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# An analysis of the dataset presented in [this technical comment](https://arxiv.org/abs/2004.06601), but with our quality cuts applied
As a response to our paper [Dessert et al. _Science_ 2020](https://science.sciencemag.org/content/367/6485/1465) (DRS20), we received [a technical comment](https://arxiv.org/abs/2004.... | github_jupyter |
# Multi-ConvNet Sentiment Classifier
In this notebook, we concatenate the outputs of *multiple, parallel convolutional layers* to classify IMDB movie reviews by their sentiment.
#### Load dependencies
```
import tensorflow
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import... | github_jupyter |
# 工厂规划
等级：中级
## 目的和先决条件
此模型和Factory Planning II都是生产计划问题的示例。在生产计划问题中，必须选择要生产哪些产品，要生产多少产品以及要使用哪些资源，以在满足一系列限制的同时最大化利润或最小化成本。这些问题在广泛的制造环境中都很常见。
### What You Will Learn
在此特定示例中，我们将建模并解决生产组合问题：在每个阶段中，我们可以制造一系列产品。每种产品在不同的机器上生产需要不同的时间，并产生不同的利润。目的是创建最佳的多周期生产计划，以使利润最大化。由于维护，某些机器在特定时期内不可用。由于市场限制，每个产品每个月的销售量都有上限，并且存储容量也受到限制。... | github_jupyter |
### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
school_data_to_load = "Resources/schools_complete.csv"
student_data... | github_jupyter |
# Lesson 2: Computer Vision Fundamentals
## Submission, Markus Schwickert, 2018-02-22
### Photos
```
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import os
%matplotlib inline
#reading in an image
k1=0 # select here which of the images i... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
import seaborn as sns
import numpy as np
import matplotlib.dates as mdates
import datetime
#sns.set(color_codes=True)
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
import statistics as st
sns.set_style('whitegrid', {'axes.linewid... | github_jupyter |
```
import os, importlib, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
%load_ext autoreload
# Common paths
BASE_PATH = os.path.join(os.getcwd(), "..", "..")
MODULE_PATH = os.path.join(BASE_PATH, "modules")
DS_PATH = os.path.join(BASE_P... | github_jupyter |
## Good review of numpy https://www.youtube.com/watch?v=GB9ByFAIAH4
## Numpy library - Remember to do pip install numpy
### Numpy provides support for math and logical operations on arrays
#### https://www.tutorialspoint.com/numpy/index.htm
### It supports many more data types than python
#### https://www.tutor... | github_jupyter |
<a href="https://colab.research.google.com/github/MattFinney/practical_data_science_in_python/blob/main/Session_2_Practical_Data_Science.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/><a>
# Practical Data Science in Python
## Unsupervised Learning: Clas... | github_jupyter |
# Python是什么？
### Python是一种高级的多用途编程语言，广泛用于各种非技术和技术领域。Python是一种具备动态语义、面向对象的解释型高级编程语言。它的高级内建数据结构和动态类型及动态绑定相结合，使其在快速应用开发上极具吸引力，也适合于作为脚本或者“粘合剂”语言，将现有组件连接起来。Python简单、易学的语法强调可读性，因此可以降低程序维护成本。Python支持模块和软件包，鼓励模块化的代码重用。
```
print('hellow world')
```
## Python简史
### 1989，为了度过圣诞假期，Guido开始编写Python语言编译器。Python这个名字来自Guido的喜爱的电视连... | github_jupyter |
<a href="https://colab.research.google.com/github/huan/concise-chit-chat/blob/master/Concise_Chit_Chat.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Concise Chit Chat
GitHub Repository: <https://github.com/huan/concise-chit-chat>
## Code TODO:... | github_jupyter |
# Scraping de Características Generales
Se obtienen características generales del equipo de la página whoscored.
Las estadísticas son de la temporada más reciente.
```
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import Web... | github_jupyter |
```
from IPython.core.debugger import set_trace
import numpy as np
import import_ipynb
from environment import *
def heuristicLWR(num_jobs, num_mc, machines, durations):
machines_ = np.array(machines)
tmp = np.zeros((num_jobs,num_mc+1), dtype=int)
tmp[:,:-1] = machines_
machines_ = tmp
... | github_jupyter |
<img src="../../../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
# _*Quantum K-Means algorithm*_
The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.... | github_jupyter |
```
from tqdm.notebook import tqdm
import math
import gym
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from collections import deque
from active_rl.networks.dqn_atari import ENS_DQN
from active_rl.utils.memory import LabelledReplayMemory
from active_rl.utils.optimization ... | github_jupyter |
# Seminar for Lecture 13 "VAE Vocoder"
In the lectures, we studied various approaches to creating vocoders. The problem of sound generation is solved by deep generative models. We've discussed autoregressive models that can be reduced to **MAF**. We've considered the reverse analogue of MAF – **IAF**. We've seen how ... | github_jupyter |
# Trigger Examples
Triggers allow the user to specify a set of actions that are triggered by the result of a boolean expression.
