code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
%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
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
# Photometric Plugin
For optical photometry, we provide the **PhotometryLike** plugin that handles forward folding of a spectral model through filter curves. Let's have a look at the avaiable procedures.
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from threeML import *
# we will need ... | github_jupyter |
##### Copyright 2018 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 |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
# Introduction to Convolutional Neural Networks (CNNs) in PyTorch
### Representing images digitally
While convolutional neural networks (CNNs) see a wide variety of uses, they were originally designed for images, and CNNs are still most commonly used for vision-related tasks.
For today, we'll primarily be focusing on... | github_jupyter |
```
import warnings
import pandas as pd
import numpy as np
import os
import sys # error msg
import operator # sorting
from math import *
from read_trace import *
from avgblkmodel import *
warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)
```
# gpu info
```
gtx950 = DeviceInfo()
gtx950.sm_num ... | 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
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
# Tutorial - Time Series Forecasting - Autoregression (AR)
The goal is to forecast time series with the Autoregression (AR) Approach. 1) JetRail Commuter, 2) Air Passengers, 3) Function Autoregression with Air Passengers, and 5) Function Autoregression with Wine Sales.
References Jason Brownlee - https://machinelearn... | 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 |
用带有三种类型噪声(度，边权重，点权重)的传销模型网络测试RoleMagnet的抗噪性
```
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
```
## Creating a graph
模拟23人的小型传销组织，带少量噪声
```
%matplotlib inline
plt.rcParams['figure.dpi'] = 150
plt.rcParams['figure.figsize'] = (4, 3)
G = nx.DiGraph()
G.add_weighted_edges_from([('11','s1',0... | github_jupyter |
# Tune TensorFlow Serving
## Guidelines
### CPU-only
If your system is CPU-only (no GPU), then consider the following values:
* `num_batch_threads` equal to the number of CPU cores
* `max_batch_size` to infinity (ie. MAX_INT)
* `batch_timeout_micros` to 0.
Then experiment with batch_timeout_micros values in the 1-10... | github_jupyter |
```
import scrublet as scr
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
import scipy
def MovePlots(plotpattern, subplotdir):
os.system('mkdir -p '+str(sc.settings.figdir)+'/'+subplotdir)
os.system('mv '+str(sc.settings.figdir)+'/*'+plotpattern... | github_jupyter |
## The Basics
At the core of Python (and any programming language) there are some key characteristics of how a program is structured that enable the proper execution of that program. These characteristics include the structure of the code itself, the core data types from which others are built, and core operators that... | github_jupyter |
# Bayesian Optimization
[Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization) is a powerful strategy for minimizing (or maximizing) objective functions that are costly to evaluate. It is an important component of [automated machine learning](https://en.wikipedia.org/wiki/Automated_machine_learn... | github_jupyter |
# UMAP
This script generates UMAP representations from spectrograms (previously generated).
### Installing and loading libraries
```
import os
import pandas as pd
import sys
import numpy as np
from pandas.core.common import flatten
import pickle
import umap
from pathlib import Path
import datetime
import scipy
impo... | github_jupyter |
There are 76,670 different agent ids in the training data.
```
import os
import pickle
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(rc={"figure.dpi":100, 'savefig.dpi':100})
sns.set_context('notebook')
# Keys to the pickle object... | github_jupyter |
# Basic Tensor operations and GradientTape.
In this graded assignment, you will perform different tensor operations as well as use [GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape). These are important building blocks for the next parts of this course so it's important to master the basics. Le... | github_jupyter |
# Exploratory Data Analysis
In this notebook, I have illuminated some of the strategies that one can use to explore the data and gain some insights about it.
