ela, teríamos que chamar plt.show() ao fim de cada plot.
```
# Matplotlib
## O que é?
Não tem como a explicação ser melhor do que a que está no próprio site, mas basicamente é uma bib... | github_jupyter |
# Convolutional Networks
So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art resu... | github_jupyter |
```
#from ssm.models import SLDS
#from ssm.emissions import GaussianIdentityEmissions
#from ssm.variational import SLDSMeanFieldVariationalPosterior, SLDSTriDiagVariationalPosterior
import ssm
import numpy as np
import scipy.io
#from pybasicbayes.util.text import progprint_xrange
import matplotlib.pyplot as plt
from ss... | github_jupyter |
```
import sys,os
PROJECT_ROOT=r"C:\Users\Matteo\Documents\projects\sdd\sdd_sports_scraper"
sys.path.insert(0, PROJECT_ROOT)
os.environ['PROJECT_ROOT']=PROJECT_ROOT
import pandas as pd
from common import sql_utils
from matplotlib import pyplot as plt
pd.options.display.max_columns=999
%matplotlib inline
matchups=pd.rea... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")
plt.grid(True)
!wget --no-check-certificate \
... | github_jupyter |
## MLS Case study: Unsupervised Learning
----------------------------------------
Welcome to the case study on Unsupervised Learning. We will be using the Credit Card Customer Data for this case study.
----------------------------
## Problem Statement:
-----------------------------
AllLife Bank wants to focus on its... | github_jupyter |
# Compare k-mer abundance and presence/absence vs "traditional" single-cell RNA-seq processing
- 500 k-mers, with abundance, were hashed from ~50k cells in Tabula Muris using `sourmash`
## Load Tabula Muris Senis data with fixed annotations
```
import dask.dataframe as dd
import numpy as np
import pandas as pd
impo... | github_jupyter |
# MLP vs. CNN image classification
## Import data and packages
```
from tensorflow.keras import datasets, layers, models, callbacks
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
(x_train, y_train), (x_test, y_test) = datasets.fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trou... | github_jupyter |
<a id="intro_ID"></a>
# Intermediate: Search and Download GI Program Light Curves
## Introduction
This notebook uses the MAST Portal's advanced search options to retrieve the light curves for a single guest investigator program. The notbook will show how to do an advanced query on the MAST's database of holdings, de... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="width:80px;height:98px;padding-right:20px;" src="https://raw.githubusercontent.com/joe-papa/pytorch-book/main/files/pytorch-book-cover.jpg">
This notebook contains an excerpt from the [PyTorch Pocket Reference](http://pytorchbook.com) book by [Joe Papa](http://joepapa.a... | github_jupyter |
### Setup
```
from __future__ import print_function
import numpy as np
import time
import matplotlib.pyplot as plt
import tensorflow as tf
import sys
sys.path.append('..')
import models.VAE as vae
import os
from io import BytesIO
import PIL.Image
import scipy.misc
import scipy.io
from IPython.display import clear_o... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
# Aprendizaje supervisado parte 2 -- Regresión
En regresión intentamos predecir una variable continua de salida -- al contrario que las variables nominales que predecíamos en los ejemplos anteriores de clasificación.
Vamos a empezar con un... | github_jupyter |
```
import sys
import warnings
if not sys.warnoptions:
warnings.simplefilter('ignore')
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from datetime import datetime
from datetime import timedelta
fro... | github_jupyter |
```
import numpy as np
from scipy import io
from scipy import sparse
from scipy.sparse import csgraph
from scipy import fftpack
from scipy import signal
from scipy import linalg
from matplotlib import pyplot as plt
from matplotlib import collections
import mpl_toolkits.mplot3d.art3d as art3d
import seaborn as sns
impor... | github_jupyter |
```
import numpy as np
import os
import tensorflow as tf
import keras
from keras import optimizers
from keras.layers import Input
from keras.models import Model
from keras.layers import Dense, Flatten, Reshape, Dropout
from keras.layers import Convolution1D, MaxPooling1D, BatchNormalization
from keras.layers import Lam... | github_jupyter |
# Gaussian Naive Bayes Classifier with Normalizer
This Code template is for Classification task using Gaussian Naive Bayes Algorithm where the scaling technique used is Normalize.
