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# DAE 4 Descriptive Statistics
Author:
- |
Sofia Dahl, sof@create.aau.dk\
Dept. Architecture, Design and Media Technology, Aalborg University Copenhagen
---
## Learning goals
After working your way through this notebook you should be able to..
- Explain what is meant by 'population' and 'sample'
- Plot and sum... | github_jupyter |
# SSD300 Training Tutorial
This tutorial explains how to train an SSD300 on the Pascal VOC datasets. The preset parameters reproduce the training of the original SSD300 "07+12" model. Training SSD512 works simiarly, so there's no extra tutorial for that. The same goes for training on other datasets.
You can find a su... | github_jupyter |
<img src="../../../images/qiskit_header.png" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" align="middle">
# Pulse Schedules
The `pulse` module allows quantum experiments to be described at the level of pulses. For IBMQ devices these are microwave puls... | github_jupyter |
## NYC Neighborhood School Quality Metric:
# Buying a Home in NYC: What Neighborhoods are the Best Value?
### Applying Data Science Tools to Understand NYC's Residential Real Estate Fundamentals
Josh Grasso | joshgrasso@gmail.com
This project seeks to understand the fundamental factors that explain differences in... | github_jupyter |
# Keyword Filter
This notebook generates keyword-filtered versions of the pre-filtered datasets (those filtered by evidence duplicates). More specifically, for each task, a list of keywords is created first. Then, all text-triple pairs that contain one of these keywords in their evidence are filtered out.
```
# Impor... | github_jupyter |
# Logistic Regression with Linear and Polynomial Features
```
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import PolynomialFeatur... | github_jupyter |
# [LEGALST-123] Lab 24: Morality and Sentiment Analysis
This lab will cover morality and sentiment analysis using the *Moral Foundations Theory* with dictionary-based analysis, connecting to topic modeling and classifications ideas from previous labs.
### Table of Contents
[The Data](#section data)<br>
[Goal and Ques... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as la
import scipy.signal as sp
import numpy.random as rnd
import ssid
%matplotlib inline
# Just a helper for defining plants
def generalizedPlant(A,B,C,D,Cov,dt):
CovChol = la.cholesky(Cov,lower=True)
NumStates = len(A)
B1 = CovCh... | github_jupyter |
# TSG003 - Show BDC Spark sessions
## Steps
### Common functions
Define helper functions used in this notebook.
```
# Define `run` function for transient fault handling, suggestions on error, and scrolling updates on Windows
import sys
import os
import re
import platform
import shlex
import shutil
import datetime
... | github_jupyter |
"With whom do users initiate?" Mlogit Modeling
===
Multiple notes in other places about this...
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
import re
import pandas as pd
import numpy as np
from collections import Counter, defaultdict
import sqlite3
from tqdm import tqdm
import random
import... | github_jupyter |
## <center> Solving Linear Systems Using `Numpy` </center>##
```
import numpy as np
```
### Linear Systems ###
An $m\times n$ [linear system of equations](https://en.wikipedia.org/wiki/System_of_linear_equations) is a collection of linear equations:
$$
\begin{eqnarray*}
a_{11}x_1 + a_{12}x_2 + \cdots... | github_jupyter |
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed u... | github_jupyter |
### ``Regularization`` - Keras
* **Regularization** is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain.
