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# Intermediate Lesson on Geospatial Data
## Data, Information, Knowledge and Wisdom
<strong>Lesson Developers:</strong> Jayakrishnan Ajayakumar, Shana Crosson, Mohsen Ahmadkhani
#### Part 1 of 5
```
# This code cell starts the necessary setup for Hour of CI lesson notebooks.
# First, it enables users to hide and u... | github_jupyter |
<a href="https://colab.research.google.com/github/tfrizza/DALL-E-tf/blob/main/tfFlowers_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%pip install -q tensorflow_addons
!git clone https://github.com/tfrizza/DALL-E-tf.git
%cd DALL-E-tf
impo... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('.')
import utils
def f(x):
return x * np.cos(np.pi*x)
utils.set_fig_size(mpl, (4.5, 2.5))
x = np.arange(-1.0, 2.0, 0.1)
fig = plt.figure()
sub... | github_jupyter |
```
!pip install d2l==0.17.2
# implement several utility functions to facilitate data downloading
import hashlib
import os
import tarfile
import zipfile
import requests
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'
# download function to download a dataset
def download(name, cache_dir=os.... | github_jupyter |
```
#Goal: Have customers narrow their travel searches based on temp and precipitation
import pandas as pd
import requests
import gmaps
from config import g_key
weather_data_df=pd.read_csv("data/WeatherPy_Database.csv")
weather_data_df
weather_data_df.dtypes
#configure gmaps to use the appropriate key
gmaps.configure(... | github_jupyter |
```
!pip install lightgbm
!pip install xgboost
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import xgboost as xgb
import zipfile
archive = zipfile.ZipFile('test.csv.zip', 'r')
test = pd.read_csv(archive.open('test.csv'), sep="... | github_jupyter |
## Final Output
```
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
from statistics import mean, median, variance
plt.rcParams['figure.figsize'] = [10, 5]
import pprint
import math
import tabulate
def get_overheads(file_name):
data = []
with open(file_name, 'r') as results:
... | github_jupyter |
## Machine learning sur le titanic
```
import pandas as pd
import numpy as np
```
On importe les données
```
titanic = pd.read_csv("./data/titanic_train.csv")
titanic.head()
```
On sélectionne les colonnes de x
```
x = titanic.drop(["PassengerId","Survived","Name","Ticket"],axis=1)
y = titanic["Survived"]
```
On ... | github_jupyter |
# Imporing Libraries and Dataset
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
data_train= pd.read_csv(r"C:\Users\shruti\Desktop\Decodr Session Recording\Project\Decodr Project\Power Plant ... | github_jupyter |
# NumPy - 科学计算
## 一、简介
NumPy是Python语言的一个扩充程序库。支持高级大量的维度数组与矩阵运算，此外也针对数组运算提供大量的数学函数库。Numpy内部解除了[CPython的GIL](https://www.cnblogs.com/wj-1314/p/9056555.html)（全局解释器锁），运行效率极好，是大量机器学习框架的基础库！
NumPy的全名为Numeric Python，是一个开源的Python科学计算库，它包括：
- 一个强大的N维数组对象ndrray；
- 比较成熟的（广播）函数库；
- 用于整合C/C++和Fortran代码的工具包；
- 实用的线性代数、傅里叶变换和随机数生... | github_jupyter |
# Filling in Missing Values in Tabular Records
You can select Run->Run All Cells from the menu to run all cells in Studio (or Cell->Run All in a SageMaker Notebook Instance).