### Required Packages
```
!pip install imblearn
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as p... | github_jupyter |
# Aplicação: cores PANTONE
## Leitura de arquivos _json_
```
import os, json
# diretório base
base = '../database/pantone-colors/'
for fi in os.listdir(base):
n,e = os.path.splitext(fi)
if e == '.json':
with open(os.path.join(base,fi), 'r') as f:
# define variáveis dinamicamente
... | github_jupyter |
```
sc.addPyFile('/local/path/to/sb/soft-boiled.zip')
from src.algorithms import slp, gmm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
# Raw Data Sources
```
data_path = 'hdfs:///post_etl_datasets/twitter/year=2015'
all_tweets = sqlCtx.read.parquet(data_path)
all_tweets.registerTempTable(... | github_jupyter |
# Classifying Fashion-MNIST
Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 9... | github_jupyter |
# Laboratório de Biomecânica e Controle Motor
## [BMClab](http://demotu.org)@[UFABC](http://www.ufabc.edu.br/): Why, How, What For?
<br>
<div class='center-align'><figure><img src="http://demotu.org/wp-content/uploads/2016/05/cropped-BMClab0.png" alt="BMClab image header"/></figure></div>
```
from datetime import dat... | github_jupyter |
# Part 2 - Data Analysis
For this project, the requirement is to use the flights dataset to predict if a particular flight in the future will be cancelled. This first notebook is used to explore the data.
**NOTE -** This notebook will only execute if you have run the project with `STORAGE_MODE == external` and if you... | github_jupyter |
<center><img src='img/ms_logo.jpeg' height=40% width=40%></center>
<center><h1>Support Vector Machines</h1></center>
In this notebook, we'll cover one of the major algorithms used in Supervised Learning--**_Support Vector Machines_** (or _SVMs_ for short!). We'll start by playing around with a visual implementation t... | github_jupyter |
# Introduction to Altair
[Altair](https://altair-viz.github.io/) is a declarative statistical visualization library for Python. Altair offers a powerful and concise visualization grammar for quickly building a wide range of statistical graphics.
By *declarative*, we mean that you can provide a high-level specificatio... | github_jupyter |
# Convolutional Neural Networks: Step by Step
Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.
**Notation**:
- Superscript $[l]$ denotes an object of the $l... | github_jupyter |
```
import gym
import torch
from src.Learner.AWAC import AWAC
from src.Learner.DQN import DQN
from src.Learner.Random import DiscreteRandomAgent
from src.nn.MLP import MLP
from src.utils.memory import ReplayMemory
from src.utils.train_utils import prepare_training_inputs
import matplotlib.pyplot as plt
gamma = 0.9
me... | github_jupyter |
This is a notebook with all experiments in the DEDPUL paper on synthetic data sets
```
import numpy as np
import pandas as pd
import random
import torch.nn as nn
import torch.optim as optim
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplo... | github_jupyter |
# DAFI's Random Field Module
This document shows the use of DAFI's random field module *dafi.random_field* to work with random fields. Particularly, the examples show how to generate samples and how to perform a modal decomposition.
```
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
f... | github_jupyter |
# 02 - Exploring Preprocessing
Data preprocessing is a set of activities performed to prepare data for future analysis and data mining activities.
## Load data from file
The dataset used in this tutorial is GeoLife GPS Trajectories. Available in https://www.microsoft.com/en-us/download/details.aspx?id=52367
```
fro... | github_jupyter |
<img src="../../img/logo_amds.png" alt="Logo" style="width: 128px;"/>
# AmsterdamUMCdb - Freely Accessible ICU Database
version 1.0.2 March 2020
Copyright © 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science
# Vasopressors and inotropes
Shows medication for artificially increasing blood pressure (vasopr... | github_jupyter |
```
%pylab inline
%load_ext autoreload
%autoreload 2
import tensorflow as tf
import numpy as np
import DifferentiableHOS as DHOS
import flowpm
import pickle
import flowpm.tfpower as tfpower
import flowpm.scipy.interpolate as interpolate
from flowpm.tfpower import linear_matter_power
import jax
from flowpm import tfpm
... | github_jupyter |
```
import numpy as np
import pandas as pd
data_path = "dataset/winequality-red.csv"
wine_df = pd.read_csv(data_path)
wine_df.head()
#print the shape
wine_df.shape
wine_df.count()
wine_df.describe()
wine_df.info()
#check for missing values
wine_df.isna().sum()
#draw box for all columns plot for checking distribution an... | github_jupyter |
<small><small><i>
All the IPython Notebooks in this lecture series by Dr. Milan Parmar are available @ **[GitHub](https://github.com/milaan9/04_Python_Functions)**
</i></small></small>
# Python `global` Keyword
In this class, you’ll learn about the **`global`** keyword, global variable and when to use **`global`** ke... | github_jupyter |
# Data Driven Dealings Development
* EDA on Sales Data
* Feature Engineering and Clustering
* Predicting Sales
* Market Basket
* Recommending Items per Customer
```
# To be able to use your data stored in your Google Drive you first need to mount your Google Drive so you can load and save files to it.
