prompt stringlengths 501 4.98M | target stringclasses 1
value | chunk_prompt bool 1
class | kind stringclasses 2
values | prob float64 0.2 0.97 ⌀ | path stringlengths 10 394 ⌀ | quality_prob float64 0.4 0.99 ⌀ | learning_prob float64 0.15 1 ⌀ | filename stringlengths 4 221 ⌀ |
|---|---|---|---|---|---|---|---|---|
```
#export
from fastai.basics import *
from fastai.tabular.core import *
from fastai.tabular.model import *
from fastai.tabular.data import *
#hide
from nbdev.showdoc import *
#default_exp tabular.learner
```
# Tabular learner
> The function to immediately get a `Learner` ready to train for tabular data
The main fu... | true | code | 0.704262 | null | null | null | null | |
# Aerospike Connect for Spark - SparkML Prediction Model Tutorial
## Tested with Java 8, Spark 3.0.0, Python 3.7, and Aerospike Spark Connector 3.0.0
## Summary
Build a linear regression model to predict birth weight using Aerospike Database and Spark.
Here are the features used:
- gestation weeks
- mother’s age
- fat... | true | code | 0.475301 | null | null | null | null | |
# Classification on Iris dataset with sklearn and DJL
In this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
## Background
### Iris Dataset
The dataset c... | true | code | 0.782642 | null | null | null | null | |
<a href="https://colab.research.google.com/github/satyajitghana/TSAI-DeepNLP-END2.0/blob/main/09_NLP_Evaluation/ClassificationEvaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip3 install git+https://github.com/extensive-nlp/ttc_nlp ... | true | code | 0.862265 | null | null | null | null | |
## Accessing TerraClimate data with the Planetary Computer STAC API
[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at g... | true | code | 0.609059 | null | null | null | null | |
```
import numpy as np
import matplotlib.pyplot as plt
import numba
from tqdm import tqdm
import eitest
```
# Data generators
```
@numba.njit
def event_series_bernoulli(series_length, event_count):
'''Generate an iid Bernoulli distributed event series.
series_length: length of the event series
event_cou... | true | code | 0.687079 | null | null | null | null | |
# Chapter 4
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`
______
## The greatest theorem never told
This chapter focuses on an idea that is always bouncing around our minds, but is rarely ma... | true | code | 0.669259 | null | null | null | null | |
<a href="https://colab.research.google.com/github/s-mostafa-a/pytorch_learning/blob/master/simple_generative_adversarial_net/MNIST_GANs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import torch
from torchvision.transforms import ToTensor, Nor... | true | code | 0.824197 | null | null | null | null | |
# Tutorial 2. Solving a 1D diffusion equation
```
# Document Author: Dr. Vishal Sharma
# Author email: sharma_vishal14@hotmail.com
# License: MIT
# This tutorial is applicable for NAnPack version 1.0.0-alpha4
```
### I. Background
The objective of this tutorial is to present the step-by-step solution of a 1D diffus... | true | code | 0.849379 | null | null | null | null | |
# Monte Carlo Integration with Python
## Dr. Tirthajyoti Sarkar ([LinkedIn](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/), [Github](https://github.com/tirthajyoti)), Fremont, CA, July 2020
---
### Disclaimer
The inspiration for this demo/notebook stemmed from [Georgia Tech's Online Masters in Analytics (... | true | code | 0.547101 | null | null | null | null | |
This illustrates the datasets.make_multilabel_classification dataset generator. Each sample consists of counts of two features (up to 50 in total), which are differently distributed in each of two classes.