1. **Overfitting**
One of the most important aspects when training neural networks... | github_jupyter |
# 2.3 高斯分布
高斯分布，又叫正态分布，是连续变量经常使用的一个分布模型，一维的高斯分布如下：
$$
\mathcal{N}\left(x\left|~\mu,\sigma^2\right.\right) = \frac{1}{(2\pi\sigma^2)^{1/2}} \exp\left\{-\frac{1}{2\sigma^2}(x-\mu)^2\right\}
$$
其中 $\mu$ 是均值，$\sigma$ 是方差。
$D$-维的高斯分布如下：
$$
\mathcal{N}\left(\mathbf x\left|~\mathbf{\mu, \Sigma}\right.\right) = \frac{1}{(... | github_jupyter |
```
# Copyright 2020 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 required by applicable law or agreed to in writi... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
import time
sns.set()
def get_vocab(file, lower = False):
with open(file, 'r') as fopen:
data = fopen.read()
if lower:
data = data.lower()
vocab = list(set(data))
return data, vocab
def embed_to_o... | github_jupyter |
```
import os
import json
import clang.cindex
import clang.enumerations
import csv
import numpy as np
import os
import re
import warnings
warnings.filterwarnings('ignore')
# set the config
try:
clang.cindex.Config.set_library_path("/usr/lib/x86_64-linux-gnu")
clang.cindex.Config.set_library_file('/usr/lib/x86_... | github_jupyter |
<!-- TODO: Self-organizing maps and hexagonal grids (Kohonen 1982; Huysmans et al. 2006a; Seret et al. 2012). A SOM -->
<!-- TODO: DBScan, TSNE, <https://speakerdeck.com/lmcinnes/umap-uniform-manifold-approximation-and-projection-for-dimension-reduction> -->
<!-- TODO: Good overview and connections to optimization <ht... | github_jupyter |
# Workflow 1, Module 3, Question 2
## What proteins produce agent [x]?
Let's run Q1 and use the endogenous output of that.
### Expand service
```
robokop_server = 'robokop.renci.org'
import requests
import pandas as pd
def expand(type1,identifier,type2,rebuild=None,csv=None,predicate=None):
url=f'http://{roboko... | github_jupyter |
## 1: Jeopardy Questions
```
import pandas as pd
jeopardy = pd.read_csv('jeopardy.csv')
jeopardy.head()
jeopardy.columns
jeopardy.columns = ['Show Number', 'Air Date', 'Round', 'Category', 'Value', 'Question', 'Answer']
```
## 2: Normalizing Text
```
import re
def norm(string):
string = string.lower()
string... | github_jupyter |
# 텍스트 데이터 전처리
딥러닝을 위한 텍스트 데이터를 준비하는 방법
딥러닝 모형에서는 텍스트를 수치로 변환하여 처리해야 한다.
원시 텍스트(raw text)를 딥러닝 모델에 직접 공급할 수 없다.
텍스트 데이터는 기계 학습 및 심층 학습 모델의 입력 또는 출력으로 사용할 숫자로 인코딩되어야 한다.
* 텍스트 데이터를 빠르게 준비하는 데 사용할 수있는 편리한 방법.
* BoW(Bag of Word)
* Tokenizer API
# BoW(Bag of Word)
텍스트나 단어를 사용하기 전에 수치 형태로 변환하는 전처리 과정을 거쳐야 한다.... | github_jupyter |
# OUTDATED, the examples moved to the manual
## See https://empymod.readthedocs.io/en/stable/examples
----
# Comparison between full wavefield and diffusive approximation for a fullspace
Play around to see that the difference is getting bigger for
- higher frequencies,
- higher eperm/mperm.
```
import numpy as np
i... | github_jupyter |
## 1--Spec with Ferry Downstream Task
## Wav Temporal Order Self-Supervised Learning from Birdsong Applied to Ferry Motor Classification.
Self-Supervised Model, Extracted Weights, and Load into Custom Model
Last Updated Date June 10
```
from __future__ import print_function
%matplotlib inline
import matplotlib as plt... | github_jupyter |
This notebook verifies math in Appendix A. Perspective effect in Oh & Evans 2020.
```
from sympy import symbols, simplify, latex
from sympy import cos, sin, Matrix, diff, N
import numpy as np
ra, dec = symbols('alpha, delta')
vra,vdec,vr = symbols(r'v_\alpha, v_\delta, v_r')
vx,vy,vz = symbols('v_x v_y v_z')
delta_ra... | github_jupyter |
<h1> Preprocessing using tf.transform and Dataflow </h1>
This notebook illustrates:
<ol>
<li> Creating datasets for Machine Learning using tf.transform and Dataflow
</ol>
<p>
While Pandas is fine for experimenting, for operationalization of your workflow, it is better to do preprocessing in Apache Beam. This will also... | 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 |
##### salimt
Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad.
