text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="https://cocl.us/topNotebooksPython101Coursera">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/TopAd.png" width="750" align="center">
</a>
</div>
<a href="https://cognit... | github_jupyter |
# Modeling and Simulation in Python
Chapter 6
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an as... | github_jupyter |
# Hierarchical Live sellers
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
from datetime import datetime as dt
style.use('ggplot')
#importando dataset e dropando colunas vazias e sem informação útil
dataset = pd.read_csv("Live.csv").drop(columns = {'status_i... | github_jupyter |
### 참고 사이트
glove model : https://jxnjxn.tistory.com/49
mecab : https://github.com/SOMJANG/Mecab-ko-for-Google-Colab
mecab : https://velog.io/@kjyggg/ %ED%98%95%ED%83%9C%EC%86%8C-%EB%B6%84%EC%84%9D%EA%B8%B0-Mecab-%EC%82%AC%EC%9A%A9%ED%95%98%EA%B8%B0-A-to-Z%EC%84%A4%EC%B9%98%EB%B6%80%ED%84%B0-%EB%8B%A8%EC%96%B4-%EC%... | github_jupyter |
# A TRIGA geometry
This notebook can be used as a template for modeling TRIGA reactors.
```
%matplotlib inline
import numpy as np
import openmc
# Materials definitions
# Borated water
water = openmc.Material(name='Borated Water')
water.set_density('g/cm3', 0.740582)
water.add_nuclide('H1', 4.9457e-2)
water.add_nucli... | github_jupyter |
```
import config
'''
Lets print all the variable for the pinouts on board
'''
def get_variable_module_name(module_name):
module = globals().get(module_name, None)
variable = {}
if module:
variable = {key: value for key, value in module.__dict__.iteritems() if not (key.startswith('__') or key.starts... | github_jupyter |
This notebook is an analysis of predictive accuracy in relative free energy calculations from the Schrödinger JACS dataset:
> Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., ... & Romero, D. L. (2015). Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way ... | github_jupyter |
# Inference and Validation
```
import torch
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# Download and load the training data
trainset = datasets... | github_jupyter |
# Assessing Corpus Quality
### In this notebook, we'll learn about assessing corpus quality and potentially correcting problems.
## Potential Problem areas
1. Unexpected characters
1. Improperly joined words
1. Loanwords
### We will consider each of these in turn.
### Datasets: We'll be using the freely available CLTK... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Reducer/stats_by_group.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href=... | github_jupyter |
# Reinforcement Learning
В этом задании постараемся разобраться в проблеме обучения с подкреплением, реализуем алгоритм REINFORCE и научим агента с помощью этого алгоритма играть в игру Cartpole.
Установим и импортируем необходимые библиотеки, а также вспомогательные функции для визуализации игры агента.
```
!pip in... | github_jupyter |
```
# import necessary modules
# uncomment to get plots displayed in notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from classy import Class
from scipy.optimize import fsolve
from scipy.interpolate import interp1d
import math
# esthetic definitions for the plots
font = ... | github_jupyter |
# Agregando funciones no lineales a las capas
> Transformaciones no lineales para mejorar las predicciones de nuestras redes
Algunas de las transformaciones no lineales más comunes en una red neuronal son la funcion ```sigmoide```, ```tanh``` y ```ReLU```
Para agregar estas funciones debemos agregar los siguientes met... | github_jupyter |
# Convolutional Neural Networks
CNNs are a twist on the neural network concept designed specifically to process data with spatial relationships. In the deep neural networks we've seen so far every node is always connected to every other node in the subsequent layer. While spatial relationships CAN be captured, as we'v... | github_jupyter |