Points are labeled as follows, where Y means the class is present:
| 1 | 2 | 3 | Color |
|--- |--- |--- |--... | true | code | 0.612194 | null | null | null | null | |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Kalman Filter Math
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
If you've gotten th... | true | code | 0.608507 | null | null | null | null | |
# Estimation on real data using MSM
```
from consav import runtools
runtools.write_numba_config(disable=0,threads=4)
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Local modules
from Model import RetirementClass
import figs
import SimulatedMinimumDistance as SMD
# Global modules
import numpy as np
import p... | true | code | 0.489076 | null | null | null | null | |
<a href="https://colab.research.google.com/github/clemencia/ML4PPGF_UERJ/blob/master/Exemplos_DR/Exercicios_DimensionalReduction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Mais Exercícios de Redução de Dimensionalidade
Baseado no livro "Pytho... | true | code | 0.712876 | null | null | null | null | |
# Working with Pytrees
[](https://colab.research.google.com/github/google/jax/blob/main/docs/jax-101/05.1-pytrees.ipynb)
*Author: Vladimir Mikulik*
Often, we want to operate on objects that look like dicts of arrays, or lists of lists of dicts... | true | code | 0.714205 | null | null | null | null | |
<a href="https://colab.research.google.com/github/ai-fast-track/timeseries/blob/master/nbs/index.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# `timeseries` package for fastai v2
> **`timeseries`** is a Timeseries Classification and Regression p... | true | code | 0.730929 | null | null | null | null | |
# The Extended Kalman Filter
선형 칼만 필터 (Linear Kalman Filter)에 대한 이론을 바탕으로 비선형 문제에 칼만 필터를 적용해 보겠습니다. 확장칼만필터 (EKF)는 예측단계와 추정단계의 데이터를 비선형으로 가정하고 현재의 추정값에 대해 시스템을 선형화 한뒤 선형 칼만 필터를 사용하는 기법입니다.
비선형 문제에 적용되는 성능이 더 좋은 알고리즘들 (UKF, H_infinity)이 있지만 EKF 는 아직도 널리 사용되서 관련성이 높습니다.
```
%matplotlib inline
# HTML("""
# <style>
# .ou... | true | code | 0.5867 | null | null | null | null | |
# Documenting Classes
It is almost as easy to document a class as it is to document a function. Simply add docstrings to all of the classes functions, and also below the class name itself. For example, here is a simple documented class
```
class Demo:
"""This class demonstrates how to document a class.
... | true | code | 0.588771 | null | null | null | null | |
<img src="https://storage.googleapis.com/arize-assets/arize-logo-white.jpg" width="200"/>
# Arize Tutorial: Surrogate Model Feature Importance
A surrogate model is an interpretable model trained on predicting the predictions of a black box model. The goal is to approximate the predictions of the black box model as cl... | true | code | 0.553686 | null | null | null | null | |
```
import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.pooling import GlobalMaxPooling1D
from keras import backend as K
import json
from collections import OrderedDict
def format_decimal(arr, places=6):
return [round(x * 10**places) / 10**places for x in arr]
DATA = O... | true | code | 0.450601 | null | null | null | null | |
# Saving and Loading Models
In this notebook, I'll show you how to save and load models with PyTorch. This is important because you'll often want to load previously trained models to use in making predictions or to continue training on new data.
