code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
<h1> Preprocessing using Dataflow </h1>
This notebook illustrates:
<ol>
<li> Creating datasets for Machine Learning using 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 help if you need to preprocess da... | github_jupyter |
# Arcane Python syntax
Python has a syntax with a few features that are quite unique.
**General advice:** don't use any of this unless you feel comfortable with it, since mistakes can lead to bugs that are hard to track down.
## Interval checking
In Python an expression such as `a < x <= b` is legal and well-define... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import malaya_speech.train.model.conformer as conformer
import malaya_speech.train.model.transducer as transducer
import malaya_speech
import tensorflow as tf
import numpy as np
import json
from glob import glob
subwords = malaya_speech.subword.load('transducer-mix... | github_jupyter |
<a href="https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%202%20-%20Lesson%202%20-%20Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2019 The TensorFlow Authors.
```
#@title Lic... | github_jupyter |
```
queries = [
"""
INSERT INTO page_lookup_nonredirect
SELECT page.page_id as redircet_id, page.page_title as redirect_title, page.page_title true_title,
page.page_id, page.page_latest
FROM page LEFT OUTER JOIN redirect ON page.page_id = redirect.rd_from
... | github_jupyter |
STAT 453: Deep Learning (Spring 2021)
Instructor: Sebastian Raschka (sraschka@wisc.edu)
Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021/
GitHub repository: https://github.com/rasbt/stat453-deep-learning-ss21
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
```
... | github_jupyter |
<center><img alt="" src="images/Cover_EDA.jpg"/></center>
## <center><font color="blue">EDA-04: Unsupervised Learning - Clustering Bagian ke-02</font></center>
<h2 style="text-align: center;">(C) Taufik Sutanto - 2020</h2>
<h2 style="text-align: center;">tau-data Indonesia ~ <a href="https://tau-data.id/eda-04/" tar... | github_jupyter |
<h1 align="center">TensorFlow Deep Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from the *Deep Neural Networks* lesson to label images of English letters! The data you are using, <a href="http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html">notMNIST<... | 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 |
# Day and Night Image Classifier
---
The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.
We'd like to build a classifier that can accurately label these images as day or night, and that relies on f... | github_jupyter |
# Components of StyleGAN
### Goals
In this notebook, you're going to implement various components of StyleGAN, including the truncation trick, the mapping layer, noise injection, adaptive instance normalization (AdaIN), and progressive growing.
### Learning Objectives
1. Understand the components of StyleGAN that... | github_jupyter |
Центр непрерывного образования
# Программа «Python для автоматизации и анализа данных»
Неделя 1 - 1
*Татьяна Рогович, НИУ ВШЭ*
## Введение в Python. Целые и вещественные числа. Логические переменные.
# Функция print()
С помощью Python можно решать огромное количество задач. Мы начнем с очень простых и постепенно ... | github_jupyter |
```
from mxnet import nd
def pure_batch_norm(X, gamma, beta, eps=1e-5):
assert len(X.shape) in (2, 4)
# 全连接: batch_size x feature
if len(X.shape) == 2:
# 每个输入维度在样本上的平均和方差
mean = X.mean(axis=0)
variance = ((X - mean)**2).mean(axis=0)
# 2D卷积: batch_size x channel x height x width
... | github_jupyter |
<a href="https://colab.research.google.com/github/wguesdon/BrainPost_google_analytics/blob/master/Report_v01_02.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Project Presentation
## About BrainPost
Kasey Hemington runs BrainPost with a fellow ... | github_jupyter |
# Merry Christmas and Happy Holidays !
## Using Python
Author: Sumudu Tennakoon\
Created Date: 2020-12-24
## Goal
To create a Greeting Card in the console output with
* An illustration of a Christmas tree using characters.
* Randomly distributed Ornaments and Decorations.
* a start in the top of of the tree
* A borde... | github_jupyter |
# Decision tree for regression
In this notebook, we present how decision trees are working in regression
problems. We show differences with the decision trees previously presented in
a classification setting.
First, we load the penguins dataset specifically for solving a regression
problem.