## Introduction
Missing data values are common due to omissions during manual entry or optional input. Simple data imputation such as using th... | github_jupyter |
Osnabrück University - Machine Learning (Summer Term 2018) - Prof. Dr.-Ing. G. Heidemann, Ulf Krumnack
# Exercise Sheet 06
## Introduction
This week's sheet should be solved and handed in before the end of **Sunday, May 20, 2018**. If you need help (and Google and other resources were not enough), feel free to conta... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, StratifiedShuffleSplit
from sklearn.preprocessing import StandardScaler
import xgboost as xgb
from sklearn.metrics import precision_score, recall_score, jaccard_score, roc_auc_score, accuracy_score, ... | github_jupyter |
<a href="https://colab.research.google.com/github/mrklees/pgmpy/blob/feature%2Fcausalmodel/examples/Causal_Games.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Causal Games
Causal Inference is a new feature for pgmpy, so I wanted to develop a fe... | github_jupyter |
```
import sys
import os
import math
import subprocess
import pandas as pd
import numpy as np
from tqdm import tqdm
import random
import torch
import torch.nn as nn
#Initialise the random seeds
def random_init(**kwargs):
random.seed(kwargs['seed'])
torch.manual_seed(kwargs['seed'])
torch.cuda.manual_seed(k... | github_jupyter |
```
import sys
import pickle
from scipy import signal
from scipy import stats
import numpy as np
from sklearn.model_selection import ShuffleSplit
import socket
import time
import math
from collections import OrderedDict
import matplotlib.pyplot as plt
sys.path.append('D:\Diamond\code')
from csp_james_2 import *
s... | github_jupyter |
# Bounding Box Visualizer
```
try:
import cv2
except ImportError:
cv2 = None
COLORS = [
"#6793be", "#990000", "#00ff00", "#ffbcc9", "#ffb9c7", "#fdc6d1",
"#fdc9d3", "#6793be", "#73a4d4", "#9abde0", "#9abde0", "#8fff8f", "#ffcfd8", "#808080", "#808080",
"#ffba00", "#6699ff", "#009933", "#1c1c1c", "... | github_jupyter |
# Chapter 8 Lists
### 8.1 A list is a sequence
```
# list of integers
[10, 20, 30, 40]
# list of strings
['frog','toad','salamander','newt']
# mixed list
[10,'twenty',30.0,[40, 45]]
cheeses = ['Cheddar','Mozzarella','Gouda','Swiss']
numbers = [27, 42]
empty = []
print(cheeses, numbers, empty)
```
### 8.2 Lists are... | github_jupyter |
## Creating a column chart for your dashboard
In this chapter, you will start to put together your own dashboard.
Your first step is to create a basic column chart showing fatalities, injured, and uninjured statistics for the states of Australia over the last 100 years.
Instructions
1. In `A1` of `Sheet1`, use a fo... | github_jupyter |
## Fibonacci Rabbits
Fibonacci considers the growth of an idealized (biologically unrealistic) rabbit population, assuming that:
1. A single newly born pair of rabbits (one male, one female) are put in a field;
2. Rabbits are able to mate at the age of one month so that at the end of its second month a female can prod... | github_jupyter |
# Analyze Data Quality with SageMaker Processing Jobs and Spark
Typically a machine learning (ML) process consists of few steps. First, gathering data with various ETL jobs, then pre-processing the data, featurizing the dataset by incorporating standard techniques or prior knowledge, and finally training an ML model ... | github_jupyter |
<p><font size="6"><b> CASE - Observation data - analysis</b></font></p>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*
---
```
import numpy as np
i... | github_jupyter |
# Classify Images using Residual Network with 50 layers (ResNet-50)
## Import Turi Create
Please follow the repository README instructions to install the Turi Create package.
**Note**: Turi Create is currently only compatible with Python 2.7
```
import turicreate as turi
```
## Reference the dataset path
```
url =... | github_jupyter |
<img src="https://upload.wikimedia.org/wikipedia/commons/4/47/Logo_UTFSM.png" width="200" alt="utfsm-logo" align="left"/>
# MAT281
### Aplicaciones de la Matemática en la Ingeniería
## Módulo 02
## Laboratorio Clase 06: Desarrollo de Algoritmos
### Instrucciones
* Completa tus datos personales (nombre y rol USM) e... | github_jupyter |
# requests
## 实例引入
```
import requests
response = requests.get('https://www.baidu.com/')
print(type(response))
print(response.status_code)
print(type(response.text))
print(response.text)
print(response.cookies)
```
## 各种请求方式
```
import requests
requests.post('http://httpbin.org/post')
requests.put('http://httpbin.... | github_jupyter |
# More To Come. Stay Tuned. !!
If there are any suggestions/changes you would like to see in the Kernel please let me know :). Appreciate every ounce of help!