We will start from finding metadata about the data, to determining what techniques to use, to getting some important insights about the data. This is based on t... | github_jupyter |
#### Bogumiła Walkowiak bogumila.walkowiak@grenoble-inp.org
#### Joachim Mąkowski joachim-kajetan.makowski@grenoble-inp.org
# Intelligent Systems: Reasoning and Recognition
## Recognizing Digits using Neural Networks
## 1. Introduction
<font size=4>The MNIST (Modified National Institute of Standards and Technology... | github_jupyter |
```
# Copyright 2019 Google Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing,... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from datetime import datetime
%matplotlib inline
import matplotlib
from datetime import datetime
import os
from scipy import stats
from definitions import HUMAN_DATA_DIR, ROOT_DIR
from dat... | github_jupyter |
# UCI Daphnet dataset (Freezing of gait for Parkinson's disease patients)
```
import numpy as np
import pandas as pd
import os
from typing import List
from pathlib import Path
from config import data_raw_folder, data_processed_folder
from timeeval import Datasets
import matplotlib
import matplotlib.pyplot as plt
%matp... | github_jupyter |
#### Amy Green - 200930437
# <center> 5990M: Introduction to Programming for Geographical Information Analysis - Core Skills </center>
## <center><u> __**Assignment 2: Investigating the Black Death**__ </u></center>
-------------------------------------------------------------
### Project Aim
<p>The aim of the p... | github_jupyter |
# SDLib
> Shilling simulated attacks and detection methods
## Setup
```
!mkdir -p results
```
### Imports
```
from collections import defaultdict
import numpy as np
import random
import os
import os.path
from os.path import abspath
from os import makedirs,remove
from re import compile,findall,split
import matplotli... | github_jupyter |
# Calculate the AMOC in density space
$VVEL*DZT*DXT (x,y,z)$ -> $VVEL*DZT*DXT (x,y,$\sigma$)$ -> $\sum_{x=W}^E$ -> $\sum_{\sigma=\sigma_{max/min}}^\sigma$
```
import os
import sys
import xgcm
import numpy as np
import xarray as xr
import cmocean
import pop_tools
import matplotlib
import matplotlib.pyplot as plt
%matp... | github_jupyter |
# ENGR 213 Project Demonstration: Toast Falling from Counter
## Iteration AND slipping of toast
This is a Jupyter notebook created to explore the utility of notebooks as an engineering/physics tool. As I consider integrating this material into physics and engineering courses I am having a hard time clarifying the outc... | github_jupyter |
# Deep learning for Natural Language Processing
* Simple text representations, bag of words
* Word embedding and... not just another word2vec this time
* 1-dimensional convolutions for text
* Aggregating several data sources "the hard way"
* Solving ~somewhat~ real ML problem with ~almost~ end-to-end deep learni... | github_jupyter |
# 3장 케라스와 텐서플로우
## 주요 내용
- 딥러닝 필수 요소
- 케라스와 텐서플로우 간략 소개
- 텐서플로우, 케라스, GPU를 활용한 딥러닝 작업환경
- 케라스와 텐서플로우를 이용한 신경망의 핵심 구성요소 구현
## 3.1 텐서플로우 소개
### 텐서플로우
- 구글을 중심으로 개발된 머신러닝 __플랫폼__(platform)
- TF-Agents: 강화학습 연구 지원
- TFX: 머신러닝 프로젝트 진행과정(workflow) 운영 지원
- TF-Hub: 훈련된 모델 제공
- 파이썬 기반
- 텐서 연산 지원
### 넘파이(Numpy)... | github_jupyter |
<h1>Linear Algebra (CpE210A)
<h3>Midterms Project
Coded and submitted by:<br>
<i>Galario, Adrian Q.<br>
201814169 <br>
58051</i>
Directions
This Jupyter Notebook will serve as your base code for your Midterm Project. You must further format and provide complete discussion on the given topic.
- Provide all ne... | github_jupyter |
<a href="https://colab.research.google.com/github/Prady96/Pothole-Detection/blob/avi_testing/Final_file_for_tata_innoverse.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip -V
!python -V
!pip install --upgrade youtube-dl
!youtube-dl https://d... | github_jupyter |
# Credit Risk Resampling Techniques
```
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from pathlib import Path
from collections import Counter
```
# Read the CSV into DataFrame
```
# Load the data
file_path = Path('Resources/lending_data.csv')
df = pd.read_csv(file_path)
df... | github_jupyter |
# Gráficos de desempenho das Caches
### Import libs
```
%matplotlib inline
##Bibliotecas importadas
# Biblioteca usada para abrir arquivos CSV
import csv
# Bibilioteca para fazer leitura de datas
from datetime import datetime, timedelta
# Fazer o ajuste de datas no gráfico
import matplotlib.dates as mdate
# Bibliotec... | github_jupyter |
## Loading libraries and looking at given data
```
import numpy as np
import pandas as pd
import seaborn as sns
import re
appendix_3=pd.read_excel("Appendix_3_august.xlsx")
appendix_3
print(appendix_3["Language"].value_counts(),)
print(appendix_3["Country"].value_counts())
pd.set_option("display.max_rows", None, "disp... | github_jupyter |
# ADN
Implemente un programa que identifique a una persona en función de su ADN, según se indica a continuación.