Create a convolutional neural network that trains to 100% accuracy on these images, which cancels training upon hitting training accuracy of >.999
Hint -- it will work best with 3 convolutional layers.
`... | github_jupyter |
# H0 Hyperparameter Tuning - ResConvLSTM
#### Author: Jayant Verma
#### Cognibit Solutions LLP
Derived from https://arxiv.org/pdf/1610.03022.pdf,
1. No conv(3x3)/2 used
2. Added an extra dense layer of 256 units
83.8% on val set
```
import sys
import os
import tensorflow as tf
sys.path.append("../libs")
from cla... | github_jupyter |
# Bayesian Normal Density
This notebook illustrate how to use a Bayesian Normal density model with the [beer framework](https://github.com/beer-asr/beer). The Normal distribution is a fairly basic model but it is used extenslively in other model as a basic building block.
```
# Add "beer" to the PYTHONPATH
import sys... | github_jupyter |
# Variant Calling Workflow:
## Setting up
### Download the reference genome for E. coli REL606:
```
!mkdir -p data/ref_genome
!curl -L -o data/ref_genome/ecoli_rel606.fasta.gz ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/017/985/GCA_000017985.1_ASM1798v... | github_jupyter |
```
# 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 required by applicable law or agreed to in writi... | github_jupyter |
# Cox-PH and DeepSurv
In this notebook we will train the [Cox-PH method](http://jmlr.org/papers/volume20/18-424/18-424.pdf), also known as [DeepSurv](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0482-1).
We will use the METABRIC data sets as an example
A more detailed introduction to the `p... | github_jupyter |
### Tutorial in hamiltorch for log probabilities
* For the corresponding blog post please see: https://adamcobb.github.io/journal/hamiltorch.html
* Bayesian neural networks are left to a different notebook
```
import torch
import hamiltorch
import matplotlib.pyplot as plt
%matplotlib inline
hamiltorch.set_random_seed... | github_jupyter |
# Running the fleet of Virtual Wind Turbines and Edge Devices
**SageMaker Studio Kernel**: Data Science
After visualizing the data and training/optimizing/packaging the Anomaly detection model, its time to deploy it and test your virtual fleet. In this exercise you will run a local application written in Python3 that... | github_jupyter |
# Face Recognition
Welcome! In this assignment, you're going to build a face recognition system. Many of the ideas presented here are from [FaceNet](https://arxiv.org/pdf/1503.03832.pdf). In the lecture, you also encountered [DeepFace](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-huma... | github_jupyter |
# Python Job Interview Questions
1) What is Python?
- Python is a high-level, interactive and object-oriented language.
- Python is a very readable language.
2) What are some key features of Python?
- Object Oriented
- Free - open source
- It has a large community.
- Simple and understandable.... | github_jupyter |
## feature reduction
Data can be loaded downloaded [here](https://drive.google.com/drive/folders/1yZI5v3ws3b8GZMl_ACe4TO_qebdS2fUz?usp=sharing). The data is contained in the `srp_raw01.zip` and has to be moved to `/data/raw`.
The resulting folder structure looks like this:
`/data/raw/n`, `/data/raw/o` and `/data/raw/x... | github_jupyter |
<a href="https://colab.research.google.com/github/cedro3/data-efficient-gans/blob/master/DiffAugment_GAN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Data-Efficient GANs with DiffAugment
## セットアップ
```
# tensorflow 1.15.0 のインストール
!pip uninstal... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# CCXT - Calculate Support and Resistance
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/CCXT/CCXT_Calculate_Support_and_Resistance... | github_jupyter |
<a href="https://colab.research.google.com/github/AbuKaisar24/Machine-Learning-Algorithms-Performance-Measurement-for-Bengali-News-Sentiment-Classification.-/blob/master/Bengali_Newspaper_Sentiment_Analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>... | github_jupyter |
Current and near-term quantum computers suffer from imperfections, as we repeatedly pointed it out. This is why we cannot run long algorithms, that is, deep circuits on them. A new breed of algorithms started to appear since 2013 that focus on getting an advantage from imperfect quantum computers. The basic idea is ext... | github_jupyter |
# Readability indices
# Cognitive Load Simulation
Cognitive load is the resources your working memory has to use during problem solving and learning activities.