<div class="admonition no... | github_jupyter |
# The Correlation Coefficient
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie with example algorithms by David Edwards
Part of the Quantopian Lecture Series:
* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)
* [github.com/quantopian/research_public](https://github.com/quantopian/rese... | github_jupyter |
# Data Preparation with SageMaker Data Wrangler (Part 2)
> A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading **Part 2:Import data from multiple... | github_jupyter |
```
import pandas as pd
```
# Load and Read In Dataframes
```
from google.colab import files
uploaded = files.upload()
df1 = pd.read_csv('base_api_df (7).csv')
df2 = pd.read_csv('data-2.csv')
df3 = pd.read_csv('featuresdf.csv')
df4 = pd.read_csv('SpotifyFeatures.csv')
```
# Cut Dataframes Down to Track Ids and Music... | github_jupyter |
# Functions of DataFrame 7
**이산화**
```
import numpy as np
import pandas as pd
from pandas import DataFrame
np.random.seed(777)
df=DataFrame({'c1':np.random.randn(20),
'c2':['a','a','a','a','a','a','a','a','a','a',
'b','b','b','b','b','b','b','b','b','b']})
print(df)
bins=np.linspace(df.c1.... | github_jupyter |
```
import pandas as pd
import statsmodels.formula.api as smf
from statsmodels.iolib.summary2 import summary_col
import matplotlib.pyplot as plt
%matplotlib inline
#%config InlineBackend.figure_format = 'retina' # Uncomment if using a retina display
plt.rc('pdf', fonttype=42)
plt.rcParams['ps.useafm'] = True
plt.rcPara... | github_jupyter |
# Storing Multiple Values in Lists
Just as a `for` loop is a way to do operations many times,
a list is a way to store many values.
Unlike NumPy arrays,
lists are built into the language (so we don't have to load a library
to use them).
We create a list by putting values inside square brackets and separating the value... | github_jupyter |
Exercise 4 - Polynomial Regression
========
Sometimes our data doesn't have a linear relationship, but we still want to predict an outcome.
Suppose we want to predict how satisfied people might be with a piece of fruit, we would expect satisfaction would be low if the fruit was under ripened or over ripened. Satisfac... | github_jupyter |
# Building a Classifier from Lending Club Data
**An end-to-end machine learning example using Pandas and Scikit-Learn**
## Data Ingestion
```
%matplotlib inline
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.tools.plotting import scatter_matrix
... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
## Yield Data
```
import pandas as pd
import numpy as np
import altair as alt
import os
pwd
vegetables = pd.read_csv('MichiganVegetableData.csv')
commodity_list1 = vegetables['Commodity'].unique().tolist()
for commodity in commodity_list1:
commoditydf = vegetables[vegetables['Commodity'] == commodity]
mi_commo... | github_jupyter |
# DiscreteDP Example: Water Management
**Daisuke Oyama**
*Faculty of Economics, University of Tokyo*
From Miranda and Fackler, <i>Applied Computational Economics and Finance</i>, 2002,
Section 7.6.5
```
%matplotlib inline
import itertools
import numpy as np
from scipy import sparse
import matplotlib.pyplot as plt
f... | github_jupyter |
# 第3部 Pythonによるデータ分析｜Pythonで学ぶ統計学入門
## 5章 標本の統計量の性質
### ライブラリのインポート
```
# 数値計算に使うライブラリ
import numpy as np
import pandas as pd
import scipy as sp
from scipy import stats
# グラフを描画するライブラリ
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
# 表示桁数の指定
%precision 3
# グラフをjupyter Notebook内に表示させるための指定
%ma... | github_jupyter |
# Setup IAM for Kinesis
```
import boto3
import sagemaker
import pandas as pd
sess = sagemaker.Session()
bucket = sess.default_bucket()
role = sagemaker.get_execution_role()
region = boto3.Session().region_name
sts = boto3.Session().client(service_name="sts", region_name=region)
iam = boto3.Session().client(service_... | github_jupyter |
# Plot unit conversions
This notebook demonstrates some examples of different kinds of units, and the circumstances under which they are converted and displayed.
```
%matplotlib inline
import sys
import atomica as at
import matplotlib.pyplot as plt
import numpy as np
import sciris as sc
from IPython.display import di... | github_jupyter |
# Using the model and best-fit parameters from CenQue, we measure the following values:
The "true" SF fraction
$$f_{True SF}(\mathcal{M}_*)$$
The "true" SF SMF
$$\Phi_{True SF}(\mathcal{M}_*)$$
```
import numpy as np
import pickle
import util as UT
import observables as Obvs
from scipy.interpolate import interp1d
# ... | github_jupyter |
# Extremal linkage networks
This notebook contains code accompanying the paper [extremal linkage networks](https://arxiv.org/abs/1904.01817).
We first implement the network dynamics and then rely on [TikZ](https://github.com/pgf-tikz/pgf) for visualization.