# Lasso and Ridge Regression
**Lasso regression:** It is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean.
<hr>
**Ridge Regression:** It is a way to create a predictive and explonatory model when the number of predictor variables in a s... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Plot parameters
sns.set()
%pylab inline
pylab.rcParams['figure.figsize'] = (4, 4)
plt.rcParams['xtick.major.size'] = 0
plt.rcParams['ytick.major.size'] = 0
# Avoid inaccurate floating values (for inverse matrices in dot product for instance)... | github_jupyter |
## Text Similarity using Word Embeddings
In this notebook we're going to play around with pre build word embeddings and do some fun calcultations:
```
%matplotlib inline
import os
from keras.utils import get_file
import gensim
import subprocess
import numpy as np
import matplotlib.pyplot as plt
from IPython.core.pyl... | github_jupyter |
##### Copyright 2020 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 |
```
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pandas_profiling
%matplotlib inline
!dir
%cd .\decision_tree
!dir
df = pd.read_csv(".\\credit_cards_dataset.csv")
df['EDUCATION'].plot.hist()
#df.describe()
#df['PAY... | github_jupyter |
```
# Copyright 2021 The Earth Engine Community Authors { display-mode: "form" }
#
# 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 req... | github_jupyter |
# NoSQL (Neo4j) (sesión 7)
, columns=['A', 'B', 'C', 'D', 'E'])
df
```
What if we want to rename the columns? There is more than one way to do this, and I'll start with an indirect answer that's not reall... | github_jupyter |
<table width="100%"> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="35%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by Abuzer Yak... | github_jupyter |
## Create a Self-Aggregating Map Layer using GeoAnalytics
This notebook will complete the following:
- Connect to your Enterprise portal
- Search through your big data file shares for your dataset of interest
- Run the GeoAnalytics Tool Copy to Data Store
- Publish the results of the tool as a Map Image Layer
```
# ... | github_jupyter |
```
import os
import numpy as np
from matplotlib import pyplot as plt, colors, lines
```
## Generate plots for Adaptive *k*-NN on Random Subspaces and Tiny ImageNet
This code expects the output from the `Adaptive k-NN Subspaces Tiny ImageNet` notebook, so be sure to run that first.
```
plt.rc('text', usetex=True)
p... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
```
#r "./../../../../../../public/src/L4-application/BoSSSpad/bin/Release/net5.0/BoSSSpad.dll"
using System;
using ilPSP;
using ilPSP.Utils;
using BoSSS.Platform;
using BoSSS.Foundation;
using BoSSS.Foundation.XDG;
using BoSSS.Foundation.Grid;
using BoSSS.Solution;
using BoSSS.Application.XNSE_Solver;
using BoSSS.Appl... | github_jupyter |
<img src="images/dask_horizontal.svg"
width="45%"
alt="Dask logo\">
# Dask Delayed
This notebook covers Dask's `delayed` interface and how it can be used to parallelize existing Python code and custom algorithms. Let's start by looking at a basic, non-parallelized example and then see how `dask.delayed... | github_jupyter |
# 1. Introduction
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from prml.preprocess import PolynomialFeature
from prml.linear import (
LinearRegression,
RidgeRegression,
BayesianRegression
)
np.random.seed(1234)
```
## 1.1. Example: Polynomial Curve Fitting
```
def create_t... | github_jupyter |
```
%matplotlib inline
```
Compute the scattering transform of a speech recording
======================================================
This script loads a speech signal from the free spoken digit dataset (FSDD)
of a man pronouncing the word "zero," computes its scattering transform, and
displays the zeroth-, first-... | github_jupyter |
# ***Good morning***
Aakansh//Asritha//Jayakrishna//Dinesh//Harsha Vardhan
**8. Even Digits Problem**
Supervin has a unique calculator. This calculator only has a display, a plus button, and a minus button. Currently, the integerNis displayed on the calculator display.Pressing the plus button incre... | github_jupyter |
```
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, UpSampling2D
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from joblib import Parallel, delayed
import matplotli... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.callbacks import ModelCheckpoint
# important constants
batch_size = 128
epochs = 20
n_classes = 10
learning_rate = 0.1
width = 28
height = 28
fashion_labels = ["T-shirt/top","Trousers","Pullover","Dr... | github_jupyter |
```
import numpy as np
import pandas as pd
```
<img src="logo.png" alt="Girl in a jacket" width="500" height="600">
## What is object oriented programming ?
there are two concepts
- procedural programming
- code as sequence of objects.
- great for data analysis and scripts.