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
i... | true | code | 0.743685 | null | null | null | null | |
# Spectral encoding of categorical features
About a year ago I was working on a regression model, which had over a million features. Needless to say, the training was super slow, and the model was overfitting a lot. After investigating this issue, I realized that most of the features were created using 1-hot encoding ... | true | code | 0.601477 | null | null | null | null | |
<a href="https://colab.research.google.com/github/dribnet/clipit/blob/future/demos/CLIP_GradCAM_Visualization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# CLIP GradCAM Colab
This Colab notebook uses [GradCAM](https://arxiv.org/abs/1610.02391) ... | true | code | 0.816168 | null | null | null | null | |
# Chapter 4: Linear models
[Link to outline](https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit#heading=h.9etj7aw4al9w)
Concept map:
#### Notebook setup
```
import numpy as np
import pandas as pd
impo... | true | code | 0.69394 | null | null | null | null | |
# Project 3 Sandbox-Blue-O, NLP using webscraping to create the dataset
## Objective: Determine if posts are in the SpaceX Subreddit or the Blue Origin Subreddit
We'll utilize the RESTful API from pushshift.io to scrape subreddit posts from r/blueorigin and r/spacex and see if we cannot use the Bag-of-words algorithm... | true | code | 0.405449 | null | null | null | null | |
By now basically everyone ([here](http://datacolada.org/2014/06/04/23-ceiling-effects-and-replications/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+DataColada+%28Data+Colada+Feed%29), [here](http://yorl.tumblr.com/post/87428392426/ceiling-effects), [here](http://www.talyarkoni.org/blog/2014/06/01/there-i... | true | code | 0.299419 | null | null | null | null | |
# Pre-training VGG16 for Distillation
```
import torch
import torch.nn as nn
from src.data.dataset import get_dataloader
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(DEVICE)
SEED = 0
BATCH... | true | code | 0.6771 | null | null | null | null | |
# _Mini Program - Working with SQLLite DB using Python_
### <font color=green>Objective -</font>
<font color=blue>1. This program gives an idea how to connect with SQLLite DB using Python and perform data manipulation </font><br>
<font color=blue>2. There are 2 ways in which tables are create below to help you unders... | true | code | 0.227341 | null | null | null | null | |
### Regular Expressions
Regular expressions are `text matching patterns` described with a formal syntax. You'll often hear regular expressions referred to as 'regex' or 'regexp' in conversation. Regular expressions can include a variety of rules, for finding repetition, to text-matching, and much more. As you advance ... | true | code | 0.262534 | null | null | null | null | |
<a href="https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/notebooks/process/masstransferMeOH.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Calculation of mass transfer and hydrate inhibition of a wet gas by inj... | true | code | 0.670446 | null | null | null | null | |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Dimensionality-Reduction" data-toc-modified-id="Dimensionality-Reduction-1"><span class="toc-item-num">1 </span>Dimensionality Reduction</a></span><ul class="toc-item"><li><span><a href="#The-Pro... | true | code | 0.626981 | null | null | null | null | |
## 용어 정의
```
#가설설정
# A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a population.
1. First, we state a hypothesis about a population. Usually the hypothesis concerns the value of a population parameter.
2. Before we select a sample, we use the hypothesis to predict the... | true | code | 0.617686 | null | null | null | null | |
```
import matplotlib.pyplot as plt
import numpy as np
from mvmm.single_view.gaussian_mixture import GaussianMixture
from mvmm.single_view.MMGridSearch import MMGridSearch
from mvmm.single_view.toy_data import sample_1d_gmm
from mvmm.single_view.sim_1d_utils import plot_est_params
from mvmm.viz_utils import plot_scat... | true | code | 0.757068 | null | null | null | null | |
# Pragmatic color describers
```
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2020"
```
## Contents
1. [Overview](#Overview)
1. [Set-up](#Set-up)
1. [The corpus](#The-corpus)
1. [Corpus reader](#Corpus-reader)
1. [ColorsCorpusExample instances](#ColorsCorpusExample-instances)
1. [... | true | code | 0.770907 | null | null | null | null | |
<a href="https://colab.research.google.com/github/thingumajig/colab-experiments/blob/master/RetinaNet_Video_Object_Detection.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# .init
## setup keras-retinanet
```
!git clone https://github.com/fizyr/k... | true | code | 0.540075 | null | null | null | null | |
# Introduction
Linear Regression is one of the most famous and widely used machine learning algorithms out there. It assumes that the target variable can be explained as a linear combination of the input features. What does this mean? It means that the target can be viewed as a weighted sum of each feature. Let’s use ... | true | code | 0.526282 | null | null | null | null | |
# T81-558: Applications of Deep Neural Networks
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information visit the [class website](https://sites.wust... | true | code | 0.469399 | null | null | null | null | |
# Reading and writing fields
There are two main file formats to which a `discretisedfield.Field` object can be saved:
- [VTK](https://vtk.org/) for visualisation using e.g., [ParaView](https://www.paraview.org/) or [Mayavi](https://docs.enthought.com/mayavi/mayavi/)
- OOMMF [Vector Field File Format (OVF)](https://ma... | true | code | 0.319077 | null | null | null | null | |
# Finetuning of the pretrained Japanese BERT model
Finetune the pretrained model to solve multi-class classification problems.