## The Model
We define a random network on an infinite set... | github_jupyter |
```
import os
import random
import math
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, concatenate, Conv2D, MaxPooling2D... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import math
import random
from math import sqrt
def sciPrintR(val, relErr, name=None):
if name != None:
print name, val, "+-", val * relErr, "(", relErr * 100., "%)"
else:
print val, "+-", val * relErr, "(", relErr * 100., "%)"
def... | github_jupyter |
```
# libraries
from pandas_datareader import data
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
# configure
%matplotlib inline
sns.set(style = 'whitegrid')
# path
os.getcwd()
# import india rain data
india_rain = pd.read_csv('C:\\Us... | github_jupyter |
# MT5 Small Model
In this notebook, we will be fine tuning the MT5 Sequence-to-Sequence Transformer model to take a Natural Language structured card specification to Java code.
### Check for Cuda Compatibility.
```
import torch
import torch.nn as nn
torch.cuda.is_available()
using_google_drive = True
if using_googl... | github_jupyter |
# Retail Demo Store Experimentation Workshop - Interleaving Recommendation Exercise
In this exercise we will define, launch, and evaluate the results of an experiment using recommendation interleaving using the experimentation framework implemented in the Retail Demo Store project. If you have not already stepped thro... | github_jupyter |
```
# Dependencies and Setup
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Hide warning messages in notebook
import warnings
warnings.filterwarnings('ignore')
# File to Load (Remember to Change These)
mouse_drug_data_to_load = "data/mouse_drug_data.csv"
clinical_trial_dat... | github_jupyter |
# What _projects_ am I a member of?
### Overview
There are a number of API calls related to projects. Here we focus on listing projects. As with any **list**-type call, we will get minimal information about each project. There are two versions of this call:
1. (default) **paginated** call that will return 50 projects... | github_jupyter |
```
import pandas as pd
import numpy as np
import pickle
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', -1)
%matplotlib inline
# Remove unrelated columns form data and get their name
folder_path = '../../../datalcdem/data/optima/dementia_18J... | github_jupyter |
## Product Review Aspect Detection: Laptop
### This is a Natural Language Processing based solution which can detect up to 8 aspects from online product reviews for laptops.
This sample notebook shows you how to deploy Product Review Aspect Detection: Laptop using Amazon SageMaker.
> **Note**: This is a reference n... | github_jupyter |
```
import numpy as np
import pprint
import sys
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.gridworld import GridworldEnv
pp = pprint.PrettyPrinter(indent=2)
env = GridworldEnv()
def value_iteration(env, theta=0.0001, discount_factor=1.0):
"""
Value Iteration Algorithm.
Args:
... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
# Asymmetric Loss
This documentation is based on the paper "[Asymmetric Loss For Multi-Label Classification](https://arxiv.org/abs/2009.14119)".
## Asymetric Single-Label Loss
```
import timm
import torch
import torch.nn.functional as F
from timm.loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
import... | github_jupyter |
# 0. Dependências
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
%matplotlib inline
pd.options.display.max_rows = 10
```
# 1. Introdução
**O objetivo principal do... | github_jupyter |
## Given two binary strings, return their sum (also a binary string).
The input strings are both non-empty and contains only characters 1 or 0.
### Example 1:
Input: a = "11", b = "1"
Output: "100"
### Example 2:
Input: a = "1010", b = "1011"
Output: "10101"
```
def add_binary(a,b):
return '{0:b}'.format(int(a... | github_jupyter |
# Chapter 3: Dynamic Programming
## 1. Exercise 4.1
$\pi$ is equiprobable random policy, so all actions equally likely.
- $q_\pi(11, down)$
With current state $s=11$ and action $a=down$, we have next is the terminal state $s'=end$, which have reward $R'=0$
$$
\begin{aligned}
q_\pi(11, down) &= \sum_{s',r}p(s',r | s... | github_jupyter |
# Predictions with Pyro + GPyTorch (High-Level Interface)
## Overview
In this example, we will give an overview of the high-level Pyro-GPyTorch integration - designed for predictive models.