- Object oriented Programming
... | github_jupyter |
# Prediction models for Project1
This notebook explores the following models:
* MeanModel - Predicts mean value for all future values
* LastDayModel - Predicts the same values like last day (given as futures)
Table of contents:
* Load model and create training and test datasets
* Evaluate Mean model
* E... | github_jupyter |
Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Author: Sebastian Raschka
- GitHub Repository: https://github.com/rasbt/deeplearning-models
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
```
# Gradi... | github_jupyter |
# "A Basic Neural Network: Differentiate Hand-Written Digits"
- badges: true
- author: Akshith Sriram
### Key Objectives:
- Building a neural network that differentiates two hand-written digits 3 and 8.
- Comparing the results of this Neural Network (NN) to that of a Logistic Regression (LR) model.
### Requirements:... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Raw-data-stats" data-toc-modified-id="Raw-data-stats-1"><span class="toc-item-num">1 </span>Raw data stats</a></span></li><li><span><a href="#Read-in-data" data-toc-modified-id="Read-in-data-2"><... | github_jupyter |
# Tutorial
We will solve the following problem using a computer to estimate the expected
probabilities:
```{admonition} Problem
An experiment consists of selecting a token from a bag and spinning a coin. The
bag contains 5 red tokens and 7 blue tokens. A token is selected at random from
the bag, its colour is noted ... | github_jupyter |
# Thinking in tensors, writing in PyTorch
A hands-on course by [Piotr Migdał](https://p.migdal.pl) (2019).
<a href="https://colab.research.google.com/github/stared/thinking-in-tensors-writing-in-pytorch/blob/master/5%20Nonlinear%20regression.ipynb" target="_parent">
<img src="https://colab.research.google.com/ass... | github_jupyter |
<a id='python-by-example'></a>
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
... | github_jupyter |
## Introduction of Fairness Workflow Tutorial
## (Dataset/Model Bias Check and Mitigation by Reweighing)
### Table of contents :
* [1 Introduction](#1.-Introduction)
* [2. Data preparation](#2.-Data-preparation)
* [3. Data fairness](#3.-Data-fairness)
* [Data bias checking](#3.1-Bias-Detection)
* [Data mitigatio... | github_jupyter |
from PIL import Image
from matplotlib import image
from os import listdir
from torchvision import transforms, datasets as ds
from torchvision import models
import torchvision as tv
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim as optim
import... | github_jupyter |
# Improving a model with Grid Search
In this mini-lab, we'll fit a decision tree model to some sample data. This initial model will overfit heavily. Then we'll use Grid Search to find better parameters for this model, to reduce the overfitting.
First, some imports.
```
%matplotlib inline
import pandas as pd
import n... | github_jupyter |
<img src="img/saturn_logo.png" width="300" />
# Introduction to Dask
Before we get into too much complexity, let's talk about the essentials of Dask.
## What is Dask?
Dask is an open-source framework that enables parallelization of Python code. This can be applied to all kinds of Python use cases, not just machine ... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pickle
from skimage.segmentation import slic
import scipy.ndimage
import scipy.spatial
import torch
from torchvision import datasets
import sys
sys.path.append("../")
from chebygin import ChebyGIN
from extract_superpixels import process_image
from graphdata ... | github_jupyter |
# View main analysis
This notebook provides a view into a snapshot of the SpikeForest analysis. A snapshot URL may be obtained from the "Archive" section of the website or it may be created offline using the spikeforest Python package.
Because this notebook is checked into the git repo, it is a good idea to make a wo... | github_jupyter |
```
import cv2
from pathlib import Path
from random import *
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1000)
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_mem... | github_jupyter |
# Stock market fluctuations
The fluctuations of stock prices represent an intriguing example of a complex random walk. Stock prices are influenced by transactions that are carried out over a broad range of time scales, from micro- to milliseconds for high-frequency hedge funds over several hours or days for day-trader... | github_jupyter |
```
#https://kieranrcampbell.github.io/blog/2016/05/15/gibbs-sampling-bayesian-linear-regression.html
%load_ext autoreload
%autoreload 2
import sys,os
import numpy as np
import readline
#from rpy2.rinterface import R_VERSION_BUILD
#print(R_VERSION_BUILD)
#import rpy2.robjects as robjects
#import rpy2.robjects.numpy2r... | github_jupyter |
```
#Replace all ______ with rjust, ljust or center.