Total cognitive load = intrinsic cognitive load + extrinsic cognitive load + germane cognitive load
Intrinsic cognitive load = cognitive load associated wi... | github_jupyter |
# Event data
One of the main benfits of working with kloppy that it loads metadata with the event data. This metadata includes teams (name, ground and provider id) and players (name, jersey number, optional position and provider id). Using this metadata it becomes very easy to an analyse that is usable by humans, beca... | github_jupyter |
# Introduction
In this article, we discuss how to construct a Geometric Brownian Motion(GBM) simulation using Python. While building the script, we also explore the intuition behind GBM model. I will not be getting into theoretical background of its derivation. It's beyond the scope of this article. I care more about ... | github_jupyter |
# **03_gen_supplement.ipynb**:
This ipython notebook interprets MAC results. Running this entire script generates a single file containing a summary of MAC results
- `ENSEMBLE_DIR/supplementary/Supplementary File XYZ.xlsx`
This code replaces previous results and is not additive like 01 and 02 .py scripts. Must be ... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Lists" data-toc-modified-id="Lists-1"><span class="toc-item-num">1 </span>Lists</a></span><ul class="toc-item"><li><span><a href="#Indexing" data-toc-modified-id="Indexing-1.1"><span class="toc-i... | github_jupyter |
```
%pylab inline
import pandas as pd
import logging
import imp
from rpy2.robjects import numpy2ri
RANDOM_SEED = 0
numpy2ri.activate()
import fairtest.utils.log as fairtest_log
imp.reload(fairtest_log)
fairtest_log.set_params(filename='fairtest.log', level=logging.INFO)
from fairtest import DataSource
import fairtest... | github_jupyter |
```
import json, sys, os, requests
import altair as alt
from altair import expr, datum
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
eco_git_home = (
"https://raw.githubusercontent.com/EconomicsObservatory/ECOvisualisations/main/"
)
vega_embed = requests.get(eco_git_home + "guidelines/html/... | github_jupyter |
# Deep Learning Explained
# Module 3 - Lab - Introduction to Deep Neural Networks
## 1.0 Overview
This lesson introduces you to the basics of neural network architecture in the form of deep forward networks. This architecture is the quintessential deep neural net architecture. In this lab you will learn the followi... | github_jupyter |
```
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import time
import seaborn as sns
sns.set()
dataset = pd.read_csv('HistoricalQuotes.csv')
del dataset['date']
del dataset['volume']
dataset.head()
count = 0; temp = dataset.iloc[0, 0]
while temp > 10:
temp /= 10.0; co... | github_jupyter |
# Summarize results at cell-type level for the purpose of contrasting results across species
1. Proportion of FDR < 10% at various parameter for scDRS.
2. Cell type level p-value for scDRS
3. Geary's C statistics for 10kb, 1000 genes default settings.
4. LDSC-SEG p-value
```
%load_ext lab_black
%load_ext autoreload
%a... | github_jupyter |
# Exp 95 analysis
See `./informercial/Makefile` for experimental
details.
```
import os
import numpy as np
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set_style('ticks')
matplotlib.r... | github_jupyter |
# Numpy Practice
- Author: Alireza Dirafzoon
- Contributions are welcome :)
```
import numpy as np
### array()
a = [1, 2, 3]
x = np.array(a)
x = np.asarray(a)
x
x.tolist()
x.astype(np.float32)
### arange()
np.arange(3)
np.arange(0,7,2)
np.arange(3, -1, -1)
### zeros, ones, eye, linspace
np.zeros(3)
np.zeros((3,3))... | github_jupyter |
# Tutorial 2: Training a spiking neural network on a simple vision dataset
Friedemann Zenke (https://fzenke.net)
> For more details on surrogate gradient learning, please see:
> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.