This notebook requires the following objects:
- trained sentencepiece model (model and vocab files)
- pretraiend Japanese BERT model
Dataset is livedoor ニュースコーパス in https://www.rondhuit.com... | true | code | 0.758231 | null | null | null | null | |
### Road Following - Live demo (TensorRT) with collision avoidance
### Added collision avoidance ResNet18 TRT
### threshold between free and blocked is the controller - action: just a pause as long the object is in front or by time
### increase in speed_gain requires some small increase in steer_gain (once a slider is... | true | code | 0.572424 | null | null | null | null | |
**Chapter 10 – Introduction to Artificial Neural Networks**
_This notebook contains all the sample code and solutions to the exercises in chapter 10._
# Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a func... | true | code | 0.686948 | null | null | null | null | |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | true | code | 0.607227 | null | null | null | null | |
# Example of extracting features from dataframes with Datetime indices
Assuming that time-varying measurements are taken at regular intervals can be sufficient for many situations. However, for a large number of tasks it is important to take into account **when** a measurement is made. An example can be healthcare, wh... | true | code | 0.524151 | null | null | null | null | |
# Modeling and Simulation in Python
Case study.
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 ... | true | code | 0.838779 | null | null | null | null | |
# Estimator validation
This notebook contains code to generate Figure 2 of the paper.
This notebook also serves to compare the estimates of the re-implemented scmemo with sceb package from Vasilis.
```
import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
import scipy as sp
import itertools
import... | true | code | 0.509886 | null | null | null | null | |
# Automate loan approvals with Business rules in Apache Spark and Scala
### Automating at scale your business decisions in Apache Spark with IBM ODM 8.9.2
This Scala notebook shows you how to execute locally business rules in DSX and Apache Spark.
You'll learn how to call in Apache Spark a rule-based decision servic... | true | code | 0.387111 | null | null | null | null | |
# Airbnb - Rio de Janeiro
* Download [data](http://insideairbnb.com/get-the-data.html)
* We downloaded `listings.csv` from all monthly dates available
## Questions
1. What was the price and supply behavior before and during the pandemic?
2. Does a title in English or Portuguese impact the price?
3. What features corre... | true | code | 0.505554 | null | null | null | null | |
<a href="https://colab.research.google.com/github/harvardnlp/pytorch-struct/blob/master/notebooks/Unsupervised_CFG.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install -qqq torchtext -qqq pytorch-transformers dgl
!pip install -qqqU git+h... | true | code | 0.662169 | null | null | null | null | |
# Assignment 9: Implement Dynamic Programming
In this exercise, we will begin to explore the concept of dynamic programming and how it related to various object containers with respect to computational complexity.
## Deliverables:
1) Choose and implement a Dynamic Programming algorithm in Python, make sure yo... | true | code | 0.335936 | null | null | null | null | |
```
%matplotlib inline
```
02: Fitting Power Spectrum Models
=================================
Introduction to the module, beginning with the FOOOF object.
```
# Import the FOOOF object
from fooof import FOOOF
# Import utility to download and load example data
from fooof.utils.download import load_fooof_data
# Dow... | true | code | 0.716479 | null | null | null | null | |
## Discretisation
Discretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of the variable's values. Discretisation is also called **binning**, where bin is an alternative name for interval.