This will introduce you to the key GPyTorch objects that play with Pyro. Here are the key benefits of the integration:
**Pyro ... | github_jupyter |
# Exercise 1: Schema on Read
```
from pyspark.sql import SparkSession
import pandas as pd
import matplotlib
spark = SparkSession.builder.getOrCreate()
dfLog = spark.read.text("data/NASA_access_log_Jul95.gz")
```
# Load the dataset
```
#Data Source: http://ita.ee.lbl.gov/traces/NASA_access_log_Jul95.gz
dfLog = spark.... | github_jupyter |
```
%%sh
pip -q install sagemaker stepfunctions --upgrade
# Enter your role ARN
workflow_execution_role = ''
import boto3
import sagemaker
import stepfunctions
from stepfunctions import steps
from stepfunctions.steps import TrainingStep, ModelStep, EndpointConfigStep, EndpointStep, TransformStep, Chain
from stepfuncti... | github_jupyter |
### Math 157: Intro to Mathematical Software
### UC San Diego, Winter 2021
### Homework 1: due Thursday, Jan 14 at 8PM Pacific
In general, each homework will be presented as a single Jupyter notebook like this one. A problem will typically consist of multiple components; however, each overall problem within a homewor... | 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 |
# Module 10 - Regression Algorithms - Linear Regression
Welcome to Machine Learning (ML) in Python!
We're going to use a dataset about vehicles and their respective miles per gallon (mpg) to explore the relationships between variables.
The first thing to be familiar with is the data preprocessing workflow. Data need... | github_jupyter |
# Losing your loops
## Python is slow!
* dynamically typed -- Python interpreter needs to compare and convert(if needed) in runtime everytime a variable is written, modified or referenced
* interpreted -- Vanilla Python comes with no compiler optimization
* Uses buffers inefficiently because Python lists aren't homo... | github_jupyter |
<a href="https://colab.research.google.com/github/isaacmg/task-vt/blob/biobert_finetune/drug_treatment_extraction/notebooks/BioBERT_RE.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Finetuning BioBERT for RE
This is a fine-tuning notebook that we... | github_jupyter |
```
!pip install eli5
!pip install xgboost
```
## Import of Libraries needed
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from cat... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
from sqlalchemy import inspect
```
# Reflect Tables into SQLAlchemy ORM
```
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
fr... | github_jupyter |
# Ensemble Learning
* The basic idea of ensemble learning is to have multiple learning algorithms for the same problem and combine their results to make a final prediction
* There are multiple types on ensemble learning. Common approaches include:
* Boosting
* Bagging/Bootstrapping
* Random Forests
... | github_jupyter |
# MixMod Tutorial
Welcome to the MixMod tutorial! Here we'll go over the basic functionality of MixMod. It's a small package, so the explanation of the MixtureModel class will be brief and will largely focus on formatting the inputs correctly. (Mixture models are relatively parameter-rich, so the syntax for properly s... | github_jupyter |
# Automated setup of mixtures
We've been working on streamlining setup of simulations of arbitrary mixtures in AMBER/GROMACS/OpenMM and others for some of our own research. I thought I'd demo this really quick so you can get a feel for it and see if you're interested in contributing. It also allows quick setup and ana... | github_jupyter |
# Mislabel detection using influence function with all of layers on Cifar-10, ResNet
### Author
[Neosapience, Inc.](http://www.neosapience.com)
### Pre-train model conditions
---
- made mis-label from 1 percentage dog class to horse class
- augumentation: on
- iteration: 80000
- batch size: 128
#### cifar-10 train d... | github_jupyter |
```
import os
os.chdir('..')
os.chdir('..')
print(os.getcwd())
import rsnapsim as rss
import numpy as np
os.chdir('rsnapsim')
os.chdir('interactive_notebooks')
import numpy as np
import matplotlib.pyplot as plt
import time
poi_strs, poi_objs, tagged_pois,raw_seq = rss.seqmanip.open_seq_file('../gene_files/H2B_with... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
##### Copyright 2018 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
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... | github_jupyter |
# K Nearest Neighbor (KNN) Model
```
# Update sklearn to prevent version mismatches
!pip install sklearn --upgrade
# Update sklearn to prevent version mismatches
!pip install tensorflow==2.2 --upgrade
!pip install keras --upgrade
# Install joblib. This will be used to save your model.
# Restart your kernel after inst... | github_jupyter |
```
import pandas as pd
import os
import glob
raw_data_path = os.path.join('data', 'raw')
clean_filename = os.path.join('data', 'clean', 'data.csv')
```
# Read data
```
all_files = glob.glob(raw_data_path + "/top_songs_with_lyrics.csv")
raw_data = pd.concat(pd.read_csv(f) for f in all_files)
raw_data.head()
```
# Pr... | github_jupyter |
# Linked List vs. Array
## Node and Linked list
linked list is based on node structure.
We can imagine a node to be an indivdual pod.
In each pod, we store different types of data - numbers, strings
A linked list also store pointers in each pod on top of the data.
