code stringlengths 2.5k 150k | kind stringclasses 1
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### First steps
The easiest way to run Python in your computer, is to install Anaconda:
https://www.anaconda.com/download for your OS (Windows, macOS, Linux).
Then from Anaconda's launcher you can run Jupyter notebook. This tutorial written in Jupyter notebooks.
### Magic hapenning here
In Jupyter notebook, you can r... | github_jupyter |
```
from birdcall.data import *
from birdcall.metrics import *
from birdcall.ops import *
import torch
import torchvision
from torch import nn
import numpy as np
import pandas as pd
from pathlib import Path
import soundfile as sf
BS = 16
MAX_LR = 1e-3
classes = pd.read_pickle('data/classes.pkl')
splits = pd.read_pickl... | github_jupyter |
# 作業 : (Kaggle)鐵達尼生存預測
https://www.kaggle.com/c/titanic
# 作業1
* 參考範例，將鐵達尼的船票票號( 'Ticket' )欄位使用特徵雜湊 / 標籤編碼 / 目標均值編碼三種轉換後，
與其他數值型欄位一起預估生存機率
```
# 做完特徵工程前的所有準備 (與前範例相同)
import pandas as pd
import numpy as np
import copy, time
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import cross_val_... | github_jupyter |
### What if we buy a share every day at the highest price?
```
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
symbols = ['ABBV','AAPL','ADBE','APD','BRK-B','COST','CTL','DRI','IRM','KIM','MA','MCD','NFLX','NVDA','SO','V','VLO']
dates = ['2018-01-01', '2018-12-31']
data_directory = './data/hist... | github_jupyter |
# 1. Loading and filtering data
```
import pandas as pd
```
## 1.1. Firstly we load the data and filter the columns
```
df = pd.read_csv("/home/alberto/Documentos/MatchingLearning/Practicas/Moriarty2.csv",
usecols=["UUID","ActionType"])
df2 = pd.read_csv("/home/alberto/Documentos/MatchingLearning/... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
# Modeling and Simulation in Python
Chapter 23
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 a... | github_jupyter |
```
from IPython.display import Markdown as md
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#from sklearn.linear_model import LogisticRegression
#from sklearn.metrics import auc as sklearn_auc
from sklearn.model_selection import train_test_split
from s... | github_jupyter |
# Logistic regression with $\ell_1$ regularization
In this example, we use CVXPY to train a logistic regression classifier with $\ell_1$ regularization. We are given data $(x_i,y_i)$, $i=1,\ldots, m$. The $x_i \in {\bf R}^n$ are feature vectors, while the $y_i \in \{0, 1\}$ are associated boolean classes; we assume th... | 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 |
<img src="imagenes/rn3.png" width="200">
<img src="http://www.identidadbuho.uson.mx/assets/letragrama-rgb-150.jpg" width="200">
# [Curso de Redes Neuronales](https://rn-unison.github.io)
# Redes neuronales multicapa y el algoritmo de *b-prop*
[**Julio Waissman Vilanova**](http://mat.uson.mx/~juliowaissman/), 27 de f... | github_jupyter |
<a href="https://colab.research.google.com/github/arunk-vnk-chn/insaid-interview-questions/blob/master/20%20April%20-%20Introduction%20to%20Machine%20Learning%20(part%202).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
```
# 20 April – Introd... | github_jupyter |
# Image Captioning with LSTMs
In the previous exercise you implemented a vanilla RNN and applied it to image captioning. In this notebook you will implement the LSTM update rule and use it for image captioning.