<code>$ python dna.py databases/large.csv sequences/5.txt
Lavender</code>
## Empezando
- Dentro de la carpeta data/adn se encuentra la información necesaria para resolver este ejercicio la cual incluye u... | github_jupyter |
```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import matplotlib
matplotlib.rcParams["figure.figsize"] = (20,10)
df1 = pd.read_csv("Bengaluru_House_Data.csv")
df1.head()
df1.shape
df1.columns
df1["area_type"].unique()
df1["area_type"].value_counts()
# Drop features t... | 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 |
# Implementing the Gradient Descent Algorithm
In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.
```
import matplotlib.pyplot as plt
import numpy as np
import ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_parent" href="https://github.com/giswqs/geemap/tree/master/tutorials/ImageCollection/01_image_collection_overview.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target=... | github_jupyter |
# Regarding this Notebook
This is a replication of the original analysis performed in the paper by [Waade & Enevoldsen 2020](missing). This replication script will not be updated as it is intended for reproducibility. Any deviations from the paper is marked with bold for transparency.
Footnotes and internal documentati... | github_jupyter |
<a href="https://colab.research.google.com/github/robertozerbini/blog/blob/add-license-1/Roberto_Zerbini's_Blog_Polynomial_Regression.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import matplotlib.pyplot as plt
import nump... | github_jupyter |
```
import keras
from IPython.display import SVG
from keras.optimizers import Adam
from keras.utils.vis_utils import model_to_dot
from tqdm import tqdm
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extrac... | github_jupyter |
# Building the dataset
In this notebook, I'm going to be working with three datasets to create the dataset that the chatbot will be trained on.
```
import pandas as pd
files_path = 'D:/Sarcastic Chatbot/Input/'
```
# First dataset
**The Wordball Joke Dataset**, [link](https://www.kaggle.com/bfinan/jokes-question-and... | github_jupyter |
# 선형연립방정식 사례: 간단한 트러스<br>Example of Systems of Linear Equations : Simple Truss
```
# 그래프, 수학 기능 추가
# Add graph and math features
import pylab as py
import numpy as np
import numpy.linalg as nl
# 기호 연산 기능 추가
# Add symbolic operation capability
import sympy as sy
```
화살표를 그리는 함수<br>Function to draw an arrow
```
def dr... | github_jupyter |
# Python for Data Professionals
## 02 Programming Basics
<p style="border-bottom: 1px solid lightgrey;"></p>
<dl>
<dt>Course Outline</dt>
<dt>1 - Overview and Course Setup</dt>
<dt>2 - Programming Basics <i>(This section)</i></dt>
<dd>2.1 - Getting help<... | github_jupyter |
# Manual Jupyter Notebook:
https://athena.brynmawr.edu/jupyter/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb
#Jupyter Notebook Users Manual
This page describes the functionality of the [Jupyter](http://jupyter.org) electronic document system. Jupyter documents are called "notebooks" and can be seen as ... | github_jupyter |
# Recommendations with IBM
In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform.
You may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page. Either way assure that your ... | github_jupyter |
# 0.0. IMPORTS
```
import inflection
import math
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from IPython.core.display import HTML
from IPython.display import Image
```
## 0.1. Helper Functions
```
def load_csv(path):... | github_jupyter |
```
import json
import pandas as pd
import numpy as np
import re
from sqlalchemy import create_engine
import psycopg2
from config import db_password
import time
# Add the clean movie function that takes in the argument, "movie".
def clean_movie(movie):
movie = dict(movie) #create a non-destructive copy
alt... | github_jupyter |
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:white;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sa... | github_jupyter |
# LAB 4c: Create Keras Wide and Deep model.
**Learning Objectives**
1. Set CSV Columns, label column, and column defaults
1. Make dataset of features and label from CSV files
1. Create input layers for raw features
1. Create feature columns for inputs
1. Create wide layer, deep dense hidden layers, and output layer
... | github_jupyter |
```
import cv2
bild = cv2.imread("data\ped2//training//frames//01//000.jpg")
bild2 = cv2.imread("data\ped2//training//frames//01//001.jpg")
import numpy as np
lista = list()
lista.append(bild)
lista.append(bild2)
lista = np.array(lista)
import cv2
import os
import numpy as np
bilder = list()
for folder in os.listdir(... | github_jupyter |
# Neural Network Example
Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.