They provide flexibility to adapt what analysis and visualization actions are taken in situ. Triggers leverage Ascent's Query and Expression infrastructure. See Ascent's [Triggers](https://as... | github_jupyter |
## These notebooks can be found at https://github.com/jaspajjr/pydata-visualisation if you want to follow along
https://matplotlib.org/users/intro.html
Matplotlib is a library for making 2D plots of arrays in Python.
* Has it's origins in emulating MATLAB, it can also be used in a Pythonic, object oriented way.
... | github_jupyter |
# Procedures and Functions Tutorial
MLDB is the Machine Learning Database, and all machine learning operations are done via Procedures and Functions. Training a model happens via Procedures, and applying a model happens via Functions.
The notebook cells below use `pymldb`'s `Connection` class to make [REST API](../..... | github_jupyter |
# Assignment 2 - Elementary Probability and Information Theory
# Boise State University NLP - Dr. Kennington
### Instructions and Hints:
* This notebook loads some data into a `pandas` dataframe, then does a small amount of preprocessing. Make sure your data can load by stepping through all of the cells up until que... | github_jupyter |
_Lambda School Data Science - Model Validation_
# Feature Selection
Objectives:
* Feature importance
* Feature selection
## Yesterday we saw that...
## Less isn't always more (but sometimes it is)
## More isn't always better (but sometimes it is)
```
csv_files = ["ABA_14361_100ug_v5.0_DATA.csv",
"ConA_13799-10ug_V5.0_DATA.csv",
'PNA_14030_10ug_v5.0_DATA.csv',
"RCAI_10ug_14110_v5.0_DATA.csv",
"PHA-E-10ug_13853_V5.0... | github_jupyter |
```
from __future__ import division
from __future__ import print_function
import sys
import math
import pickle
import copy
import numpy as np
import cv2
import matplotlib.pyplot as plt
from DataLoader import Batch
from Model import Model, DecoderType
from SamplePreprocessor import preprocess
# constants like filepaths
... | github_jupyter |
# Descripción del TP
## Introducción
En este práctico se pide desarrollar un programa que calcule parámetros de interés para un circuito RLC serie.
Tomando como datos de entrada:
* εmax
* la frecuencia de la fuente en Hz
* los valores de R, L y C:
El programa debe determinar:
* Imax
* la diferencia de potencial... | github_jupyter |
```
import json
import random
import numpy as np
import tensorflow as tf
from collections import deque
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.layers import Dense, Flatten
from keras.layers.convolutional import Conv2D
from keras import backend as K
import datetime
import iter... | github_jupyter |
<a href="https://colab.research.google.com/github/dauparas/tensorflow_examples/blob/master/VAE_cell_cycle.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
https://github.com/PMBio/scLVM/blob/master/tutorials/tcell_demo.ipynb
Variational Autoencoder ... | github_jupyter |
# Predicting Boston Housing Prices
## Using XGBoost in SageMaker (Hyperparameter Tuning)
_Deep Learning Nanodegree Program | Deployment_
---
As an introduction to using SageMaker's Low Level API for hyperparameter tuning, we will look again at the [Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/bost... | github_jupyter |
```
import DSGRN
import cProfile
import sys
sys.setrecursionlimit(10**9)
sys.path.insert(0,'/home/elizabeth/Desktop/GIT/dsgrn_acdc/src')
import PhenotypeGraphviz
import PhenotypeGraphFun
import CondensationGraph_iter
database = Database("/home/elizabeth/Desktop/ACDC/ACDC_Fullconn.db")
network = Network("/home/elizabe... | github_jupyter |
```
from FC_RNN_Evaluater.FC_RNN_Evaluater import *
from FC_RNN_Evaluater.Stateful_FC_RNN_Configuration import *
from FC_RNN_Evaluater.runFC_RNN_Experiment import *
from keras import Model
from keras.layers import TimeDistributed, LSTM, Dense, Dropout, Flatten, Input
def getFinalModel(timesteps = timesteps, lstm_nodes ... | github_jupyter |
```
import csv
from io import StringIO
with open('/Users/yetongxue/Downloads/网站管理.csv')
reader = csv.reader(f, delimiter=',')
for index,row in enumerate(reader):
if index == 0:
continue
print(row)
import tldextract
result = tldextract.extract('http://*.sometime')
result
from queue import PriorityQueue
i... | github_jupyter |
### Cell Painting morphological (CP) and L1000 gene expression (GE) profiles for the following datasets:
- **CDRP**-BBBC047-Bray-CP-GE (Cell line: U2OS) :
* $\bf{CP}$ There are 30,430 unique compounds for CP dataset, median number of replicates --> 4
* $\bf{GE}$ There are 21,782 unique compounds for GE data... | github_jupyter |
# Introduction
Visualization of statistics that support the claims of Black Lives Matter movement, data from 2015 and 2016.