```
# As usual, a bit of setup
import time, os, json
import numpy as np
import matplotlib.pyplot as plt
fro... | github_jupyter |
# Part 3: Advanced Remote Execution Tools
In the last section we trained a toy model using Federated Learning. We did this by calling .send() and .get() on our model, sending it to the location of training data, updating it, and then bringing it back. However, at the end of the example we realized that we needed to go... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
# Tutorial 3 of 3: Advanced Topics and Usage
**Learning Outcomes**
* Use different methods to add boundary pores to a network
* Manipulate network topology by adding and removing pores and throats
* Explore the ModelsDict design, including copying models between objects, and changing model parameters
* Write a custom... | github_jupyter |
## What is convolution and how it works ?
[Convolution][1] is the process of adding each element of the image to its local neighbors, weighted by the [kernel][2]. A kernel, convolution matrix, filter, or mask is a small matrix. It is used for blurring, sharpening, embossing, edge detection, and more. This is accomplis... | github_jupyter |
<a href="https://colab.research.google.com/github/DingLi23/s2search/blob/pipelining/pipelining/exp-cshc/exp-cshc_cshc_1w_ale_plotting.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Experiment Description
> This notebook is for experiment \<ex... | github_jupyter |
# SAX circuit simulator
[SAX](https://flaport.github.io/sax/) is a circuit solver written in JAX, writing your component models in SAX enables you not only to get the function values but the gradients, this is useful for circuit optimization.
This tutorial has been adapted from SAX tutorial.
Note that SAX does not w... | github_jupyter |
```
from torchvision.models import *
import wandb
from sklearn.model_selection import train_test_split
import os,cv2
import numpy as np
import matplotlib.pyplot as plt
from torch.optim import *
from torch.nn import *
import torch,torchvision
from tqdm import tqdm
device = 'cuda'
PROJECT_NAME = 'Musical-Instruments-Imag... | github_jupyter |
# (Optional) Testing the Function Endpoint with your Own Audio Clips
Instead of using pre-recorded clips we show you in this notebook how to invoke the deployed Function
with your **own** audio clips.
In the cells below, we will use the [PyAudio library](https://pypi.org/project/PyAudio/) to record a short 1 second... | github_jupyter |
<font size=3 color="midnightblue" face="arial">
<h1 align="center">Escuela de Ciencias Básicas, Tecnología e Ingeniería</h1>
</font>
<font size=3 color="navy" face="arial">
<h1 align="center">ECBTI</h1>
</font>
<font size=2 color="darkor... | github_jupyter |
# Interdisciplinary Health Data Competition - Data Cleaning
## Import necessary libraries
```
import pandas as pd
import numpy as np
import warnings
```
## Agenda
Step 1 - Read in Data Files
- Read in drug and prescription files
- Inspect their initial format
- Inspect their initial data types
- Inspect data distri... | github_jupyter |
# Import Libraries
```
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
```
# Sentences
```
sentence = [("the", "DT"), ("little", "JJ"), ("yellow", "JJ"),("dog", "NN"), ("barked", "VBD"), ("at", "IN"), ("the", "DT"), ("cat", "NN")]
sentence2 = "Four score and sev... | github_jupyter |
<a href="https://colab.research.google.com/github/shivangisachan20/ML-DL-Projects/blob/master/Copy_of_pytorch_quick_start.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# PyTorch 1.2 Quickstart with Google Colab
In this code tutorial we will learn ... | github_jupyter |
# Talking Head Anime from a Single Image 2: More Expressive (Manual Poser Tool)
**Instruction**
1. Run the four cells below, one by one, in order by clicking the "Play" button to the left of it. Wait for each cell to finish before going to the next one.