If the linked list only has 1 pointer, which poin... | github_jupyter |
```
import matplotlib.pyplot as plt
from sklearn.neighbors import LocalOutlierFactor
import pandas as pd
import os
import numpy as np
# file_dir = os.getcwd()
# raw_data_dir = os.path.join(file_dir, '/raw_data')
file_list = []
for root, dirs, files in os.walk('./raw_data'):
for file in files:
if os.path.s... | github_jupyter |
# Working with Samples and Features
From a combined dataset of cancer and normal samples, extract the normal samples. Within the normal samples, find the genes coexpressed with LRPPRC (Affymetrix probe M92439_at), a gene with mitochondrial function.
## Before you begin
* Sign in to GenePattern by entering your usern... | github_jupyter |
# Import
Use this resource with the export resource to migrate objects from one organization to another.
# Prerequisite - Need to get the source and target object ids
When we are performing an import operation, we need to map the source connection with the target connection, similartly map the source runtime environ... | github_jupyter |
```
%%html
<link href="http://mathbook.pugetsound.edu/beta/mathbook-content.css" rel="stylesheet" type="text/css" />
<link href="https://aimath.org/mathbook/mathbook-add-on.css" rel="stylesheet" type="text/css" />
<style>.subtitle {font-size:medium; display:block}</style>
<link href="https://fonts.googleapis.com/css?fa... | github_jupyter |
<span style="color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;">An Exception was encountered at '<a href="#papermill-error-cell">In [40]</a>'.</span>
# PA005: High Value Customer Identification
# 0.0 Imports
```
import os
import joblib
import s3fs
import pickle
import re
import nump... | github_jupyter |
```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metri... | github_jupyter |
<p float="center">
<img src="https://github.com/carlosalvarezh/Analisis_Numerico/blob/master/images/C00_Img00_logo.png?raw=true" width="350" />
</p>
<h1 align="center">ST0256 - Análisis Numérico</h1>
<h1 align="center">Presentación del Curso</h1>
<h1 align="center">2021/01</h1>
<h1 align="center">MEDELLÍN - COLOMBIA ... | github_jupyter |
## CIFAR 10
```
%matplotlib inline
%reload_ext autoreload
%autoreload 2
```
You can get the data via:
wget http://pjreddie.com/media/files/cifar.tgz
**Important:** Before proceeding, the student must reorganize the downloaded dataset files to match the expected directory structure, so that there is a dedicat... | github_jupyter |
# 1. Very simple 'programs'
## 1.1 Running Python from the command line
In order to test pieces of code we can run Python from the command line. In this Jupyter Notebook we are going to simulate this. You can type the commands in the fields and execute them.<br>
In the field type:<br>
`print('Hello, World')`<br>
Then p... | github_jupyter |
## Install packages and connect to Oracle
```
sc.install_pypi_package("sqlalchemy")
sc.install_pypi_package("pandas")
sc.install_pypi_package("s3fs")
sc.install_pypi_package("cx_Oracle")
sc.install_pypi_package("fsspec")
from sqlalchemy import create_engine
engine = create_engine('oracle://CMSDASHADMIN:4#X9#Veut#KSsU#... | github_jupyter |
# Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.... | github_jupyter |
```
#!/usr/bin/python
# coding: UTF-8
import json
import pandas as pd
import logging
import codecs
SimulationName="nii_videodata_jsonl_parse"
#ログ設定
log_fmt = '%(asctime)s- %(name)s - %(levelname)s - %(message)s'
logger_name = "LOGGER"
logging.basicConfig(filename="./Log/" + SimulationName + ".log",format=log_fmt, level... | github_jupyter |
# Generative Spaces (ABM)
In this workshop we will lwarn how to construct a ABM (Agent Based Model) with spatial behaviours, that is capable of configuring the space. This file is a simplified version of Generative Spatial Agent Based Models. For further information, you can find more advanced versions here:
* [Objec... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader... | github_jupyter |
# 1. Terrestrial vs solar origins of radiation in Earth's atmosphere
```
import math
import numpy as np
import matplotlib.pyplot as plt
# Define Constants
Ts = 5785 # K
Te = 255 # K
des = 150e9 # m
re = 6.371e6 # m
rs = 6.96e8 # m
h = 6.62e-34 # m^2 kg/s
c = 299792458 # m/s
k = 1.38e-23 # J/K (kg m2 s-2 K-1)... | github_jupyter |
# Introduction
This notebook illustrates how to access your database instance using Python by following the steps below:
1. Import the `ibm_db` Python library
1. Identify and enter the database connection credentials
1. Create the database connection
1. Create a table
1. Insert data into the table
1. Query data from t... | github_jupyter |
# W3 Lab: Perception
In this lab, we will learn basic usage of `pandas` library and then perform a small experiment to test the perception of length and area.