### Discretisation helps handl... | true | code | 0.542682 | null | null | null | null | |
## Obligatory imports
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (12,8)
matplotlib.rcParams['font.size']=20
matplotlib.rcParams['lines.linewidth']=4
matplotlib.rcParams['xtick.major.size'] = 10... | true | code | 0.645343 | null | null | null | null | |
```
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils i... | true | code | 0.805861 | null | null | null | null | |
# Bayesian Parametric Regression
Notebook version: 1.5 (Sep 24, 2019)
Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Jesús Cid-Sueiro (jesus.cid@uc3m.es)
Changes: v.1.0 - First version
v.1.1 - ML Model selection included
v.1.2 - Some typos corrected
... | true | code | 0.672681 | null | null | null | null | |
# Goals
### 1. Learn to implement Resnet V2 Block (Type - 1) using monk
- Monk's Keras
- Monk's Pytorch
- Monk's Mxnet
### 2. Use network Monk's debugger to create complex blocks
### 3. Understand how syntactically different it is to implement the same using
- Traditional Keras
- Traditiona... | true | code | 0.814293 | null | null | null | null | |
```
import numpy as np
import matplotlib.pyplot as plt
```
# 1.
## a)
```
def simetrica(A):
"Verifică dacă matricea A este simetrică"
return np.all(A == A.T)
def pozitiv_definita(A):
"Verifică dacă matricea A este pozitiv definită"
for i in range(1, len(A) + 1):
d_minor = np.linalg.det(A[:i... | true | code | 0.400046 | null | null | null | null | |
# Soft Computing
## Vežba 1 - Digitalna slika, computer vision, OpenCV
### OpenCV
Open source biblioteka namenjena oblasti računarske vizije (eng. computer vision). Dokumentacija dostupna <a href="https://opencv.org/">ovde</a>.
### matplotlib
Plotting biblioteka za programski jezik Python i njegov numerički paket ... | true | code | 0.376695 | null | null | null | null | |
## <center>Ensemble models from machine learning: an example of wave runup and coastal dune erosion</center>
### <center>Tomas Beuzen<sup>1</sup>, Evan B. Goldstein<sup>2</sup>, Kristen D. Splinter<sup>1</sup></center>
<center><sup>1</sup>Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sy... | true | code | 0.675015 | null | null | null | null | |
# Building and using data schemas for computer vision
This tutorial illustrates how to use raymon profiling to guard image quality in your production system. The image data is taken from [Kaggle](https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product) and is courtesy of PILOT TECHNOCAST, S... | true | code | 0.396798 | null | null | null | null | |
# Maximum Likelihood Estimation (Generic models)
This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples:
1. Probit model for binary dependent variables
2. Negative binomial model for count data
The `GenericLikelihoodModel` class eases the process by prov... | true | code | 0.686528 | null | null | null | null | |
<a href="https://colab.research.google.com/github/lvisdd/object_detection_tutorial/blob/master/object_detection_face_detector.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# restart (or reset) your virtual machine
#!kill -9 -1
```
# [Tensorfl... | true | code | 0.648466 | null | null | null | null | |
# `Практикум по программированию на языке Python`
<br>
## `Занятие 2: Пользовательские и встроенные функции, итераторы и генераторы`
<br><br>
### `Мурат Апишев (mel-lain@yandex.ru)`
#### `Москва, 2021`
### `Функции range и enumerate`
```
r = range(2, 10, 3)
print(type(r))
for e in r:
print(e, end=' ')
for ind... | true | code | 0.213705 | null | null | null | null | |
<h1>CREAZIONE MODELLO SARIMA REGIONE SARDEGNA
```
import pandas as pd
df = pd.read_csv('../../csv/regioni/sardegna.csv')
df.head()
df['DATA'] = pd.to_datetime(df['DATA'])
df.info()
df=df.set_index('DATA')
df.head()
```
<h3>Creazione serie storica dei decessi totali della regione Sardegna
```
ts = df.TOTALE
ts.head()... | true | code | 0.582491 | null | null | null | null | |
# Logistic Regression on 'HEART DISEASE' Dataset
Elif Cansu YILDIZ
```
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import col, countDistinct
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler, MinMaxScaler, IndexToString
from pysp... | true | code | 0.662114 | null | null | null | null | |
# Recommending Movies: Retrieval
Real-world recommender systems are often composed of two stages:
1. The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this model is to efficiently weed out all candidates that the user is not i... | true | code | 0.727897 | null | null | null | null | |
# Lab 11: MLP -- exercise
# Understanding the training loop
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from random import randint
import utils
```
### Download the data and print the sizes
```
train_data=torch.load('../data/fashion-mnist/train_data.pt')
print... | true | code | 0.874507 | null | null | null | null | |
## Main points
* Solution should be reasonably simple because the contest is only 24 hours long
* Metric is based on the prediction of clicked pictures one week ahead, so clicks are the most important information
* More recent information is more important
* Only pictures that were shown to a user could be clicked, so... | true | code | 0.304223 | null | null | null | null | |
# Azure ML Training Pipeline for COVID-CXR
This notebook defines an Azure machine learning pipeline for a single training run and submits the pipeline as an experiment to be run on an Azure virtual machine.