thickness = int(input()) #This must be an odd number
c = 'H'
#Top Cone
for i in range(thickness):
print((c*i).rjust(thickness-1)+c+(c*i).ljust(thickness-1))
#Top Pillars
for i in range(thickness+1):
print((c*thickness).center(thickness*2)+(c*thickness).ce... | github_jupyter |
For network data visualization we can use a number of libraries. Here we'll use [networkX](https://networkx.github.io/documentation/networkx-2.4/install.html).
```
! pip3 install networkx
! pip3 install pytest
import networkx as nx
! ls ../facebook_large/
import pandas as pd
target = pd.read_csv('../facebook_large/mus... | github_jupyter |
# 공시지가 K-NN
```
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, classification_report
import sklearn.neighbors as neg
import matplotlib.pyplot as plt
import folium
import json
import sklearn.preprocessing as pp
import warnings
warnings.filterwarnings('ignore')
```
**데이터 전처리**
```
#... | github_jupyter |
# NEST by Example - An Introduction to the Neural Simulation Tool NEST Version 2.12.0
# Introduction
NEST is a simulator for networks of point neurons, that is, neuron
models that collapse the morphology (geometry) of dendrites, axons,
and somata into either a single compartment or a small number of
compartments <cit... | github_jupyter |
To Queue or Not to Queue
=====================
In this notebook we look at the relative performance of a single queue vs multiple queues
using the [Simpy](https://simpy.readthedocs.io/en/latest/) framework as well as exploring various
common load balancing algorithms and their performance in M/G/k systems.
First we e... | github_jupyter |
# Machine Learning and Statistics for Physicists
Material for a [UC Irvine](https://uci.edu/) course offered by the [Department of Physics and Astronomy](https://www.physics.uci.edu/).
Content is maintained on [github](github.com/dkirkby/MachineLearningStatistics) and distributed under a [BSD3 license](https://openso... | github_jupyter |
```
import cv2
import numpy as np
import pandas as pd
import pickle as cPickle
from matplotlib import pyplot as plt
from sklearn.cluster import MiniBatchKMeans
from sklearn.neighbors import KNeighborsClassifier
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
fr... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D2_ModelingPractice/W1D2_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 2, Tutorial 1
# Modeling ... | github_jupyter |
# Custom widgets in a notebook
The notebook explore a couple of ways to interact with the user and modifies the output based on these interactions. This is inspired from the examples from [ipwidgets](http://ipywidgets.readthedocs.io/).
```
from jyquickhelper import add_notebook_menu
add_notebook_menu()
```
## List o... | github_jupyter |
# Working With STAC
[](https://mybinder.org/v2/gh/developmentseed/titiler/master?filepath=docs%2Fexamples%2F%2Fnotebooks%2FWorking_with_STAC_simple.ipynb)
### STAC: SpatioTemporal Asset Catalog
> The SpatioTemporal Asset Catalog (STAC) specification aims to standardize t... | github_jupyter |
```
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='talk', style='ticks', color_codes=True, font_scale=0.8)
import numpy as np
import pandas as pd
import scipy
from tqdm import tqdm
%matplotlib inline
```
This notebook generates some of the precursor files for the fragment decomposition figure... | github_jupyter |
# Basic Usage of the Uncertainty Characteristics Curve (UCC)
Needs `uncertainty_characteristics_curve.py` and `sample_predict.pkl` files to be in the same directory.