Data source: https://www.theguardian.com/us-news/ng-interactive/2015/jun/01/about-the-counted
Idea from BuzzFeed article: https://www.buzzfeednews.com/article/peteraldhous/race-and-police-shooti... | github_jupyter |
```
import json
import requests
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from scipy.stats import norm
df = pd.read_csv('user_info.csv')
from selenium import webdriver
options = webdriver.ChromeOptions()
optio... | github_jupyter |
# 1. Write a Python Program to Find LCM?
```
# Given some numbers, LCM of these numbers is the smallest positive integer that is divisible by all the numbers numbers
def LCM(ls):
lar = max(ls)
flag=1
while(flag==1):
for i in ls:
if(lar%i!=0):
flag=1
... | github_jupyter |
# Data Wrangling, Analysis and Visualization of @WeLoveDogs twitter data.
```
import pandas as pd
import numpy as np
import tweepy as ty
import requests
import json
import io
import time
```
## Gathering
```
df = pd.read_csv('twitter-archive-enhanced.csv')
df.head()
image_response = requests.get(r'https://d17h27t6h... | github_jupyter |
```
import keras
import keras.backend as K
from keras.datasets import mnist
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, Bat... | github_jupyter |
<a href="https://colab.research.google.com/github/iVibudh/TensorFlow-for-DeepLearning/blob/main/08-Time-Series-Forecasting/moving_average.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Moving average
<table class="tfo-notebook-buttons" align="le... | github_jupyter |
# Approximate q-learning
In this notebook you will teach a __tensorflow__ neural network to do Q-learning.
__Frameworks__ - we'll accept this homework in any deep learning framework. This particular notebook was designed for tensorflow, but you will find it easy to adapt it to almost any python-based deep learning fr... | github_jupyter |
# NOAA Wave Watch 3 and NDBC Buoy Data Comparison
*Note: this notebook requires python3.*
This notebook demostrates how to compare [WaveWatch III Global Ocean Wave Model](http://data.planetos.com/datasets/noaa_ww3_global_1.25x1d:noaa-wave-watch-iii-nww3-ocean-wave-model?utm_source=github&utm_medium=notebook&utm_campa... | github_jupyter |
# Aula 1
```
import pandas as pd
url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'
dados = pd.read_csv(url_dados, compression = 'zip')
dados
dados.head()
dados.shape
dados['tratamento']
dados['tratamento'].unique()
dados['tempo'].unique()
dados['dose'].unique()... | github_jupyter |
# Part 2 - Lists
We learned about **variables** in Part 1. Sometimes it makes sense to group lots of items of information together in a **list**. This is a good idea when the items are all connected in some way.
For example, we might want to store the names of our friends. We could create several variables and assign... | github_jupyter |
<a href="https://colab.research.google.com/github/osipov/edu/blob/master/pyt0/Demo_Data_Visualization.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg"/></a>
# Data Visualization
```
%matplotlib inline
import torch as pt
import matplotlib.pyplot as plt
x = pt.linspace(0, 10, ... | github_jupyter |
# Classification
## MNIST
```
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
X, y = mnist['data'], mnist['target']
X.shape, y.shape
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
some_digit = X[0]
some_digit_img = some_digit.reshape(2... | github_jupyter |
<center>
<img src="https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# **Hands-on Lab : Web Scraping**
Estimated time needed: **30 to 45** minutes
## Objectives
In this lab you will perform the fo... | github_jupyter |
Datashader provides a flexible series of processing stages that map from raw data into viewable images. As shown in the [Introduction](1-Introduction.ipynb), using datashader can be as simple as calling ``datashade()``, but understanding each of these stages will help you get the most out of the library.