```
# Import statements
import azureml.core
from azureml.core import Experiment
from azureml.core import Workspa... | true | code | 0.645371 | null | null | null | null | |
# General Equilibrium
This notebook illustrates **how to solve GE equilibrium models**. The example is a simple one-asset model without nominal rigidities.
The notebook shows how to:
1. Solve for the **stationary equilibrium**.
2. Solve for (non-linear) **transition paths** using a relaxtion algorithm.
3. Solve for ... | true | code | 0.574335 | null | null | null | null | |
*This notebook is part of course materials for CS 345: Machine Learning Foundations and Practice at Colorado State University.
Original versions were created by Asa Ben-Hur.
The content is availabe [on GitHub](https://github.com/asabenhur/CS345).*
*The text is released under the [CC BY-SA license](https://creativecom... | true | code | 0.732765 | null | null | null | null | |
# Lecture 3.3: Anomaly Detection
[**Lecture Slides**](https://docs.google.com/presentation/d/1_0Z5Pc5yHA8MyEBE8Fedq44a-DcNPoQM1WhJN93p-TI/edit?usp=sharing)
This lecture, we are going to use gaussian distributions to detect anomalies in our emoji faces dataset
**Learning goals:**
- Introduce an anomaly detection pro... | true | code | 0.59134 | null | null | null | null | |
# Import Necessary Libraries
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.metrics import precision_score, recall_score
# display images
from IPy... | true | code | 0.640383 | null | null | null | null | |
# 第8章: ニューラルネット
第6章で取り組んだニュース記事のカテゴリ分類を題材として，ニューラルネットワークでカテゴリ分類モデルを実装する．なお，この章ではPyTorch, TensorFlow, Chainerなどの機械学習プラットフォームを活用せよ．
## 70. 単語ベクトルの和による特徴量
***
問題50で構築した学習データ，検証データ，評価データを行列・ベクトルに変換したい．例えば，学習データについて，すべての事例$x_i$の特徴ベクトル$\boldsymbol{x}_i$を並べた行列$X$と正解ラベルを並べた行列（ベクトル）$Y$を作成したい．
$$
X = \begin{pmatrix}
\boldsy... | true | code | 0.608943 | null | null | null | null | |
# Analyse a series
<div class="alert alert-block alert-warning">
<b>Under construction</b>
</div>
```
import os
import pandas as pd
from IPython.display import Image as DImage
from IPython.core.display import display, HTML
import series_details
# Plotly helps us make pretty charts
import plotly.offline as py
imp... | true | code | 0.524699 | null | null | null | null | |
# SLU07 - Regression with Linear Regression: Example notebook
# 1 - Writing linear models
In this section you have a few examples on how to implement simple and multiple linear models.
Let's start by implementing the following:
$$y = 1.25 + 5x$$
```
def first_linear_model(x):
"""
Implements y = 1.25 + 5*x
... | true | code | 0.773548 | null | null | null | null | |
# Compare different DEMs for individual glaciers
For most glaciers in the world there are several digital elevation models (DEM) which cover the respective glacier. In OGGM we have currently implemented 10 different open access DEMs to choose from. Some are regional and only available in certain areas (e.g. Greenland ... | true | code | 0.530723 | null | null | null | null | |
# 📃 Solution of Exercise M6.01
The aim of this notebook is to investigate if we can tune the hyperparameters
of a bagging regressor and evaluate the gain obtained.