```
import pandas as pd
import math
import matplotlib.pyplot as plt
%matplotlib inline
```
## Vega datasets
Before going into the perception experiment, let... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
```
import pandas as pd
import numpy as np
name = 'Revere' #name of the town
town = pd.read_csv(name+'-google.csv') #file name, in this case they all followed format "town-google.csv"
#strip all white space and split the types into a list for easier searching
town['type_list'] = town['types'].str.replace(' ','').str.s... | github_jupyter |
# Pre-procesamiento de datos
## Candidaturas elegidas
Principales transformaciones:
- Selección de atributos
- Tratamiento de valores faltantes
```
import glob
import nltk
import re
import pandas as pd
from string import punctuation
df_deputadas_1... | github_jupyter |
```
import torch
from dataset import load_dataset
from basic_unet import UNet
import matplotlib.pyplot as plt
from rise import RISE
from pathlib import Path
from plot_utils import plot_image_row
from skimage.feature import canny
batch_size = 1
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
trai... | github_jupyter |
# Testing the Head
**Warning:** Before running this notebook, first make sure you understand the command you run and make sure that the robot can freely move.
**Note:** Also stop all other running Python script or notebook connected to the robot as only one connection can run at the same time.
```
%matplotlib notebo... | github_jupyter |
<a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Evaluating_TrOCR_base_handwritten_on_the_IAM_test_set.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Set-up environment
```
!pip install -q gi... | github_jupyter |
## Time to do some data science
Before creating a tome, we must decide on how to transform our data before concatenating. Therefore, we will explore the data for a single match.
We will investigate the number of footsteps players make as a function of rank, wins, and friendly commends.
After we developed the code t... | github_jupyter |
```
import argparse
import os
import sys
import torch
import torch.nn as nn
import datasets
import models.resnet as ResNet
import models.senet as SENet
from liveview import LiveView
import utils
configurations = {
1: dict(
max_iteration=1000000,
lr=1.0e-1,
momentum=0.9,
weight_deca... | github_jupyter |
```
import os, sys
import torch
from transformers import BertModel, BertConfig
from greenformer import auto_fact
from itertools import chain
from os import path
import sys
def count_param(module, trainable=False):
if trainable:
return sum(p.numel() for p in module.parameters() if p.requires_grad)
else:... | github_jupyter |
<img src='./img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='40%'></img>
<br>
<a href="./00_index.ipynb"><< Index </a><br>
<a href="./04_sentinel3_NRT_SLSTR_FRP_load_browse.ipynb"><< 04 - Sentinel-3 NRT SLSTR FRP - Load and browse </a><span style="float:right;"><a href="./06_IA... | github_jupyter |
# Keane and Wolpin (1997)
**Parameter Estimation via the Method of Simulated Moments (MSM)**
In their seminal paper on the career decisions of young men, Keane and Wolpin (1997) estimate a life-cycle model for occupational choice based on NLSY data for young white men. The paper contains a basic and an extended specif... | github_jupyter |
```
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.common.exceptions import *
import pandas as pd
from time import sleep
import os
nome_hoteis = []
preco_hoteis = []
... | github_jupyter |
# DS108 Databases : Lesson Ten Companion Notebook
### Table of Contents <a class="anchor" id="DS108L10_toc"></a>
* [Table of Contents](#DS108L10_toc)
* [Page 1 - Overview](#DS108L10_page_1)
* [Page 2 - Sharding](#DS108L10_page_2)
* [Page 3 - More Methods](#DS108L10_page_3)
* [Page 4 - Key Terms](#DS10... | github_jupyter |
# Implementing a one-layer Neural Network
We will illustrate how to create a one hidden layer NN
We will use the iris data for this exercise
We will build a one-hidden layer neural network to predict the fourth attribute, Petal Width from the other three (Sepal length, Sepal width, Petal length).
```
import matpl... | github_jupyter |
# From batch to online
## A quick overview of batch learning
If you've already delved into machine learning, then you shouldn't have any difficulty in getting to use incremental learning. If you are somewhat new to machine learning, then do not worry! The point of this notebook in particular is to introduce simple no... | github_jupyter |
#### Copyright 2017 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 writin... | github_jupyter |
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