> https://arxiv.org/abs/1901.09948... | github_jupyter |
## Calculate the GPS Distance with the Haversine Formula
* Dan Couture [@MathYourLife](https://twitter.com/MathYourLife), [github](https://github.com/MathYourLife)
* 2015-03-05
### Problem
I've got the start and end gps location from an excursion across town and need to determine the travel distance
start: 43.0... | github_jupyter |
# About this file
Data normalization takes a csv file, and outputs a set of public CSV's with one column and private ordering files.
Each public file is associated with a corresponding private file.
The public file consists of a shuffled column. The first line is the column name, and the rest of the file consists o... | github_jupyter |
# Desafio #9
### Instalação de libs requeridas
```
!pip install opencv-python imutils pandas matplotlib
# Libs de apoio
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Image, display
import os , random , json , requests
import types
import pandas as pd
# Libs para c... | github_jupyter |
# Building an image classifier using the Sequential API for Tensorflow
## Getting started with Fashion MNIST
```
import tensorflow as tf
from tensorflow import keras
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import random
print(tf.__version__)
print(keras.__version__)
#Bui... | github_jupyter |
```
from collections import defaultdict
from pathlib import Path
import re
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
import toml
import tqdm
def logdir2df(logdir):
"""convert tf.events files in a log... | github_jupyter |
# Búsqueda Tabú
La librería **Pyristic** incluye una clase llamada `TabuSearch` que facilita la implementación de una metaheurística basada en Búsqueda Tabú para resolver problemas de minimización. Para poder utilizar esta clase es necesario:
1. Definir:
* La función objetivo $f$.
* La lista de restricciones.... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#0.1-Motivation" data-toc-modified-id="0.1-Motivation-1"><span class="toc-item-num">1 </span>0.1 Motivation</a></span></li><li><span><a href="#0.3-Open-Source" data-toc-mo... | github_jupyter |
# Vinicius Augusto de Souza - RA: 1997530
-------------------------------------------------------------------------------------------------------------------------------
```
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.metrics import classification_report, confusion_matrix
import matplo... | github_jupyter |
```
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb
from matplotlib import rcParams
%matplotlib inline
rcParams['figure.figsize'] = 5, 4
sb.set_style('whitegrid')
# take a look at 10 head of data file
data = pd.read_csv("~/Downloads/creditcard.csv")
data.h... | github_jupyter |
## Final Notebook Submission
Please fill out:
* Student name:
* Student pace: self paced / part time / full time
* Scheduled project review date/time:
* Instructor name:
* Blog post URL:
```
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder
from sklearn.li... | github_jupyter |
# Results: XXXX Scaled
<b> MIL </b> <i>stratified k fold Validation</i> is performed.
Metrics: <br>
- AUC
- Accuracie
### Import Libraries
```
import sys,os
import warnings
os.chdir('/Users/josemiguelarrieta/Documents/MILpy')
sys.path.append(os.path.realpath('..'))
from sklearn.utils import shuffle
import... | github_jupyter |
# Object Detection Demo
Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_de... | github_jupyter |
# nlp-transform-snippets
creates snippets out of large text files
```
!pip3 install wget==3.2
import wget
import logging
import numpy as np
import os
import re
import shutil
import sys
import tarfile
import time
# file name for training data zip
input_filename = os.environ.get('input_filename ', 'data.zip')
# result... | github_jupyter |
# Final Project: Classifying Flowers
It's nearing the end of year and it's time we work on one final project. First we learned about AI, and now we are going to combine it with web scraping. The first thing we are going to do is create a neural network to classify the flowers. Then I will direct you to a website where ... | github_jupyter |
# Preferential Bayesian Optimization: Dueling-Thompson Sampling
Implementation of the algorithm by Gonzalez et al (2017).
```
import numpy as np
import gpflow
import tensorflow as tf
import tensorflow_probability as tfp
import matplotlib.pyplot as plt
import sys
import os
import datetime
import pickle
from gpflow.ut... | github_jupyter |
# Entities Recognition
<div class="alert alert-info">
This tutorial is available as an IPython notebook at [Malaya/example/entities](https://github.com/huseinzol05/Malaya/tree/master/example/entities).
</div>
<div class="alert alert-warning">
This module only trained on standard language structure, so it is no... | github_jupyter |
**[WGT-01]**
Specify the TensorFlow version.