2. Scroll down to the end of the last cell, and play with the GU... | github_jupyter |
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.st... | github_jupyter |
# Notebook example
Installing some necessary packages:
```
!pip install ipywidgets
!jupyter nbextension enable --py widgetsnbextension
!jupyter labextension install @jupyter-widgets/jupyterlab-manager
!pip install xgboost
```
**It is necessary to change the working directory so the project structure works properly:*... | github_jupyter |
# Segregation Index Decomposition
## Table of Contents
* [Decomposition framework of the PySAL *segregation* module](#Decomposition-framework-of-the-PySAL-*segregation*-module)
* [Map of the composition of the Metropolitan area of Los Angeles](#Map-of-the-composition-of-the-Metropolitan-area-of-Los-Angeles)
* [Map o... | github_jupyter |
# Visualising PAG neurons in CCF space
In this notebook we will load the .csv file containing the metadata from our PAG_scRNAseq project and use the CCF coordinates obtained after registration with Sharp-Track to visualise our sequenced cells with Brainrender. We will also write some code to generate some figures for t... | github_jupyter |
```
import numpy as np
from matplotlib import pyplot as plt
%matplotlib
# if you are plotting at the rig computer and want to plot the last debugging
# run images, set this to True.
plot_at_rig = True
processed_is_CDS_subtracted = True # whether to halve the processed_img size
# to help explore possible settings
resh... | github_jupyter |
# Introduction to Digital Earth Australia <img align="right" src="../Supplementary_data/dea_logo.jpg">
* **Acknowledgement**: This notebook was originally created by [Digital Eath Australia (DEA)](https://www.ga.gov.au/about/projects/geographic/digital-earth-australia) and has been modified for use in the EY Data Scie... | github_jupyter |
#Given a budget of 30 million dollar (or less) and genre, can I predict gross domestic profit using linear regression?
```
%matplotlib inline
import pickle
from pprint import pprint
import pandas as pd
import numpy as np
from dateutil.parser import parse
import math
# For plotting
import seaborn as sb
import matplotli... | github_jupyter |
## Import a model from ONNX and run using PyTorch
We demonstrate how to import a model from ONNX and convert to PyTorch
#### Imports
```
import os
import operator as op
import warnings; warnings.simplefilter(action='ignore', category=FutureWarning)
import numpy as np
import torch
from torch import nn
from torch.nn... | github_jupyter |
# Credit Risk Classification
Credit risk poses a classification problem that’s inherently imbalanced. This is because healthy loans easily outnumber risky loans. In this Challenge, you’ll use various techniques to train and evaluate models with imbalanced classes. You’ll use a dataset of historical lending activity fr... | github_jupyter |
<a href="https://colab.research.google.com/github/LeonVillanueva/CoLab/blob/master/Google_CoLab_DL_Recommender.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Loading Libraries
```
!pip install -q tensorflow==2.0.0-beta1
%%capture
import numpy ... | github_jupyter |
# Building Simple Neural Networks
In this section you will:
* Import the MNIST dataset from Keras.
* Format the data so it can be used by a Sequential model with Dense layers.
* Split the dataset into training and test sections data.
* Build a simple neural network using Keras Sequential model and Dense layers.
* Tra... | github_jupyter |
```
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import math
%matplotlib inline
```
# Volunteer 1
## 3M Littmann Data
```
image = Image.open('3Ms.bmp')
image
x = image.size[0]
y = image.size[1]
print(x)
print(y)
matrix = []
points = []
integrated_density = 0
for i... | github_jupyter |
```
#Set working directory
import os
path="/Users/sarakohnke/Desktop/data_type_you/processed-final/"
os.chdir(path)
os.getcwd()
#Import required packages
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#Import cleaned dataframe
dataframe=... | github_jupyter |
```
%matplotlib inline
from pyvista import set_plot_theme
set_plot_theme('document')
```
Volumetric Analysis
===================
Calculate mass properties such as the volume or area of datasets
```
# sphinx_gallery_thumbnail_number = 4
import numpy as np
from pyvista import examples
```
Computing mass properties su... | github_jupyter |
# Exemplo sobre a correlação cruzada
A correlação cruzada é definida por
\begin{equation}
R_{xy}(\tau)=\int_{-\infty}^{\infty}x(t)y(t+\tau)\mathrm{d} t
\tag{1}
\end{equation}
Considerede um navio a navegar por águas não muito conhecidas. Para navegar com segurança, o navio necessita ter uma noção da profundidade da ... | github_jupyter |
## Vehicle Detection
### Import
Import of the used packages.