**This notebook will always be a work in progress**. Please leave any comments about further improvements to the notebook! Any feedback or constructive criticis... | github_jupyter |
### DemIntro02:
# Rational Expectations Agricultural Market Model
#### Preliminary task:
Load required modules
```
from compecon.quad import qnwlogn
from compecon.tools import discmoments
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('dark')
%matplotlib notebook
```
Generate ... | github_jupyter |
```
from numpy import array
import datetime as dt
from matplotlib import pyplot as plt
from sklearn import model_selection
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklear... | github_jupyter |
# Baseline model classification
The purpose of this notebook is to make predictions for all six categories on the given dataset using some set of rules.
<br>Let's assume that human labellers have labelled these comments based on the certain kind of words present in the comments. So it is worth exploring the comments t... | github_jupyter |
# Manual Labeling Data Preparation
Generate the pixels that will be used for train, test, and validation. This keeps pixels a certain distance and ensures they're spatially comprehensive.
```
import rasterio
import random
import matplotlib.pyplot as plt
import os
import sys
import datetime
from sklearn.utils import c... | github_jupyter |
<a href="https://colab.research.google.com/github/hansong0219/Advanced-DeepLearning-Study/blob/master/UNET/UNET_Build.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import os
import sys
from tensorflow.keras.layers import Inp... | github_jupyter |
```
import torch
import torchphysics as tp
import math
import numpy as np
import pytorch_lightning as pl
print('Tutorial zu TorchPhysics:')
print('https://torchphysics.readthedocs.io/en/latest/tutorial/tutorial_start.html')
from IPython.display import Image, Math, Latex
from IPython.core.display import HTML
Image(file... | github_jupyter |
Work looking at https://www.bexar.org/2988/Online-District-Clerk-Criminal-Records
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
%matplotlib inline
Bexar_Criminal_AB_df = pd.read_csv(r'http://edocs.bexar.org/cc/DC_cjjorad_a_b.csv',header=0)
Bexar_Criminal_C_df = pd.read_csv(... | github_jupyter |
# Quantum device tuning via hypersurface sampling
**NOTE: DUE TO MULTIPROCESSING PACKAGE THE CURRENT IMPLEMENTATION ONLY WORKS ON UNIX/LINUX OPERATING SYSTEMS [TO RUN ON WINDOWS FOLLOW THIS GUIDE](Resources/Running_on_windows.ipynb)**
Quantum devices used to implement spin qubits in semiconductors are challenging to t... | github_jupyter |
<a href="https://colab.research.google.com/github/cccaaannn/machine_learning_colab/blob/master/feature_selection/data_mining_hw3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Feature selection methods
.
```
import bookutils
```
## Synopsis
<!-- Automatically generated. Do not edit. -->
To [use the code provided in... | github_jupyter |
# Bayesian GAN
Bayesian GAN (Saatchi and Wilson, 2017) is a Bayesian formulation of Generative Adversarial Networks (Goodfellow, 2014) where we learn the **distributions** of the generator parameters $\theta_g$ and the discriminator parameters $\theta_d$ instead of optimizing for point estimates. The benefits of the B... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
from fasterai.visualize import *
plt.style.use('dark_background')
#Adjust render_factor (int) if image doesn't look quite right (max 64 on 11GB GPU). The default here works for most photos.
#It literally just is a number multiplied by 16 to get the square render r... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import pandas as pd
import numpy as np
import scipy.stats as st
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score
import sklearn.metrics as mt
import matplotlib.pyplot as plt
... | github_jupyter |
# ML Pipeline Preparation
Follow the instructions below to help you create your ML pipeline.
### 1. Import libraries and load data from database.
- Import Python libraries
- Load dataset from database with [`read_sql_table`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql_table.html)
- Define fea... | github_jupyter |
# AutoRec: Rating Prediction with Autoencoders
Although the matrix factorization model achieves decent performance on the rating prediction task, it is essentially a linear model. Thus, such models are not capable of capturing complex nonlinear and intricate relationships that may be predictive of users' preferences. ... | github_jupyter |
# <span style='color:darkred'> 4 Trajectory Analysis </span>
***
**<span style='color:darkred'> Important Note </span>**
Before proceeding to the rest of the analysis, it is a good time to define a path that points to the location of the MD simulation data, which we will analyze here.