```
%tensorflow_version 2.x
```
**[WGT-02]**
Import modules.
```
import numpy as np
import copy, random, time
from tensorflow.keras import layers, models
from IPython.display import clear_output
```
**[WGT-03]**
Define a function to get the field data.
```
def get_fi... | github_jupyter |
# binary classification example - titanic dataset
```
import warnings
warnings.filterwarnings('ignore')
%load_ext autoreload
%autoreload 2
import copy
import numpy as np
import pandas as pd
import databricks.koalas as ks
from pandas.testing import assert_frame_equal
from pandas.testing import assert_series_equal
from ... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
... | github_jupyter |
```
# Use centrailzed training to compare with federated learning
epochs = 30
n_train_items = 12800
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset
from torchvision import datasets, transforms
import numpy as np
import opacus
from opacu... | 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 |
# 학습된 NarrativeKoGPT2을 이용한 Text Generation
## 1.Google Drive 연동
- 모델 파일과 학습 데이터가 저장 되어있는 구글 드라이브의 디렉토리와 Colab을 연동.
### 1.1 Google Drive 연동
아래 코드를 실행후 나오는 URL을 클릭하여 나오는 인증 코드 입력
```
from google.colab import drive
drive.mount('/content/drive')
```
**Colab 디렉토리 아래 NarrativeKoGPT2 경로 확인**
```
!ls drive/'My Drive'/'Col... | github_jupyter |
# Liver Disorders Data Set Arm Identefication
# Importing the important libraries
```
import pandas as pd
import numpy
import sys
%matplotlib inline
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
import numpy as np
import time
import sklearn
from IPython.display import set_matplotlib_fo... | github_jupyter |
```
import pandas as pd
# demo 验证
train_df = pd.read_csv('Data/Movie_RS.csv',nrows=10000)
print(train_df.shape)
train_df.head(1)
# 去除空值
train_df.dropna(axis=0, how='any', inplace=True)
train_df.info()
!pip install lightfm
from sklearn.metrics.pairwise import cosine_similarity
from lightfm import LightFM, cross_validati... | github_jupyter |
## Calculating inter-annotators agreement
#### This script generates 8 additional files:
1. batches_annotators.json – a list of annotators per batch
2. k_alpha_per_batch_4_options.csv – Krippendorff's alpha per batch for all 4 options
3. k_alpha_per_batch_2_options.csv – Krippendorff's alpha per batch for 2 options ('... | github_jupyter |
Copyright (c) Microsoft Corporation.
Licensed under the MIT License.
# Library Imports
```
data_lake_account_name = '' # Synapse Workspace ADLS
file_system_name = 'data'
table_name = "c360_data.preparedinferencedata"
#AML workspace details
subscription_id = ""
resource_group = ""
workspace_name = ""
import azure... | github_jupyter |
```
import tensorflow as tf
#import wave
import glob
import scipy.io.wavfile as wavfile
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
global SMP_RATE
SMP_RATE = 16000
def getWaveName(wavepath):
return wavepath.split('/')[-1]
def findWave(wavefile,path):
r = gl... | github_jupyter |
# Introduction to Data Science – Lecture 2 – Python
Hi there, welcome to our first coding lecture. We will be using Python, a popular data science programming language in the lectures, homeworks, and projects. As part of Homework 0, you should have already setup Python, IPython and Jupyter notebooks, so it's time to g... | github_jupyter |
# Lesson 4 - Euler—McLaurin evaluation
time: 30 min
## Learning outcomes
Python:
- lambda functions
- recursive functions
SageMath:
- symbolic and numerical integrals
- plotting
To check on the Riemann Hypothesis we need to be able to evaluate $\zeta$
to the left of the real part = 1. We can not rely on the de... | github_jupyter |
```
import torch, torchvision
print(torch.__version__, torch.cuda.is_available())
!python -m pip install -q 'git+https://github.com/facebookresearch/detectron2.git'
import pandas as pd
import numpy as np
import pandas as pd
from tqdm import tqdm
from tqdm import tqdm_notebook as tqdm # progress bar
from datetime impor... | github_jupyter |
<img src = "./media/walmart.png" width = 400 height = 400>
```
import pandas as pd
sales = pd.read_csv('./dataset/walmart_data.csv')
sales
sales.drop(['MarkDown1', 'MarkDown2', 'MarkDown3', 'MarkDown4', 'MarkDown5', 'Size'], inplace=True, axis=1)
sales
sales.rename(columns={'Store':'store',
'Typ... | github_jupyter |
```
import matplotlib.pyplot as plt
import seaborn as sns
import os
import pandas as pd
import numpy as np
%matplotlib inline
sns.set_context('talk')
sns.set_style('ticks')
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10,6)
sim_name = 'test_schedule_v7'
outdir = f'fig/{sim_name}'