- Author: Aymeric Damien
- Project: https://github.com/aymericdamien/TensorFlow-Examples/
## Neural Network Overview
<img src="http://cs231n.github.io/assets/nn1/neural_net2.jpeg" alt="nn" ... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_3_text_generation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 10: Time Serie... | github_jupyter |
```
%matplotlib inline
import pandas as pd
from os.path import join
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import skbio
# from q2d2 import get_within_between_distances, filter_dm_and_map
from stats import mc_t_two_sample
from skbio.stats.distance import anosim, permanova
from skbio.sta... | github_jupyter |
## Training with Chainer
[VGG](https://arxiv.org/pdf/1409.1556v6.pdf) is an architecture for deep convolution networks. In this example, we train a convolutional network to perform image classification using the CIFAR-10 dataset. CIFAR-10 consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.... | github_jupyter |
<img src="images/utfsm.png" alt="" width="200px" align="right"/>
# USM Numérica
## Tema del Notebook
### Objetivos
1. Conocer el funcionamiento de la librerìa sklearn de Machine Learning
2. Aplicar la librerìa sklearn para solucionar problemas de Machine Learning
## Sobre el autor
### Sebastián Flores
#### ICM UTFSM
... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")
plt.grid(True)
#!wget --no-check-certificate \
# ... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import h5py
archive = h5py.File('/Users/bmmorris/git/aesop/notebooks/spectra.hdf5', 'r+')
targets = list(archive)
list(archive['HD122120'])#['2017-09-11T03:27:13.140']['flux'][:]
from scipy.ndimage import gaussian_filter1d
spectrum1 = archive['... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Download-and-Clean-Data" data-toc-modified-id="Download-and-Clean-Data-1"><span class="toc-item-num">1 </span>Download and Clean Data</a></span></li><li><span><a href="#Making-Recommendations" da... | github_jupyter |
# CI coverage, length and bias
For event related design.
```
# Directories of the data for different scenario's
DATAwd <- list(
'Take[8mmBox10]' = "/Volumes/2_TB_WD_Elements_10B8_Han/PhD/IBMAvsGLM/Results/Cambridge/ThirdLevel/8mm/boxcar10",
'Take[8mmEvent2]' = "/Volumes/2_TB_WD_Elements_10B8_Han/PhD/IBMAvsGLM/Resu... | github_jupyter |
## Mixture Density Networks with PyTorch ##
Related posts:
JavaScript [implementation](http://blog.otoro.net/2015/06/14/mixture-density-networks/).
TensorFlow [implementation](http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/).
```
import matplotlib.pyplot as plt
import numpy as np
import t... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import preprocess
preprocess.main('CC*.pkl','BIOS.pkl')
!ls
import pickle
all_bios = pickle.load( open( "BIOS.pkl", "rb" ) )
```
## Dictionary Details
1. r["title"] tells you the noramlized title
2. r["gender"] tells you the gender (binary for simplicity, determined from the p... | github_jupyter |
```
#pandas
#indexes are visible
#2 types of data structure
#1. sereis - vector - 1d
#2.data framee - 2d
#3. index - index is visible
import numpy as np
import pandas as pd
#descriptive statistics
data = pd.Series([0.25,0.5,0.75,1])
data
data.values
data.index
data.shape
data.describe
data.describe()
#explicit indexin... | github_jupyter |
## Environment
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%autosave 20
import csv
import pandas as pd
from keras.backend import tf as ktf
import sys
import cv2
import six
# keras
import keras
from ke... | github_jupyter |
```
import numpy as np
import os
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics.classification import classification_report, accuracy_score
from sklearn.model_selection import cross_val_predict
from nltk.corpus import stopwords
stop_words=stopwords.... | github_jupyter |
```
import os
os.environ['MACOSX_DEPLOYMENT_TARGET'] = '10.9' # HACK: needed for stan...
import astropy.coordinates as coord
from astropy.table import Table, join, hstack
import astropy.units as u
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pystan
from pyia import GaiaData
sm = pystan... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from tqdm import tnrange, tqdm_notebook
import gc
import operator
sns.set_context('talk')
pd.set_option('display.max_columns', 500)
import warnings
warnings.filterwarnings('ignore', message='Changing the ... | github_jupyter |
# Test For The Best Machine Learning Algorithm For Prediction
This notebook takes about 40 minutes to run, but we've already run it and saved the data for you. Please read through it, though, so that you understand how we came to the conclusions we'll use moving forward.