The stages ... | github_jupyter |
Lambda School Data Science, Unit 2: Predictive Modeling
# Applied Modeling, Module 1
You will use your portfolio project dataset for all assignments this sprint.
## Assignment
Complete these tasks for your project, and document your decisions.
- [ ] Choose your target. Which column in your tabular dataset will you... | github_jupyter |
___
<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
___
# Matplotlib Exercises
Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first ... | github_jupyter |
```
# Turn on Auto-Complete
%config IPCompleter.greedy=True
# Start logging process at root level
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logging.root.setLevel(level=logging.INFO)
# Load model and dictionary
#model_id_current = 99999
#model_path_current... | github_jupyter |
```
import pandas as pd
import psycopg2
import sqlalchemy
import config
import json
import numpy as np
import scrape
scrape.scrape_bls()
from sqlalchemy import create_engine
from config import password
Engine = create_engine(f"postgresql://postgres:{password}@localhost:5432/Employee_Turnover")
Connection = Engine.conne... | 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 |
```
import json
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
from scipy.special import comb
from tabulate import tabulate
%matplotlib inline
```
## Expected numbers on Table 3.
```
rows = []
datasets = {
'Binary': 2,
'AG news': 4,
'CIFAR10': 10,
'CIFAR100': 100... | github_jupyter |
# PageRank Performance Benchmarking
# Skip notebook test
This notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy version
Notebook Cred... | github_jupyter |
```
pip install contractions
import pandas as pd
import boto3, sys,sagemaker
import pandas as pd
import pandas as pd
import numpy as np
import nltk
import string
import contractions
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.corpus import st... | github_jupyter |
# Langmuir-enhanced entrainment
This notebook reproduces Fig. 15 of [Li et al., 2019](https://doi.org/10.1029/2019MS001810).
```
import sys
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib... | github_jupyter |
##Tirmzi Analysis
n=1000 m+=1000 nm-=120 istep= 4 min=150 max=700
```
import sys
sys.path
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import signal
ls
import capsol.newanalyzecapsol as ac
ac.get_gridparameters
import glob
folders = glob.glob("FortranOutputTest/*/")
folders
all_data= dict() ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
r = np.random.randn((1000))
S0 = 1
S = np.cumsum(r) + S0
T = 2
mu = 0.
sigma = 0.01
S0 = 20
dt = 0.01
N = round(T/dt)
t = np.linspace(0, T, N)
W = np.random.standard_normal(size = N)
W = ... | github_jupyter |
# Plotting with Matplotlib
IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling.
## matplotlib mode
To make plots using Matplotlib, you must first enable IPython's matplotlib mode.
To do this, run the `%matplo... | github_jupyter |
<a href="https://colab.research.google.com/github/mjvakili/MLcourse/blob/master/day2/nn_qso_finder.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Let's start by importing the libraries that we need for this exercise.
```
import numpy as np
import ... | github_jupyter |
# Udacity PyTorch Scholarship Final Lab Challenge Guide
**A hands-on guide to get 90% + accuracy and complete the challenge**
**By [Soumya Ranjan Behera](https://www.linkedin.com/in/soumya044)**
## This Tutorial will be divided into Two Parts,
### [1. Model Building and Training](https://www.kaggle.com/soumya044/... | github_jupyter |
```
import numpy as np
from scipy import pi
import matplotlib.pyplot as plt
import pickle as cPickle
#Sine wave
N = 128
def get_sine_wave():
x_sin = np.array([0.0 for i in range(N)])
# print(x_sin)
for i in range(N):
# print("h")
x_sin[i] = np.sin(2.0*pi*i/16.0)
plt.plot(x_sin)
pl... | github_jupyter |
# Exercise: Find correspondences between old and modern english
The purpose of this execise is to use two vecsigrafos, one built on UMBC and Wordnet and another one produced by directly running Swivel against a corpus of Shakespeare's complete works, to try to find corelations between old and modern English, e.g. "tho... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_04_3_regression.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 4: Training for Tab... | github_jupyter |
```
# flake8: noqa
##########################################################
# Relative Imports
##########################################################
import sys
from os.path import isfile
from os.path import join
def find_pkg(name: str, depth: int):
if depth <= 0:
ret = None
else:
d = ["... | github_jupyter |
# About this Notebook
In this notebook, we provide the tensor factorization implementation using an iterative Alternating Least Square (ALS), which is a good starting point for understanding tensor factorization.