```
import numpy as np
import os
import cv2
import pickle
import glob
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from skimage.feature import hog
import time
fro... | github_jupyter |
```
#Python Basics
#Functions in Python
#Functions take some inputs, then they produce some outputs
#The functions are just a piece of code that you can reuse
#You can implement your functions, but in many cases, people reuse other people's functions
#in this case, it is important how the function work and how we can i... | github_jupyter |
# Navigation
---
Congratulations for completing the first project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893)! In this notebook, you will learn how to control an agent in a more challenging environment, where it can learn directly from... | github_jupyter |
```
# Imports
from biocrnpyler import *
from genelet import *
from subsbml import System, createSubsystem, combineSystems, createNewSubsystem, createBasicSubsystem, SimpleModel, SimpleReaction
import numpy as np
import pylab as plt
from bokeh.layouts import row
from bokeh.io import export_png
import warnings
import l... | github_jupyter |
## 3. Exploring data tables with Pandas
1. Use Pandas to read the house prices data. How many columns and rows are there in this dataset?
2. The first step I usually do is to use commands like pandas.head() to print a few rows of data. Look around what kind of features are available and read data description.txt for m... | github_jupyter |
```
#Import section
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import pickle
%matplotlib inline
# Loading camera calibration coefficients(matrix and camera coefficients) from pickle file
def getCameraCalibrationCoefficientsFromPickleFile(filePath):
cameraCalibration = pickle.load( ope... | github_jupyter |
```
import numpy as np
import re
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans, DBSCAN
from sklearn.neighbors import NearestNeighbors
from requests import get
import unicodedata
from bs4 import BeautifulSoup
im... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import tensorflow as tf
import numpy as np
from glob import glob
from itertools import cycle
mels = glob('universal-mel/*.npy')
file_cycle = cycle(mels)
f = next(file_cycle)
path = 'hifigan-512-combined'
ckpt_path = tf.train.latest_checkpoint(path)
ckpt_path
def g... | github_jupyter |
# Import and convert Neo23x0 Sigma scripts
ianhelle@microsoft.com
This notebook is a is a quick and dirty Sigma to Log Analytics converter.
It uses the modules from sigmac package to do the conversion.
Only a subset of the Sigma rules are convertible currently. Failure to convert
could be for one or more of these rea... | github_jupyter |
## This notebook shows how to run evaluation on our models straight from Colab environment
```
# mount GD
from google.colab import drive
drive.mount('/content/drive')
# your GD path to clone the repo
project_path="/content/drive/MyDrive/UofT_MEng/MIE1517/Project/FINDER_github/"
# Clone repo
%cd {project_path}
!git c... | github_jupyter |
```
%matplotlib inline
import re
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from numpy import nan
from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support.wait import WebDriverWait
## create a pandas dataframe... | github_jupyter |
```
%pushd ../../
%env CUDA_VISIBLE_DEVICES=3
import json
import os
import sys
import tempfile
from tqdm.auto import tqdm
import torch
import torchvision
from torchvision import transforms
from PIL import Image
import numpy as np
torch.cuda.set_device(0)
from netdissect import setting
segopts = 'netpqc'
segmodel, se... | github_jupyter |
# How do distributions transform under a change of variables ?
Kyle Cranmer, March 2016
```
%pylab inline --no-import-all
```
We are interested in understanding how distributions transofrm under a change of variables.
Let's start with a simple example. Think of a spinner like on a game of twister.