If you successfully ran the MD... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
from freedom.utils.i3cols_dataloader import load_hits, load_strings
import dragoman as dm
%load_ext line_profiler
plt.rcParams['figure.figsize'] = [12., 8.]
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] ... | github_jupyter |
# Automatic generation of Notebook using PyCropML
This notebook implements a crop model.
### Model Cumulttfrom
```
model_cumulttfrom <- function (calendarMoments_t1 = c('Sowing'),
calendarCumuls_t1 = c(0.0),
cumulTT = 8.0){
#'- Name: CumulTTFrom -Version: 1.0, -Time step: 1
#'- Descripti... | github_jupyter |
# Store Item Demand Forecasting Challenge
## Benchmark Models
<a href="https://www.kaggle.com/c/demand-forecasting-kernels-only">Link to competition on Kaggle.</a>
In this notebook, two simple benchmarking techniques are presented.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotl... | github_jupyter |
```
import requests
import requests_cache
requests_cache.install_cache('calrecycle')
import pandas as pd
import time
URL = 'https://www2.calrecycle.ca.gov/LGCentral/DisposalReporting/Destination/CountywideSummary'
params = {'CountyID': 58, 'ReportFormat': 'XLS'}
resp = requests.post(URL, data=params)
resp
import io
def... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_HiddenDynamics/student/W3D2_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <a href="https://kaggle.com/kernels/welcome?src... | github_jupyter |
# A tutorial for the whitebox Python package
This notebook demonstrates the usage of the **whitebox** Python package for geospatial analysis, which is built on a stand-alone executable command-line program called [WhiteboxTools](https://github.com/jblindsay/whitebox-tools).
* Authors: Dr. John Lindsay (https://jblind... | github_jupyter |
<a href="https://cognitiveclass.ai/">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/CCLog.png" width="200" align="center">
</a>
<h1>Dictionaries in Python</h1>
<p><strong>Welcome!</strong> This notebook will teach you about the dictionaries in the Python Pr... | github_jupyter |
### gQuant Tutorial
First import all the necessary modules.
```
import sys; sys.path.insert(0, '..')
import os
import warnings
import ipywidgets as widgets
from gquant.dataframe_flow import TaskGraph
warnings.simplefilter("ignore")
```
In this tutorial, we are going to use gQuant to do a simple quant job. The task i... | github_jupyter |
# Cuaderno para cargar metadatos
Este cuaderno toma un directorio de MinIO con estructura de datos abiertos y crea la definición para Hive-Metastore para cada una de las tablas
## Librerias
```
from minio import Minio
import pandas as pd
from io import StringIO
from io import BytesIO
import json
from pyhive import ... | github_jupyter |
```
import lifelines
import pymc as pm
import pyBMA
import matplotlib.pyplot as plt
import numpy as np
from math import log
from datetime import datetime
import pandas as pd
%matplotlib inline
```
The first step in any data analysis is acquiring and munging the data
An example data set can be found at:
https://jak... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
train=pd.read_csv('../input/nlp-getting-started/train.csv')
test=pd.read_csv('../input/nlp-getting-started/test.csv')
sample=pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
train.head()
train.info()
train.... | github_jupyter |
```
import os
import random
import shutil
from shutil import copyfile
import csv
root_dir = "ISAFE MAIN DATABASE FOR PUBLIC/"
data = "Database/"
global_emotion_dir = "emotions_5/"
# global_emotion_dir = "emotions/"
subject_list = os.path.join(root_dir, data)
x = os.listdir(subject_list)
csv_file = "ISAFE MAIN DATABASE ... | github_jupyter |
<a href="https://colab.research.google.com/github/aljeshishe/FrameworkBenchmarks/blob/master/How_much_samples_is_enough_for_transfer_learning_same_steps_per_epoch_InceptionResNetV2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
pip install kagg... | github_jupyter |
```
import numpy as np
import pandas as pd
import datetime
from pandas.tseries.frequencies import to_offset
import niftyutils
from niftyutils import load_nifty_data
import matplotlib.pyplot as plt
start_date = datetime.datetime(2005,8,1)
end_date = datetime.datetime(2020,9,25)