```
import numpy as np
import matplotlib.pyplot as plt
from uq360.metrics.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve as ucc... | github_jupyter |
```
import pathlib
import tensorflow as tf
import tensorflow.keras.backend as K
import skimage
import imageio
import numpy as np
import matplotlib.pyplot as plt
# Makes it so any changes in pymedphys is automatically
# propagated into the notebook without needing a kernel reset.
from IPython.lib.deepreload import rel... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss, roc_auc_score, accuracy_score
from xgboost import XGBClassifier
from cinnamon.drift import ModelDriftExplainer, AdversarialDriftExplainer
# pandas config
pd.set_option('display.max_col... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$$
\newcommand{\set}[1]{\left\{#1\right\}}
\newcommand{\abs}[1]{\left\lvert#1\right\rvert}
\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
\newcommand{\inner}[2]{\left\langle#1,#2\right\rangle}
\newcomma... | github_jupyter |
# Load forecasting benchmark
Example created by Wilson Rocha Lacerda Junior
## Note
The following example is **not** intended to say that one library is better than another. The main focus of these examples is to show that SysIdentPy can be a good alternative for people looking to model time series.
We will compare... | github_jupyter |
# Huggingface SageMaker-SDK - BERT Japanese NER example
1. [Introduction](#Introduction)
2. [Development Environment and Permissions](#Development-Environment-and-Permissions)
1. [Installation](#Installation)
2. [Permissions](#Permissions)
3. [Uploading data to sagemaker_session_bucket](#Uploading-data... | github_jupyter |
```
# Select the TensorFlow 2.0 runtime
%tensorflow_version 2.x
# Install Weights and Biases (WnB)
#!pip install wandb
# Primary imports
import tensorflow as tf
import numpy as np
import wandb
# Load the FashionMNIST dataset, scale the pixel values
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.... | github_jupyter |
# QCoDeS Example with Yokogawa GS200 and Keithley 7510 Multimeter
In this example, we will show how to use the Yokogawa GS200 smu and keithley 7510 dmm to perform a sweep measurement. The GS200 smu will source current through a 10 Ohm resistor using the **program** feature, and **trigger** the the 7510 dmm, which will... | github_jupyter |
<div class = 'alert alert-block alert-info'
style = 'background-color:#4c1c84;
color:#eeebf1;
border-width:5px;
... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# !git pull
import malaya_speech
import malaya_speech.train.model.vggvox_v2 as vggvox_v2
import tensorflow as tf
class Model:
def __init__(self):
self.X = tf.placeholder(tf.float32, [None, 257, None, 1])
self.logits = vggvox_v2.Model(self.X, num... | github_jupyter |
# Word Embeddings using CBOW [without python DL libraries]
This project presents how to compute word embeddings and use them for sentiment analysis.
- To implement sentiment analysis, you can go beyond counting the number of positive words and negative words.
- You can find a way to represent each word numerically, ... | github_jupyter |
# MNIST Image Classification with TensorFlow on Cloud AI Platform
This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras).
## Learning Objectives
1. Understand how to build a Dense Neural Network (DNN) for ... | github_jupyter |
```
import boto3
import sagemaker
print(boto3.__version__)
print(sagemaker.__version__)
session = sagemaker.Session()
bucket = session.default_bucket()
print("Default bucket is {}".format(bucket))
### REPLACE INPUT FROM PREVIOUS SAGEMAKER PROCESSING CONFIG OUTPUT #####
prefix="customer_support_classification"
s3_traini... | github_jupyter |
```
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
from networkx.drawing.nx_agraph import graphviz_layout
from abc import ABC, abstractmethod
import numpy as np
def V(num):
return 4*num+1
def S(num):
return 2*num+1
def fG(num):
return 2*num-1
def getType(num):
if (num+1) %3... | github_jupyter |
# 2017 August Duplicate Bug Detection
[**Find more on wiki**](https://wiki.nvidia.com/itappdev/index.php/Duplicate_Detection)
[**Demo Link**](http://qlan-vm-1.client.nvidia.com:8080/)
## Walk through of the Algorithm
<img src="imgsrc/Diagram.png">
## 1. Data Cleaning - SenteceParser Python 3
Available on Perforce... | github_jupyter |
[](https://colab.research.google.com/github/davemlz/eemont/blob/master/docs/tutorials/018-Complete-Preprocessing-One-Method.ipynb)
# Complete Preprocessing (Clouds Masking, Shadows Masking, Scaling and Offsetting) With Just One Method
_Tutorial created... | github_jupyter |
# Problem 1
**Least Recently Used Cache**
We have briefly discussed caching as part of a practice problem while studying hash maps.