fro... | github_jupyter |
## Updating Editor Tracking Data in a Hosted Feature Service (Online)
#### This example is an adaptation from the blog post: https://community.esri.com/people/smoore-esristaff/blog/2019/03/21/updating-editor-tracking-data-in-a-hosted-feature-service-online
#### This example was developed for a specific user use case ... | github_jupyter |
<div style="color:#303030;font-family:'arial blACK', sans-serif,monospace; text-align: center; padding: 50px 0; vertical-align:middle;" > <img src="https://github.com/PIA-Group/ScientIST-notebooks/blob/master/_Resources/Images/Lightbulb.png?raw=true" style=" background:linear-gradient(to right,#FDC86E,#fbb144);border-... | github_jupyter |
<a href="https://colab.research.google.com/github/aTom-Pie/dw_matrix/blob/master/matrix_1_day5.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install eli5
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeRegress... | github_jupyter |
```
# !wget https://raw.githubusercontent.com/huseinzol05/Malaya-Dataset/master/dependency/gsd-ud-train.conllu.txt
# !wget https://raw.githubusercontent.com/huseinzol05/Malaya-Dataset/master/dependency/gsd-ud-test.conllu.txt
# !wget https://raw.githubusercontent.com/huseinzol05/Malaya-Dataset/master/dependency/gsd-ud-d... | github_jupyter |
<a href="https://colab.research.google.com/github/ChielChiel/TextToMySpeech/blob/master/textToMySpeech.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# IMPORT
---
```
pip install pydub
from bs4 import BeautifulSoup
from IPython.display import Aud... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# reading csv file
athlete_events = pd.read_csv('../CSV for ML models/athlete_events.csv')
athlete_events.head()
fi... | github_jupyter |
# Depixelizing Pixel Art using Deep Neural Networks
---
- Author: Diego Inácio
- GitHub: [github.com/diegoinacio](https://github.com/diegoinacio)
- Notebook: [pixel-art-depixelization-deepNN.ipynb](https://github.com/diegoinacio/creative-coding-notebooks/blob/master/ML-and-AI/pixel-art-depixelization-deepNN.ipynb)
---
... | github_jupyter |
【ThisNotebook】
[`Local`](./03_arrays.ipynb)
[`Github`](https://github.com/RenyuanL/ElementsOfDataScience/blob/master/03_arrays.ipynb)
[`Colab`](https://colab.research.google.com/github/RenyuanL/ElementsOfDataScience/blob/master/03_arrays.ipynb)
# Lists and arrays
Elements of Data Science
by [Allen Downey](https://al... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_2_lstm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 10: Time Series in Keras*... | github_jupyter |
```
# %load ../start.py
# Imports
import os
import sys
from pathlib import Path
import re
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
# Project level imports
sys.path.insert(0, '../../lib')
from larval_gonad.notebook import Nb
from larval_gona... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import espaloma as esp
import torch
import numpy as np
from simtk import unit
GAS_CONSTANT = 8.31446261815324 * unit.joule / (unit.kelvin * unit.mole)
GAS_CONSTANT = GAS_CONSTANT.value_in_unit(
esp.units.ENERGY_UNIT / (unit.kelvin)
)
kT = GAS_CONSTANT * 300
WINDOWS = 50
def le... | github_jupyter |
# Ethan's Modeling
```
import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso, Ridge, LinearRegression, ElasticNet
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.impute import KNNImputer
from sklearn.metrics import mean_squared_er... | github_jupyter |
# Sparseloop Tutorial - 02 - Matrix Multiply
This notebook contains a series of examples of a **matrix multiply** computation. The **fibertree** emulator is used to illustrate the impact of a set of optimizations to exploit sparsity. The basic computation is represented by the Einsum:
$$ Z_{m,n} = A_{m,k} \times B_{k... | github_jupyter |
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