<!--<img src="ht... | github_jupyter |
# Introduction to Docker
**Learning Objectives**
* Build and run Docker containers
* Pull Docker images from Docker Hub and Google Container Registry
* Push Docker images to Google Container Registry
## Overview
Docker is an open platform for developing, shipping, and running applications. With Docker, you can... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
[<img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg">](https://deepnote.com/launch?url=https%3A%2F%2Fgithub.com%2Fgordicaleksa%2Fget-started-with-JAX%2Fblob%2Fmain%2FTutorial_4_Flax_Zero2Hero_Colab.ipynb)
<a href="https://colab.research.google.com/github/gordicaleksa/get-started-with-JAX/blob/main/Tut... | github_jupyter |
```
import numpy as np
import scipy
from scipy.linalg import expm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA... | github_jupyter |
```
# Загрузка зависимостей
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_tes... | github_jupyter |
```
#Importing libraries
import tensorflow as tf
from tensorflow import keras
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.metrics import classification_report
#Setting the visualization
%matplotlib inline
%config InlineBackend.figur... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
!pip3 install glove_python
import os
os.chdir('/content/drive/MyDrive/sharif/DeepLearning/ipython(guide)')
import numpy as np
import codecs
import os
import random
import pandas
from keras import backend as K
from keras.models import Model
from keras.laye... | github_jupyter |
(code-advcd-best-practice)=
# Tools for Better Coding
## Introduction
This chapter covers the tools that will help you to write better code. This includes practical topics such as debugging code, logging, linting, and the magic of auto-formatting.
As ever, you may need to `conda install packagename` or `pip install ... | github_jupyter |
```
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import torch
print(torch.__version__)
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data_utils
from torch.utils.d... | github_jupyter |
```
# Initialize Otter Grader
import otter
grader = otter.Notebook()
```
# In-class Assignment (Feb 9)
Run the following two cells to load the required modules and read the data.
```
import pandas as pd
import numpy as np
... | github_jupyter |
# SLU10 - Classification: Exercise notebook
```
import pandas as pd
import numpy as np
```
In this notebook you will practice the following:
- What classification is for
- Logistic regression
- Cost function
- Binary classification
You thought that you would get away without implementing your ... | github_jupyter |
<a href="https://colab.research.google.com/github/adasegroup/ML2022_seminars/blob/master/seminar1/seminar01.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Seminar 1. Machine learning on Titanic data
The notebook provides an intro to the explorat... | github_jupyter |
# PTN Template
This notebook serves as a template for single dataset PTN experiments
It can be run on its own by setting STANDALONE to True (do a find for "STANDALONE" to see where)
But it is intended to be executed as part of a *papermill.py script. See any of the
experimentes with a papermill script to get sta... | github_jupyter |
```
import pandas as pd
from os.path import join
data_path = "../Dataset-1/selfie_dataset.txt"#join("..", "..", "Dataset-1", "selfie_dataset.txt")
image_path = "../Dataset-1/images"#join("..", "..", "Dataset-1", "selfie_dataset.txt")#join("..", "..", "Dataset-1", "images")
headers = [
"image_name", "score", "partia... | github_jupyter |
# Railroad Diagrams
The code in this notebook helps with drawing syntax-diagrams. It is a (slightly customized) copy of the [excellent library from Tab Atkins jr.](https://github.com/tabatkins/railroad-diagrams), which unfortunately is not available as a Python package.
**Prerequisites**
* This notebook needs some ... | github_jupyter |
# **Jupyter Notebook to demonstrate (simple) Linear Regression for Advertising/Sales Predicition**
Linear Regression is a simple yet powerful and mostly used algorithm in data science. There are a plethora of real-world applications of Linear Regression.
The purpose of this tutorial/notebook is to get a clear idea on... | github_jupyter |