```
import numpy as np
from numpy.linalg import inv as inv
```
# Part 1: Matrix Computation Concepts
##... | github_jupyter |
Let's look at:
Number of labels per image (histogram)
Quality score per image for images with multiple labels (sigmoid?)
```
import csv
from itertools import islice
from collections import defaultdict
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torchvision
import numpy as np
CSV_PATH = 'wga... | github_jupyter |
# Naive Bayes Classifier
Predicting positivty/negativity of movie reviews using Naive Bayes algorithm
## 1. Import Dataset
Labels:
* 0 : Negative review
* 1 : Positive review
```
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
reviews = pd.read_csv('ratings_train.txt', delimiter='\t')
reviews... | github_jupyter |
# Auditing a dataframe
In this notebook, we shall demonstrate how to use `privacypanda` to _audit_ the privacy of your data. `privacypanda` provides a simple function which prints the names of any columns which break privacy. Currently, these are:
- Addresses
- E.g. "10 Downing Street"; "221b Baker St"; "EC2R 8AH"
... | github_jupyter |
```
import pandas as pd
import numpy as np
```
## read datafiles
- C-18 for language population
- C-13 for particular age-range population from a state
```
c18=pd.read_excel('datasets/C-18.xlsx',skiprows=6,header=None,engine='openpyxl')
c13=pd.read_excel('datasets/C-13.xls',skiprows=7,header=None)
```
### particular... | github_jupyter |
```
# Neo4J graph example
# author: Gressling, T
# license: MIT License # code: github.com/gressling/examples
# activity: single example # index: 25-2
# https://gist.github.com/korakot/328aaac51d78e589b4a176228e4bb06f
# download 3.5.8 or neo4j-enterprise-4.0.0-alpha09mr02-unix
!curl https://neo4j.com/artifact.php?name=... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | github_jupyter |
# Communication in Crisis
## Acquire
Data: [Los Angeles Parking Citations](https://www.kaggle.com/cityofLA/los-angeles-parking-citations)<br>
Load the dataset and filter for:
- Citations issued from 2017-01-01 to 2021-04-12.
- Street Sweeping violations - `Violation Description` == __"NO PARK/STREET CLEAN"__
Let's ac... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
<h1><center>Clustering Chicago Public Libraries by Top 10 Nearby Venues</center></h1>
<h4><center>Author: Kunyu He</center></h4>
<h5><center>University of Chicago CAPP'20<h5><center>
### Executive Summary
In this notebook, I clustered 80 public libraries in the city of Chicago into 7 clusters, based on the categorie... | github_jupyter |
```
from keras.models import load_model
import pandas as pd
import keras.backend as K
from keras.callbacks import LearningRateScheduler
from keras.callbacks import Callback
import math
import numpy as np
def coeff_r2(y_true, y_pred):
from keras import backend as K
SS_res = K.sum(K.square( y_true-y_pred ))
... | github_jupyter |
```
import pandas as pd
from sklearn.decomposition import IncrementalPCA, PCA
from lung_cancer.connection_settings import get_connection_string, TABLE_LABELS, TABLE_FEATURES, TABLE_PCA_FEATURES, IMAGES_FOLDER
from lung_cancer.connection_settings import TABLE_PATIENTS, TABLE_TRAIN_ID, MICROSOFTML_MODEL_NAME, TABLE_PRED... | github_jupyter |
```
import numpy as np
import pandas as pd
import sklearn
import spacy
import re
from nltk.corpus import gutenberg
import nltk
import warnings
warnings.filterwarnings("ignore")
nltk.download('gutenberg')
!python -m spacy download en
```
## 1. Converting words or sentences into numeric vectors is fundamental when work... | github_jupyter |
# Training Collaborative Experts on MSR-VTT
This notebook shows how to download code that trains a Collaborative Experts model with GPT-1 + NetVLAD on the MSR-VTT Dataset.
## Setup
* Download Code and Dependencies
* Import Modules
* Download Language Model Weights
* Download Datasets
* Generate Encodings fo... | github_jupyter |
# k-Nearest Neighbor (kNN) exercise
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*
... | github_jupyter |
```
# default_exp callback.core
#export
from fastai2.data.all import *
from fastai2.optimizer import *
from nbdev.showdoc import *
#export
_all_ = ['CancelFitException', 'CancelEpochException', 'CancelTrainException', 'CancelValidException', 'CancelBatchException']