## Six Algorithms
We're going to compare six ... | github_jupyter |
# Model building
https://www.kaggle.com/vadbeg/pytorch-nn-with-embeddings-and-catboost/notebook#PyTorch
mostly based off this example, plus parts of code form tutorial 5 lab 3
```
# import load_data function from
%load_ext autoreload
%autoreload 2
# fix system path
import sys
sys.path.append("/home/jovyan/work")
i... | github_jupyter |
* 比较不同组合组合优化器在不同规模问题上的性能；
* 下面的结果主要比较``alphamind``和``python``中其他优化器的性能差别，我们将尽可能使用``cvxopt``中的优化器，其次选择``scipy``；
* 由于``scipy``在``ashare_ex``上面性能太差，所以一般忽略``scipy``在这个股票池上的表现；
* 时间单位都是毫秒。
* 请在环境变量中设置`DB_URI`指向数据库
```
import os
import timeit
import numpy as np
import pandas as pd
import cvxpy
from alphamind.api import... | github_jupyter |
# Control Flow
### Python if else
```
def multiply(a, b):
"""Function to multiply"""
print(a * b)
print(multiply.__doc__)
multiply(5,2)
def func():
"""Function to check i is greater or smaller"""
i=10
if i>5:
print("i is greater than 5")
else:
print("i is less than 15")
print(f... | github_jupyter |
```
# Code for artery tracking
#simplified from 1-1
%load_ext autoreload
%autoreload 2
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import json
import cv2
import os
import matplotlib.pyplot as plt
import copy
import numpy as np
im... | 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 |
<a href="https://colab.research.google.com/github/Abhishekauti21/dsmp-pre-work/blob/master/practice_project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
class test:
def __init__(self,a):
self.a=a
def display(self):
print(... | github_jupyter |
# Detecting COVID-19 with Chest X Ray using PyTorch
Image classification of Chest X Rays in one of three classes: Normal, Viral Pneumonia, COVID-19
Dataset from [COVID-19 Radiography Dataset](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database) on Kaggle
# Importing Libraries
```
from google.colab im... | github_jupyter |
# Plotting Target Pixel Files with Lightkurve
## Learning Goals
By the end of this tutorial, you will:
- Learn how to download and plot target pixel files from the data archive using [Lightkurve](https://docs.lightkurve.org).
- Be able to plot the target pixel file background.
- Be able to extract and plot flux from... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](#scrollTo=ByZjmtFgB_Y5).
```
// #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file ... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy as sp
from scipy import sparse
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import string
import re
import glob
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVec... | github_jupyter |
## LDA
The graphical model representation of LDA is given blow:
<img src="figures/LDA.png">
The basic idea of LDA is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words.
LDA assumes the following generative process for each document $\m... | github_jupyter |
# Example 1: Sandstone Model
```
# Importing
import theano.tensor as T
import theano
import sys, os
sys.path.append("../GeMpy")
sys.path.append("../")
# Importing GeMpy modules
import gempy as GeMpy
# Reloading (only for development purposes)
import importlib
importlib.reload(GeMpy)
# Usuful packages
import numpy as... | github_jupyter |
<a href="https://colab.research.google.com/github/100rab-S/TensorFlow-Advanced-Techniques/blob/main/C1W3_L3_CustomLayerWithActivation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Ungraded Lab: Activation in Custom Layers
In this lab, we extend... | github_jupyter |
# Using Google Cloud Functions to support event-based triggering of Cloud AI Platform Pipelines
> This post shows how you can run a Cloud AI Platform Pipeline from a Google Cloud Function, providing a way for Pipeline runs to be triggered by events.
- toc: true
- badges: true
- comments: true
- categories: [ml, pipel... | github_jupyter |
# Herramientas Estadisticas
# Contenido:
1.Estadistica:
- Valor medio.
- Mediana.
- Desviacion estandar.
2.Histogramas:
- Histrogramas con python.
- Histogramas con numpy.
- Como normalizar un histograma.