We will load the California housing dataset and split it into a training and
a testing set.
```
from sklearn.datasets import fetch_california_housing
fr... | true | code | 0.701419 | null | null | null | null | |
## Recommendations with MovieTweetings: Collaborative Filtering
One of the most popular methods for making recommendations is **collaborative filtering**. In collaborative filtering, you are using the collaboration of user-item recommendations to assist in making new recommendations.
There are two main methods of ... | true | code | 0.487124 | null | null | null | null | |
# Figure 4: NIRCam Grism + Filter Sensitivities ($1^{st}$ order)
***
### Table of Contents
1. [Information](#Information)
2. [Imports](#Imports)
3. [Data](#Data)
4. [Generate the First Order Grism + Filter Sensitivity Plot](#Generate-the-First-Order-Grism-+-Filter-Sensitivity-Plot)
5. [Issues](#Issues)
6. [About this... | true | code | 0.409044 | null | null | null | null | |
# Solution based on Multiple Models
```
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
# Tokenize and Numerize - Make it ready
```
training_size = 20000
training_sentences = sent... | true | code | 0.75985 | null | null | null | null | |
# **OPTICS Algorithm**
Ordering Points to Identify the Clustering Structure (OPTICS) is a Clustering Algorithm which locates region of high density that are seperated from one another by regions of low density.
For using this library in Python this comes under Scikit Learn Library.
## Parameters:
**Reachability Dis... | true | code | 0.708112 | null | null | null | null | |
```
#import necessary modules, set up the plotting
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib;matplotlib.rcParams['figure.figsize'] = (8,6)
from matplotlib import pyplot as plt
import GPy
```
# Interacting with models
### November 2014, by Max Zwiessele
#### wi... | true | code | 0.623692 | null | null | null | null | |
```
%matplotlib inline
```
# Partial Dependence Plots
Sigurd Carlsen Feb 2019
Holger Nahrstaedt 2020
.. currentmodule:: skopt
Plot objective now supports optional use of partial dependence as well as
different methods of defining parameter values for dependency plots.
```
print(__doc__)
import sys
from skopt.plot... | true | code | 0.544741 | null | null | null | null | |
# Tutorial 6.3. Advanced Topics on Extreme Value Analysis
### Description: Some advanced topics on Extreme Value Analysis are presented.
#### Students are advised to complete the exercises.
Project: Structural Wind Engineering WS19-20
Chair of Structural Analysis @ TUM - R. Wüchner, M. Péntek
Autho... | true | code | 0.696784 | null | null | null | null | |
```
## Advanced Course in Machine Learning
## Week 4
## Exercise 2 / Probabilistic PCA
import numpy as np
import scipy
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from numpy import linalg as LA
sns.set_style("darkgrid")
def build_dataset(N, D, K, ... | true | code | 0.783357 | null | null | null | null | |
# 3. Markov Models Example Problems
We will now look at a model that examines our state of healthiness vs. being sick. Keep in mind that this is very much like something you could do in real life. If you wanted to model a certain situation or environment, we could take some data that we have gathered, build a maximum l... | true | code | 0.419529 | null | null | null | null | |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Spectral Shaping of the Quantization Noise
The quan... | true | code | 0.72132 | null | null | null | null | |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
# default_exp losses
# default_cls_lvl 3
#export
from fastai.imports import *
from fastai.torch_imports import *
from fastai.torch_core import *
from fastai.layers import *
#hide
from nbdev.showdoc import *
```
# Loss Functions
> C... | true | code | 0.75452 | null | null | null | null | |
# Spark on Kubernetes
Preparing the notebook https://towardsdatascience.com/make-kubeflow-into-your-own-data-science-workspace-cc8162969e29
## Setup service account permissions
https://github.com/kubeflow/kubeflow/issues/4306 issue with launching spark-operator from jupyter notebook