```
# Copyright 2020 Google LLC. All Rights Reserved.
#
# 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 ... | github_jupyter |
# Project Euler Problems 1 and 2
> Multiples of 3 or 5 and even Fibonacci numbers in Python
- toc: false
- badges: true
- comments: true
- categories: [euler, programming]
In order to stay fresh with general programming skills I am going to attempt various Project Euler problems and walk through my solutions. For th... | github_jupyter |
<a href="https://colab.research.google.com/github/ginttone/test_visuallization/blob/master/2_T_autompg_xgboost.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## 데이터 로딩
```
import pandas as pd
df = pd.read_csv('./auto-mpg.csv', header=None)
df.colu... | github_jupyter |
##### Load the data to dataframes
```
from pathlib import Path
import pandas as pd
%run util.ipynb
from termcolor import colored
file_april_2020_parent_address = "../Data/ProlificAcademic/April 2020/Data/CRISIS_Parent_April_2020.csv"
file_april_2020_adult_address = "../Data/ProlificAcademic/April 2020/Data/CRISIS_Adu... | github_jupyter |
# Distributed Training with Keras
## Import dependencies
```
import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow import keras
import os
print(tf.__version__)
```
## Dataset - Fashion MNIST
```
#datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
#mnist_train, mnist_t... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
```
Manually Principal Component Analysis
```
#Reading wine data
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/wine/wine.data',
header=None)
# in the data first... | github_jupyter |
```
import os, shutil, csv
original_dataset_dir = '/Users/mithyyin/Documents/GitHub/TeamEve/Classfication_small_datasets_inception_v3/waste_original_dataset' #directory name of your biendata
#original_dataset_dir =r'C:\Users\oscarscaro\Documents\GitHub\TeamEve\Classfication_small_datasets_inception_v3\images_withoutre... | github_jupyter |
# Problem Statement
## About Company
Company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.
## Problem
Company wants to automate the loan eligibility process (real time) based... | github_jupyter |
# Run a batch of samples on the HPC cluster
This experiment is part of a series which should help us validate the Kingston, ON model.
## Set-up orchistration and compute environments
To set-up access to the remote compute server:
1. On the local host generate keys:
```
ssh-keygen -t rsa
```
1. Copy those keys to ... | github_jupyter |
# Monte Carlo Methods
In this notebook, you will write your own implementations of many Monte Carlo (MC) algorithms.
While we have provided some starter code, you are welcome to erase these hints and write your code from scratch.
### Part 0: Explore BlackjackEnv
We begin by importing the necessary packages.
```
i... | github_jupyter |
# Narowcast Server service migration to Distribution Services
## 1. Getting data from NC
### 1.1 List of NC Services
```
# Run this SQL code against Narrocast Server database
"""
select
names1.MR_OBJECT_ID AS serviceID,
names1.MR_OBJECT_NAME AS service_name,
parent1.MR_OBJECT_NAME AS foldername,
names2.MR_OBJECT... | github_jupyter |
# High-level Keras (Theano) Example
```
# Lots of warnings!
# Not sure why Keras creates model with float64?
%%writefile ~/.theanorc
[global]
device = cuda0
force_device= True
floatX = float32
warn_float64 = warn
import os
import sys
import numpy as np
os.environ['KERAS_BACKEND'] = "theano"
import theano
import keras ... | github_jupyter |
```
from sqlalchemy import create_engine
import pandas as pd
import matplotlib.pyplot as plot
import json
import pymysql
import statsmodels.formula.api as sm
from sklearn.cross_validation import train_test_split
from sklearn import metrics
from sklearn.cross_validation import cross_val_score
from collections import Ord... | github_jupyter |
### 6. Python API Training - Continuous Model Training [Solution]
<b>Author:</b> Thodoris Petropoulos <br>
<b>Contributors:</b> Rajiv Shah
This is the 6th exercise to complete in order to finish your `Python API Training for DataRobot` course! This exercise teaches you how to deploy a trained model, make predictions ... | 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 |
```
import pandas as pd
df = pd.read_csv('X.txt',sep=',')
df.head()
df.收益率 = df.收益率.str.strip(to_strip='%')
df.head()
df.sort_values(by=['收益率','天数','车型','城市车商','众筹金额'],ascending=False).head()