nifty_data = load_nifty_data(start_date... | github_jupyter |
```
%matplotlib inline
import gym
import itertools
import matplotlib
import numpy as np
import sys
import sklearn.pipeline
import sklearn.preprocessing
if "../" not in sys.path:
sys.path.append("../")
from lib import plotting
from sklearn.linear_model import SGDRegressor
from sklearn.kernel_approximation import R... | github_jupyter |
# Writing Reusable Code using Functions in Python
### Part 4 of "Data Analysis with Python: Zero to Pandas"
This tutorial covers the following topics:
- Creating and using functions in Python
- Local variables, return values, and optional arguments
- Reusing functions and using ... | github_jupyter |
# Modeling and Simulation in Python
Chapter 3
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an as... | github_jupyter |
# Tutorial: PyTorch
```
__author__ = "Ignacio Cases"
__version__ = "CS224u, Stanford, Spring 2021"
```
## Contents
1. [Motivation](#Motivation)
1. [Importing PyTorch](#Importing-PyTorch)
1. [Tensors](#Tensors)
1. [Tensor creation](#Tensor-creation)
1. [Operations on tensors](#Operations-on-tensors)
1. [GPU compu... | github_jupyter |
# How to create Popups
## Simple popups
You can define your popup at the feature creation, but you can also overwrite them afterwards:
```
import folium
m = folium.Map([45, 0], zoom_start=4)
folium.Marker([45, -30], popup="inline implicit popup").add_to(m)
folium.CircleMarker(
location=[45, -10],
radius=... | github_jupyter |
##### Copyright 2018 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | github_jupyter |
# Introduction to Python
In this lesson we will learn the basics of the Python programming language (version 3). We won't learn everything about Python but enough to do some basic machine learning.
<img src="figures/python.png" width=350>
# Variables
Variables are objects in Python that can hold anything with numb... | github_jupyter |
# CHAPTER 14 - Probabilistic Reasoning over Time
### George Tzanetakis, University of Victoria
## WORKPLAN
The section number is based on the 4th edition of the AIMA textbook and is the suggested
reading for this week. Each list entry provides just the additional sections. For example the Expected reading include ... | github_jupyter |
# <span style="color:green"> Numerical Simulation Laboratory (NSL) </span>
## <span style="color:blue"> Numerical exercises 10</span>
### Exercise 10.1
By adapting your Genetic Algorithm code, developed during the Numerical Exercise 9, write a C++ code to solve the TSP with a **Simulated Annealing** (SA) algorithm. ... | github_jupyter |
# OpEn Rust Examples: with General Gradient Function
In this example, we are going to use a function that can obtain the gradient of any given function. This sort of function was used in relaxed_ik rust version. Now, we are trying to use this approach for [the previous example that we implemented before](https://githu... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Plot style
sns.set()
%pylab inline
pylab.rcParams['figure.figsize'] = (4, 4)
# Avoid inaccurate floating values (for inverse matrices in dot product for instance)
# See https://stackoverflow.com/questions/24537791/numpy-matrix-inversion-roun... | github_jupyter |
# 正则化和模型选择 Regularization and Model Selection
设想现在对于一个学习问题，需要从一组不同的模型中进行挑选。比如多元回归模型 $h_\theta(x)=g(\theta_0+\theta_1x+\theta_2x^2+\cdots+\theta_kx^k)$，如何自动地确定 $k$ 的取值，从而在偏差和方差之间达到较好的权衡？或者对于局部加权线性回归，如何确定带宽 $\tau$ 的值，以及对于 $\ell_1$ 正则化的支持向量机，如何确定参数 $C$ 的值？
为了方便后续的讨论，统一假定有一组有限数量的模型集合 $\mathcal{M}=\{M_1,\cdots,M_d\}$。（推广到... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
# fp = os.path.join('..\scripts', 'the_hunchback_of_notre_dame.txt ')
# # os.listdir('scripts')
# chars = np.array([])
# words = np.array([])
# scene_setup = np.array([])
# new_char = True
# with open(fp, 'r', encoding='utf-8') as ... | github_jupyter |
<a href="https://colab.research.google.com/github/constantinpape/dl-teaching-resources/blob/main/exercises/classification/5_data_augmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Data Augmentation on CIFAR10
In this exercise we will us... | github_jupyter |
# KFServing Sample
In this notebook, we provide two samples for demonstrating KFServing SDK and YAML versions.