The lookup operation (i.e., `get()`) and `put()` / `set()` is supposed to be fast for a cache memory.
While doing the `get()` operation, if the entry is found in the cache, it is known... | github_jupyter |
# Stellargraph example: GraphSAGE on the CORA citation network
Import NetworkX and stellar:
```
import networkx as nx
import pandas as pd
import os
import stellargraph as sg
from stellargraph.mapper import GraphSAGENodeGenerator
from stellargraph.layer import GraphSAGE
from tensorflow.keras import layers, optimizer... | github_jupyter |
```
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
from openmdao.utils.assert_utils import assert_near_equal
import os
if os.path.exists('cases.sql'):
os.remove('cases.sql')
```
# Driver Recording
A CaseRecorder is commonly atta... | github_jupyter |
# Theano, Lasagne
и с чем их едят
# разминка
* напиши на numpy функцию, которая считает сумму квадратов чисел от 0 до N, где N - аргумент
* массив чисел от 0 до N - numpy.arange(N)
```
!pip install Theano
!pip install lasagne
import numpy as np
def sum_squares(N):
return сумма квадратов чисел от 0 до N
%%time
sum... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import my_utils as my_utils
635 - 243
1027 - 635
```
## prepare test data
```
row_test = pd.read_csv('./1962_to_1963.csv')
# row_test = pd.read_excel('./normalized_bs.xlsx')
row_test[row_data.day > 635].head()
test_data = ro... | github_jupyter |
# [LEGALST-190] Lab 3/20: TF-IDF and Classification
This lab will cover the term frequency-inverse document frequency method, and classification algorithms in machine learning.
Estimated Lab time: 30 minutes
```
# Dependencies
from datascience import *
import numpy as np
import pandas as pd
from sklearn.feature_extr... | github_jupyter |
```
import os
from subprocess import Popen, PIPE, STDOUT
es_server = Popen(['/home/dr_lunars/elasticsearch-7.0.0/bin/elasticsearch'],stdout=PIPE, stderr=STDOUT)
!sleep 30
!/home/dr_lunars/elasticsearch-7.0.0/bin/elasticsearch-plugin install analysis-nori
!/home/dr_lunars/elasticsearch-7.0.0/bin/elasticsearch-plugin ins... | github_jupyter |
```
from env_1_nonstochastic_kings import Environment1,StartandGoal
from SophAgent import SophAgentActions
from QlearningAgent import QAgent
import numpy as np
import random as random
[startstate,goalstate]=StartandGoal()
trials=100000
Time_horizon=15
T_min=2
#RandomAgent
#Experimenting success rate of RandomAgent fro... | github_jupyter |
<center><img src='../../img/ai4eo_logos.jpg' alt='Logos AI4EO MOOC' width='80%'></img></center>
<hr>
<br>
<a href='https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring/1/steps/1280514' target='_blank'><< Back to FutureLearn</a><br>
# 3B - Tile-based classification using Sentinel-2 L1C an... | github_jupyter |
# Задание 1.2 - Линейный классификатор (Linear classifier)
В этом задании мы реализуем другую модель машинного обучения - линейный классификатор. Линейный классификатор подбирает для каждого класса веса, на которые нужно умножить значение каждого признака и потом сложить вместе.
Тот класс, у которого эта сумма больше,... | github_jupyter |
```
import os
import pickle
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
matplotlib.style.use('seaborn')
sns.set(style='whitegrid', color_codes=True)
save_path = './figures'
if not os.path.exists(save_path):
os.makedirs(save_path)
save_type = 'png'
def remove_pts(x,y... | github_jupyter |
```
import tensorflow as tf
import torch
import numpy as np
Hc = 3
Wc = 4
coord_cells = tf.stack(tf.meshgrid(tf.range(Hc), tf.range(Wc), indexing='ij'), axis=-1)
a = tf.Session().run(coord_cells)
print(a)
import torch
coor_cells = torch.stack(torch.meshgrid(torch.arange(Hc), torch.arange(Wc)), dim=2) # [Hc, Wc, 2]
b ... | github_jupyter |
# Introduction
Although math is the fundamental basis of physics and astrophysics, we cannot always easily convert numbers and equations into a coherent picture. Plotting is therefore a vital tool in bridging the gap between raw data and a deeper scientific understanding.