Run command in your shell (not i... | true | code | 0.334943 | null | null | null | null | |
# Cyclical Systems: An Example of the Crank-Nicolson Method
## CH EN 2450 - Numerical Methods
**Prof. Tony Saad (<a>www.tsaad.net</a>) <br/>Department of Chemical Engineering <br/>University of Utah**
<hr/>
```
import numpy as np
from numpy import *
# %matplotlib notebook
# %matplotlib nbagg
%matplotlib inline
%config... | true | code | 0.716938 | null | null | null | null | |
<table class="ee-notebook-buttons" align="left">
<td><a target="_parent" href="https://github.com/giswqs/geemap/tree/master/tutorials/Image/06_convolutions.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_parent" href="https:... | true | code | 0.622115 | null | null | null | null | |
<h1 align="center">Theano</h1>
```
!pip install numpy matplotlib
!pip install --upgrade https://github.com/Theano/Theano/archive/master.zip
!pip install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip
```
### Разминка
```
import theano
import theano.tensor as T
%pylab inline
```
#### будущий пар... | true | code | 0.604282 | null | null | null | null | |
## Change sys.path to use my tensortrade instead of the one in env
```
import sys
sys.path.append("/Users/jasonfiacco/Documents/Yale/Senior/thesis/deeptrader")
print(sys.path)
```
## Read PredictIt Data Instead
```
import ssl
import pandas as pd
ssl._create_default_https_context = ssl._create_unverified_context # O... | true | code | 0.423995 | null | null | null | null | |
[Table of Contents](./table_of_contents.ipynb)
# Smoothing
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
## Introduction
The performance of the Kalman filter is not optimal when you consider future data. For example, suppo... | true | code | 0.672036 | null | null | null | null | |
# 準備
```
# バージョン指定時にコメントアウト
#!pip install torch==1.7.0
#!pip install torchvision==0.8.1
import torch
import torchvision
# バージョンの確認
print(torch.__version__)
print(torchvision.__version__)
# Google ドライブにマウント
from google.colab import drive
drive.mount('/content/gdrive')
%cd '/content/gdrive/MyDrive/Colab Notebooks/gan_... | true | code | 0.785946 | null | null | null | null | |
# One-step error probability
Write a computer program implementing asynchronous deterministic updates for a Hopfield network. Use Hebb's rule with $w_{ii}=0$. Generate and store p=[12,24,48,70,100,120] random patterns with N=120 bits. Each bit is either +1 or -1 with probability $\tfrac{1}{2}$.
For each value of ppp... | true | code | 0.266787 | null | null | null | null | |
# Code Review #1
Purpose: To introduce the group to looking at code analytically
Created By: Hawley Helmbrecht
Creation Date: 10-12-21
# Introduction to Analyzing Code
All snipets within this section are taken from the Hitchhiker's Guide to Python (https://docs.python-guide.org/writing/style/)
### Example 1: Exp... | true | code | 0.769313 | null | null | null | null | |
# SLU13: Bias-Variance trade-off & Model Selection -- Examples
---
<a id='top'></a>
### 1. Model evaluation
* a. [Train-test split](#traintest)
* b. [Train-val-test split](#val)
* c. [Cross validation](#crossval)
### 2. [Learning curves](#learningcurves)
# 1. Model evaluation
```
import matplotlib.pyplot as plt
... | true | code | 0.751489 | null | null | null | null | |
## Rhetorical relations classification used in tree building: ESIM
Prepare data and model-related scripts.
Evaluate models.
Make and evaluate ansembles for ESIM and BiMPM model / ESIM and feature-based model.
Output:
- ``models/relation_predictor_esim/*``
```
%load_ext autoreload
%autoreload 2
import os
import gl... | true | code | 0.703269 | null | null | null | null | |
<a href="https://colab.research.google.com/github/ebagdasa/propaganda_as_a_service/blob/master/Spinning_Language_Models_for_Propaganda_As_A_Service.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Experimenting with spinned models
This is a Colab ... | true | code | 0.474388 | null | null | null | null |
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