### Setup
1. Your ~/.kube/config should point to a cluster with [KFServing installed](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#deploy-kfserving).
2. Your cluster's Istio Ing... | github_jupyter |
# "Folio 03: MLP Classifier"
> "[ML 3/3] Use Neural Networks for Data Classification with Keras"
- toc: true
- branch: master
- badges: true
- image: images/ipynb/mlp_clf_main.png
- comments: false
- author: Giaco Stantino
- categories: [portfolio project, machine learning]
- hide: false
- search_exclude: true
- perma... | github_jupyter |
```
!pip install splinter
! pip install bs4
! pip install datetime
import pandas as pd
from splinter import Browser
from bs4 import BeautifulSoup as bs
from datetime import datetime
import os
import time
! brew cask install chromedriver
# Capture path to Chrome Driver & Initialize browser
browser = Browser("chrome", he... | github_jupyter |
```
# Realize ResAE
# The decoder part only have the symmetic sturcture as the encoder, but weights and biase are initialized.
# Let's have a try.
# Display the result
import matplotlib
matplotlib.use('Agg')
%matplotlib inline
import matplotlib.pyplot as plt
import utils
import Block
import os
import time
import numpy... | github_jupyter |
```
%autosave 0
```
# 4. Evaluation Metrics for Classification
In the previous session we trained a model for predicting churn. How do we know if it's good?
## 4.1 Evaluation metrics: session overview
* Dataset: https://www.kaggle.com/blastchar/telco-customer-churn
* https://raw.githubusercontent.com/alexeygrigor... | github_jupyter |
# DSCI 525 - Web and Cloud Computing
***Milestone 3:*** This milestone aims to set up your spark cluster and develop your machine learning to deploy in the cloud for the next milestone.
## Milestone 3 checklist :
- [ ] Setup your EMR cluster with Spark, Hadoop, JupyterEnterpriseGateway, JupyterHub 1.1.0, and Livy. ... | github_jupyter |
# Prospect Theory and Cumulative Prospect Theory Agent Demo
The PTAgent and CPTAgent classes reproduce patterns of choice behavior described by Kahneman & Tverski's survey data in their seminal papers on Prospect Theory and Cumulative Prospect Theory. These classes expresses valuations of single lottery inputs, or exp... | github_jupyter |
# 神经网络的训练
作者：杨岱川
时间：2019年12月
github：https://github.com/DrDavidS/basic_Machine_Learning
开源协议：[MIT](https://github.com/DrDavidS/basic_Machine_Learning/blob/master/LICENSE)
参考文献：
- 《深度学习入门》，作者：斋藤康毅；
- 《深度学习》，作者：Ian Goodfellow 、Yoshua Bengio、Aaron Courville。
- [Keras overview](https://tensorflow.google.cn/guide/keras... | github_jupyter |
# AEJxLPS (Auroral electrojets SECS)
> Abstract: Access to the AEBS products, SECS type. This notebook uses code from the previous notebook to build a routine that is flexible to plot either the LC or SECS products - this demonstrates a prototype quicklook routine.