3.Distribuciones:
- Como obtener una distribucion a partir... | github_jupyter |
# 01 - Sentence Classification Model Building
# Parse & clearn labeled training data
```
import xml.etree.ElementTree as ET
tree = ET.parse('../data/Restaurants_Train.xml')
root = tree.getroot()
root
# Use this dataframe for multilabel classification
# Must use scikitlearn's multilabel binarizer
labeled_reviews = []... | github_jupyter |
# Network inference of categorical variables: non-sequential data
```
import sys
import numpy as np
from scipy import linalg
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
%matplotlib inline
import inference
import fem
# setting parameter:
np.random.seed(1)
n = 20 # number of positi... | github_jupyter |
1. You are provided the titanic dataset. Load the dataset and perform splitting into training and test sets with 70:30 ratio randomly using test train split.
2. Use the Logistic regression created from scratch (from the prev question) in this question as well.
3. Data cleaning plays a major role in this question. Repo... | github_jupyter |
起手式，導入 numpy, matplotlib
```
from PIL import Image
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('bmh')
matplotlib.rcParams['figure.figsize']=(8,5)
```
使用之前下載的 mnist 資料，載入訓練資料 `train_set` 和測試資料 `test_set`
```
import gzip
import pickle
with gzip.open('..... | github_jupyter |
# Python For Bioinformatics
Introduction to Python for Bioinformatics - available at https://github.com/kipkurui/Python4Bioinformatics.
<small><small><i>
## Attribution
These tutorials are an adaptation of the Introduction to Python for Maths by [Andreas Ernst](http://users.monash.edu.au/~andreas), available from ht... | github_jupyter |
Скородумов Александр
БВТ1904
Лабораторная работа №2 Методы поиска
№1
```
#Импорты
from IPython.display import HTML, display
from tabulate import tabulate
import random
import time
#Рандомная генерация
def random_matrix(m = 50, n = 50, min_limit = -250, max_limit = 1016):
return [[random.randint(min_limit, max_l... | github_jupyter |
```
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
tf.config.run_functions_eagerly(False)
#tfds.disable_progress_bar()
tf.version.VERSION
import pandas as pd
dataset = pd.read_csv("/content/drive/MyDrive/sentiment-dataset/airline_sentiment_analysis.csv")
print (dataset[:10])
print (data... | github_jupyter |
```
import tensorflow as tf
from tensorflow.python.keras.utils import HDF5Matrix
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import (Input, Lambda, Conv2D, MaxPooling2D, Flatten, Dense, Dropout,
Lambda, Activation, BatchNormalization,... | github_jupyter |
```
import numpy as np
class PCA:
def __init__(self, n_components):
"""
初始化PCA
"""
assert n_components>=1, "n_components 必须大于1"
self.n_components=n_components
self.components_=None
def fit(self, X, eta=0.01,n_iters=1e4):
"""
获得数据集X的n_... | github_jupyter |
# Implementing a Neural Network
In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset.
```
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib ... | github_jupyter |
```
import pandas as pd
import numpy as np
# import pymssql
# from fuzzywuzzy import fuzz
import json
import tweepy
from collections import defaultdict
from datetime import datetime
import re
# import pyodbc
from wordcloud import WordCloud
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import Word... | github_jupyter |
### Project: Create a neural network class
---
Based on previous code examples, develop a neural network class that is able to classify any dataset provided. The class should create objects based on the desired network architecture:
1. Number of inputs
2. Number of hidden layers
3. Number of neurons per layer
4. Num... | github_jupyter |
# Titanic 4
> ### `Pclass, Sex, Age`
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
sns.set(font_scale=2.5)
import missingno as msno
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
df_train=pd.read_csv('C:/Users/ehfus/D... | github_jupyter |
# Section 1.2: Dimension reduction and principal component analysis (PCA)
One of the iron laws of data science is know as the "curse of dimensionality": as the number of considered features (dimensions) of a feature space increases, the number of data configurations can grow exponentially and thus the number observati... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/geemap/tree/master/examples/notebooks/geemap_and_ipyleaflet.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href=... | github_jupyter |
# Gender and Age Detection
```
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import cv2
from tensorflow.keras.models import Sequential, load_model, Model
from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Dropout, BatchNormalization, Flatten, Input
from sklearn.model_selec... | github_jupyter |
# An Introduction to SageMaker LDA
***Finding topics in synthetic document data using Spectral LDA algorithms.***
---
1. [Introduction](#Introduction)
1. [Setup](#Setup)
1. [Training](#Training)
1. [Inference](#Inference)
1. [Epilogue](#Epilogue)
# Introduction
***
Amazon SageMaker LDA is an unsupervised learning ... | github_jupyter |
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