df.to_csv('X.csv',index=False,encoding='utf8')
df.info()
df.城市车商.value_counts()
df[(df.城市车商.str[0]>=u'\u4e00') & (df.城市车商.str[0]... | github_jupyter |
<div>
<img src="figures/svtLogo.png"/>
</div>
<h1><center>Mathematical Optimization for Engineers</center></h1>
<h2><center>Lab 14 - Uncertainty</center></h2>
We want to optimize the total annualized cost of a heating and electric power system. Three different technologies are present:
- a gas boiler
- a combined hea... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
```
注意点：
- b 零初始值
- w 初始化要用 tf，不要用 np
```
# 读取数据集MNIST，并放在当前目录data文件夹下MNIST文件夹中，如果该地址没有数据，则下载数据至该文件夹
# 一张图片有 28*28=784 个像素点，每个... | github_jupyter |
```
import pickle
import numpy as np
import collections
import matplotlib.pyplot as plt
import copy
import matplotlib.ticker as ticker
import mpmath as mp
from mpmath import gammainc
def power_law(x,s_min, s_max, alpha):
C = ((1-alpha)/(s_max**(1-alpha)-s_min**(1-alpha)))
return [C*x[i]**(-alpha) for i in range... | github_jupyter |
# TensorFlow Tutorial #02
# Convolutional Neural Network
These lessons are adapted from [tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials)
by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/) / [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.c... | github_jupyter |
Assessment Requirements
Each group is required to complete the following two tasks:
1. Generate a sparse representation for Paper Bodies (i.e. paper text without Title, Authors, Abstract and References). The sparse representation consists of two files: a. Vocabulary index file b. Sparse count vectors file
2. Gener... | github_jupyter |
# Python Strings
- Strings is one of the most important data types.
- Let's know how it is declared, defined, accessed and common operations performed on the python strings.
## Strings
- A string is a collection or series of characters.
- An important point to remember is that python strings are **immutable**. Once,... | github_jupyter |
# Visualize gene expression
This notebook visualizes the gene expression data for the template and simulated experiments in order to:
1. Validate that the structure of the gene expression data and simulated data are consistent
2. To visualize the signal that is in the experiments
```
%load_ext autoreload
%load_ext rp... | 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 |
# Regression, body and brain
## About this page
This is a Jupyter Notebook. It can be run as an interactive demo, or you can
read it as a web page.
You don't need to understand the code on this page, the text will tell you
what the code is doing.
You can also [run this demo
interactively](https://mybinder.org/v2/g... | github_jupyter |
# Retraining of top performing FFNN
## Imports
```
# General imports
import sys
import os
sys.path.insert(1, os.path.join(os.pardir, 'src'))
from itertools import product
# Data imports
import cv2
import torch
import mlflow
import numpy as np
from mlflow.tracking.client import MlflowClient
from torchvision import ... | github_jupyter |
## Face and Facial Keypoint detection
After you've trained a neural network to detect facial keypoints, you can then apply this network to *any* image that includes faces. The neural network expects a Tensor of a certain size as input and, so, to detect any face, you'll first have to do some pre-processing.
1. Detect... | github_jupyter |
```
2+2
answer = 2+2
print(answer)
new_variable = 9
new_variable = 6
print(new_variable)
```
This ia markdown cell
# This is a heading
My program is awesome
```
import numpy
data = numpy.loadtxt(fname='data/inflammation-01.csv',delimiter=',')
print(data)
print(type(data[0,0]))
print(data.dtype)
print(data.shape)
pr... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from numpy.linalg import inv
from astropy.table import Table, Column, vstack, hstack, unique, SortedArray,SCEngine
import astropy.units as u
from astropy.io import fits, ascii
import glob
import os
import numpy
from scipy.signal import med... | github_jupyter |
This notebook is scratch space for some relatively simple tweaks I'm making to ScienceBase Items in the NDC in order to better position the system for building new data indexing code against. It requires authentication for using the sciencebasepy package (in PyPI) to write changes to ScienceBase.
```
import sciencebas... | github_jupyter |
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