```
%load_ext watermark
%watermark -i -v -p virescli... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from allennlp.commands.evaluate import *
from kb.include_all import *
from allennlp.nn import util as nn_util
from allennlp.common.tqdm import Tqdm
import torch
import warnings
warnings.filterwarnings("ignore")
archive_file = "knowbert_wiki_wordnet_model"
cuda_device = -1
# line =... | github_jupyter |
```
import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
from IPython import display
import torchsummary as ts
import num... | github_jupyter |
```
# this is a little trick to make sure the the notebook takes up most of the screen:
from IPython.display import HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
# Recommendation to leave the logging config like this, otherwise you'll be flooded with unnecessary info
import logging
logging.... | github_jupyter |
# <center>Dataset Anaylsis</center>
```
%%html
<style>
body {
font-family: "Apple Script", cursive, sans-serif;
}
</style>
```
_importing necessary libraries of Data Science_
```
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
import os
```
_making a function for showing i... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import torch
from torch import nn as nn
from math import factorial
import random
import torch.nn.functional as F
import numpy as np
import seaborn as sn
import pandas as pd
import os
from os.path import join
import glob
from math import factorial
ttype = torch.cud... | github_jupyter |
<a id="ndvi_std_top"></a>
# NDVI STD
Deviations from an established average z-score.
<hr>
# Notebook Summary
* A baseline for each month is determined by measuring NDVI over a set time
* The data cube is used to visualize at NDVI anomalies over time.
* Anomalous times are further explored and visualization sol... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# This is a custom matplotlib style that I use for most of my charts
country_data = pd.read_csv('C:/Users/user/Desktop/test.csv')
country_data
```
圖中顯示2016到2017入境各個國家人數
多數國家在2017人數皆些微成長
```
fig = plt.figure(figsize=(15, 7)... | github_jupyter |
# Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!
GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio'... | github_jupyter |
# DAPA Tutorial #3: Timeseries - Sentinel-2
## Load environment variables
Please make sure that the environment variable "DAPA_URL" is set in the `custom.env` file. You can check this by executing the following block.
If DAPA_URL is not set, please create a text file named `custom.env` in your home directory with th... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import gc
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
pal = sns.color_palette()
df_train = pd.read_csv('train.csv')
df_train.head()
print('Total number of question pairs... | github_jupyter |
```
# default_exp models.OmniScaleCNN
```
# OmniScaleCNN
> This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@gmail.com based on:
* Rußwurm, M., & Körner, M. (2019). Self-attention for raw optical satellite time series classification. arXiv preprint arXiv:1910.10536.
* Official implementation: h... | github_jupyter |
# Natural Language Processing - Unsupervised Topic Modeling with Reddit Posts
###### This project dives into multiple techniques used for NLP and subtopics such as dimensionality reduction, topic modeling, and clustering.
1. [Google BigQuery](#Google-BigQuery)
1. [Exploratory Data Analysis (EDA) & Preprocessing](#Exp... | github_jupyter |
# Forecasting in Statsmodels
This notebook describes forecasting using time series models in Statsmodels.
**Note**: this notebook applies only to the state space model classes, which are:
- `sm.tsa.SARIMAX`
- `sm.tsa.UnobservedComponents`
- `sm.tsa.VARMAX`
- `sm.tsa.DynamicFactor`
```
%matplotlib inline
import num... | github_jupyter |
```
import pandas as pd
import numpy as np
from bayes_opt import BayesianOptimization
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from Data_Processing import DataProcessing
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train... | github_jupyter |
```
from keras.layers import Input, Dense, merge
from keras.models import Model
from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
from keras.layers import Activation, Dropout, Flatten, Dense
def default_categorical():
img_in = Input(shape=(120, 160, 3), name='img_in') ... | github_jupyter |
# Notebook to visualize location data
```
import csv
# count the number of Starbucks in DC
with open('starbucks.csv') as file:
csvinput = csv.reader(file)
acc = 0
for record in csvinput:
if 'DC' in record[3]:
acc += 1
print( acc )
def parse_locations(csv_iterator,state=''):
... | 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 |
### Calculating intensity in an inclined direction $\cos\theta \neq 1$
For this first part we are going to use our good old FALC model and calculate intensity in direction other than $\mu = 1$. This is also an essential part of scattering problems!
### We will assume that we are dealing with continuum everywhere!
... | github_jupyter |
# Working with code cells
In this notebook you'll get some experience working with code cells.
First, run the cell below. As I mentioned before, you can run the cell by selecting it the click the "run cell" button above. However, it's easier to run it by pressing **Shift + Enter** so you don't have to take your hands... | github_jupyter |
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