```
# Callback
> Basic callbacks for Learner
## Cal... | github_jupyter |
```
# Update sklearn to prevent version mismatches
#!pip install sklearn --upgrade
# install joblib. This will be used to save your model.
# Restart your kernel after installing
#!pip install joblib
import pandas as pd
```
# Read the CSV and Perform Basic Data Cleaning
```
df = pd.read_csv("resources/exoplanet_data... | github_jupyter |
# Negative Binomial Regression (Students absence example)
## Negative binomial distribution review
I always experience some kind of confusion when looking at the negative binomial distribution after a while of not working with it. There are so many different definitions that I usually need to read everything more tha... | 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 |
# In-Class Coding Lab: Data Analysis with Pandas
In this lab, we will perform a data analysis on the **RMS Titanic** passenger list. The RMS Titanic is one of the most famous ocean liners in history. On April 15, 1912 it sank after colliding with an iceberg in the North Atlantic Ocean. To learn more, read here: https:... | github_jupyter |
```
import numpy as np
from scipy.spatial import Delaunay
import networkx as nx
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pandas
import os
import graphsonchip.graphmaker
from graphsonchip.graphmaker import make_spheroids
from graphsonchip.graphmaker import graph_generation_func
fro... | github_jupyter |
# Model Explainer Example
In this example we will:
* [Describe the project structure](#Project-Structure)
* [Train some models](#Train-Models)
* [Create Tempo artifacts](#Create-Tempo-Artifacts)
* [Run unit tests](#Unit-Tests)
* [Save python environment for our classifier]... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
# Neural Machine Translation with Attention
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/e... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# This code creates a virtual display to draw game images on.
# If you are running locally, just ignore it
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY"))==0:
!bash ../xvfb start
%env DISPLAY=:1... | github_jupyter |
<table>
<tr align=left><td><img align=left src="https://i.creativecommons.org/l/by/4.0/88x31.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
```
%matplotlib inline
import numpy
import matplotl... | github_jupyter |
# Many to Many Classification
Simple example for Many to Many Classification (Simple pos tagger) by Recurrent Neural Networks
- Creating the **data pipeline** with `tf.data`
- Preprocessing word sequences (variable input sequence length) using `padding technique` by `user function (pad_seq)`
- Using `tf.nn.embedding_l... | github_jupyter |
# Android的人脸识别库（NDK）
## 创建工程向导
## dlib库源代码添加到工程
* 把dlib目录下的dlib文件夹拷贝到app/src/main/
## 增加JNI接口
### 创建Java接口类
在app/src/main/java/com/wangjunjian/facerecognition下创建类Fa... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from sklearn import svm
import pandas as pd
import os
import scipy as sc
# get the annotated data to build the classifier
direc = r'C:\Users\Daniellab\Desktop\Light_level_videos_second_batch\Data\Step3\Annotation'
fi... | github_jupyter |
#$EXERCISE_PREAMBLE$
As always, run the setup code below before working on the questions (and if you leave this notebook and come back later, remember to run the setup code again).
```
from learntools.core import binder; binder.bind(globals())
from learntools.python.ex5 import *
print('Setup complete.')
```
# Exerci... | 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 |
<a href="https://colab.research.google.com/github/rlberry-py/rlberry/blob/main/notebooks/introduction_to_rlberry.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction to
del sys
%reload_ext autoreload
%autoreload 2
from toolbox.parsers import standard_parser, add_task_arguments, add_model_arguments
from toolbox.utils import load_task, get_pretrained_model, to_class_name
import modeling.models as models
```
### Notebook funct... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv("Data.csv")
df.head(5)
df['sentiment'] = np.where(df['sentiment'] == "positive", 1, 0)
df.head()
df['sentiment'].value_counts().s... | github_jupyter |
```
print('Hello World')
import numpy
print(numpy.pi)
import numpy as np
print(np.pi)
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
%pinfo print
%matplotlib inline
plt.style.use('../../solving_pde_mooc/notebooks/styles/mainstyle.use')
#create an empty python list that will create values of delta... | github_jupyter |
# Implementing TF-IDF
------------------------------------
Here we implement TF-IDF, (Text Frequency - Inverse Document Frequency) for the spam-ham text data.
We will use a hybrid approach of encoding the texts with sci-kit learn's TFIDF vectorizer. Then we will use the regular TensorFlow logistic algorithm outline.... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Texte-d'oral-de-modélisation---Agrégation-Option-Informatique" data-toc-modified-id="Texte-d'oral-de-modélisation---Agrégation-Option-Informatique-1"><span class="toc-item-num">1 </span>Texte d'oral de modélisation - Agrégation Option Informatique<... | github_jupyter |
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