if not os.path.exi... | github_jupyter |
# LAB 4a: Creating a Sampled Dataset.
**Learning Objectives**
1. Setup up the environment
1. Sample the natality dataset to create train/eval/test sets
1. Preprocess the data in Pandas dataframe
## Introduction
In this notebook, we'll read data from BigQuery into our notebook to preprocess the data within a Panda... | github_jupyter |
#Trends Places To Sheets Via Query
Move using a WOEID query.
#License
Copyright 2020 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
Unl... | github_jupyter |
# Train and Deploy Your First Machine Learning Model on Amazon SageMaker
## Create SageMaker session
A SageMaker session needs to be initialized in order to start interacting the SageMaker service.
```
import boto3
import re
import os
import numpy as np
import pandas as pd
import sagemaker as sage
boto_session = b... | github_jupyter |
# Survey Analysis: Summary Tables and Statistical Tests
This notebook summarizes survey responses from the original and amended flu surveys. It generates Tables 1, 2, 3 and S1 and reproduces the statistical tests reported in the paper.
```
import pandas as pd
import numpy as np
import scipy.stats as stats
```
### L... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
并按照提示设置适当的路径。
当这些准备工作完成... | github_jupyter |
```
import pickle
import os
import numpy as np
import pandas as pd
import os
import glob
def read_lines(fn):
if not os.path.exists(fn):
return []
with open(fn, 'r', encoding='utf-8') as f:
text = f.read()
lines = text.split("\n")
if lines[-1] == '':
return lines[:-1]
else:
... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
, '../')
sys.path.append(root)
from matplotlib import pyplot as plt
import matplotlib
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from gpt.gpt_distgen import run_gpt_with_distgen
GPT_IN... | github_jupyter |
<a href="https://colab.research.google.com/github/ritesh-chafer/coronavirus-analysis/blob/master/Coronavirus_Dataset_Enrichment.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')
ls '/con... | github_jupyter |
```
library(caret, quiet=TRUE);
library(base64enc)
library(httr, quiet=TRUE)
```
# Build a Model
```
set.seed(1960)
create_model = function() {
model <- train(Species ~ ., data = iris, method = "nnet", trace = FALSE)
return(model)
}
# dataset
model = create_model()
pred <- predict(model, as.matrix(i... | github_jupyter |
# Brainiak Tutorials Environment Setup for Google CoLab
## Install Brainiak and code dependencies <i>(Approx install time 1 minute)</i>
```
!pip install deepdish ipython matplotlib nilearn notebook pandas seaborn watchdog
!pip install pip\<10
!pip install git+https://github.com/brainiak/brainiak
```
## Git-clone hel... | github_jupyter |
# Functional data
This notebook links various functional layers to ET cells across GB. Various methods are used based on the nature of input data, from areal interpolation to zonal statistics.
All data are furhter measured within a relevant spatial context.
## Population estimates
Population estimates are linked us... | github_jupyter |
```
import ncbi_genome_download as ngd
import os, re, gzip
from ete3 import NCBITaxa
import os
ncbi = NCBITaxa()
workpath = os.path.join("../" + "NCBITaxa/")
try:
os.mkdir(workpath)
except FileExistsError:
print("File exists:"+workpath)
def getTaxid(namelist):
# Get Taxon id
accessid = []
for i in n... | github_jupyter |
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