*Disclaimer:*
There are many ways to make t... | github_jupyter |
# MNIST distributed training
The **SageMaker Python SDK** helps you deploy your models for training and hosting in optimized, productions ready containers in SageMaker. The SageMaker Python SDK is easy to use, modular, extensible and compatible with TensorFlow and MXNet. This tutorial focuses on how to create a conv... | github_jupyter |
```
import pandas as pd
import numpy as np
import json
import time
import hmac
import hashlib
import random
random.seed(42)
HMAC_KEY = "Insert your HMAC key from Edge Impulse project"
classes = {
"grazing": 0,
"lying": 0,
"running": 0,
"standing": 0,
"walking":0,
"trotting":0
}
ident_col... | github_jupyter |
# Configuring analyzers for the MSMARCO Document dataset
Before we start tuning queries and other index parameters, we wanted to first show a very simple iteration on the standard analyzers. In the MS MARCO Document dataset we have three fields: `url`, `title` and `body`. We tried just couple very small improvements, ... | github_jupyter |
```
import pandas as pd
import numpy as np
import os, sys
sys.path.append(os.environ['HOME'] + '/src/models/')
from deeplearning_models import DLTextClassifier
from feature_based_models import FBConstructivenessClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
%matplotlib ... | github_jupyter |
# Objective
In this notebook we will:
+ load and merge data from different sources (in this case, data source is filesystem.)
+ preprocess data
+ create features
+ visualize feature distributions across the classes
+ write down our observations about the data
```
import pandas as pd
import matplot... | github_jupyter |
```
import numpy as np,scipy.stats as ss,pandas as pd,datetime as dt
from random import gauss
from itertools import product
#----------------------------------------------------------------------------------------
def getRefDates_MonthBusinessDate(dates):
refDates,pDay={},[]
first=dt.date(year=dates[0].year,mo... | github_jupyter |
# eICU Collaborative Research Database
# Notebook 4: Summary statistics
This notebook shows how summary statistics can be computed for a patient cohort using the `tableone` package. Usage instructions for tableone are at: https://pypi.org/project/tableone/
## Load libraries and connect to the database
```
# Import ... | github_jupyter |
# Ler a informação de um catálogo a partir de um arquivo texto e fazer gráficos de alguns parâmetros
## Autores
Adrian Price-Whelan, Kelle Cruz, Stephanie T. Douglas
## Tradução
Ricardo Ogando, Micaele Vitória
## Objetivos do aprendizado
* Ler um arquivo ASCII usando o `astropy.io`
* Converter entre representações d... | github_jupyter |
# PyTorch 및 SMDataParallel을 사용한 분산 데이터 병렬 MNIST 훈련
## 배경
SMDataParallel은 Amazon SageMaker의 새로운 기능으로 딥러닝 모델을 더 빠르고 저렴하게 훈련할 수 있습니다. SMDataParallel은 TensorFlow2, PyTorch, MXNet을 위한 분산 데이터 병렬 훈련 프레임워크입니다.
이 노트북 예제는 MNIST 데이터셋을 사용하여 SageMaker에서 PyTorch와 함께 SMDataParallel을 사용하는 방법을 보여줍니다.
자세한 내용은 아래 자료들을 참조해 주세요.
1. [PyT... | github_jupyter |
Based on: https://medium.com/swlh/transformer-fine-tuning-for-sentiment-analysis-c000da034bb5
```
!pip install pytorch-transformers
!pip install pytorch-ignite
from google.colab import drive
drive.mount('/content/drive')
# !cp drive/'My Drive'/cs231n-project/train_captions_v2.pkl .
# !cp drive/'My Drive'/cs231n-proje... | github_jupyter |
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