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##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
Проект команды **paranormal** в рамках домашнего задания Летней Школы **МТС.Тета**, направление "Машинное обучение"
#### Загрузка и настройка необходимых библиотек
```
import pickle
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from scipy.stats impor... | github_jupyter |
# Testing HLS Module
The HLS module simply copies the input image to the output image (passthrough)
The project builds on the VDMA demo.
## Project sources can be found here
[HLS Passthrough Demo](https://github.com/CospanDesign/pynq-hdl/tree/master/Projects/Simple%20HLS%20VDMA)
```
import cv2
import numpy as np
... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
# Example: CanvasXpress heatmap Chart No. 11
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/heatmap-11.html
This example is generated using the reproducible JSON obtained from the above pag... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
defaulter_df = pd.read_csv("Default.csv")
defaulter_df.head()
print("Size of the data : ", defaulter_df.shape)
print("Target variable frequency distribution : \n", defaulter_df["default"].value_counts())
X = defaulter_df[["bal... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Tutorial-IllinoisGRMHD: InitSymBound.C
## Authors: Leo W... | github_jupyter |
### Here are the simple examples for plotting nomogram, ROC curves, Calibration curves, and Decision curves in training and test dataset by using R language.
```
# Library and data
library(rms)
library(pROC)
library(rmda)
train <-read.csv("E:/Experiments/YinjunDong/nomogram/EGFR-nomogram.csv")
test <-read.csv("E:/Expe... | github_jupyter |
# DREAMER Dominance EMI-GRU 48_16
Adapted from Microsoft's notebooks, available at https://github.com/microsoft/EdgeML authored by Dennis et al.
```
import pandas as pd
import numpy as np
from tabulate import tabulate
import os
import datetime as datetime
import pickle as pkl
import pathlib
from __future__ import pri... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import joblib
import tensorflow as tf
from tensorflow.keras.mod... | github_jupyter |
# Interactive Demo for Metrics
* command line executables: see README.md
* algorithm documentation: [metrics.py API & Algorithm Documentation](metrics.py_API_Documentation.ipynb)
* **make sure you enabled interactive widgets via: **
```
sudo jupyter nbextension enable --py --sys-prefix widgetsnbextension
```
* **make... | github_jupyter |
```
import json
import requests
import spacy
import nltk
from collections import Counter
import sys
sys.path.append("..")
with open('../data/comment_data/headphoneadvice_360.json') as f:
c_ha = json.load(f)
len(c_ha)
with open('../data/comment_data/audiophile_360.json') as f:
c_a = json.load(f)
len(c_a)
with op... | github_jupyter |
```
# default_exp callback.core
#export
from fastai.data.all import *
from fastai.optimizer import *
#hide
from nbdev.showdoc import *
#export
_all_ = ['CancelFitException', 'CancelEpochException', 'CancelTrainException', 'CancelValidException', 'CancelBatchException']
```
# Callback
> Basic callbacks for Learner
##... | github_jupyter |
<a id='ar1'></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">
</a>
</div>
... | github_jupyter |
<font size=4>**Create Plots**</font>
**Plot with Symbolic Plotting Functions**
MATLAB® provides many techniques for plotting numerical data. Graphical capabilities of MATLAB include plotting tools, standard plotting functions, graphic manipulation and data exploration tools, and tools for printing and exporting graph... | github_jupyter |
```
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
%matplotlib inline
```
# type 4 ... | github_jupyter |
<a href="https://colab.research.google.com/github/seopbo/nlp_tutorials/blob/main/single_text_classification_(nsmc)_LoRa.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Single text classification - LoRa
[LoRA: Low-Rank Adaptation of Large Language ... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
### 94. Binary Tree Inorder Traversal
#### Content
<p>Given the <code>root</code> of a binary tree, return <em>the inorder traversal of its nodes' values</em>.</p>
<p> </p>
<p><strong>Example 1:</strong></p>
<img alt="" src="https://assets.leetcode.com/uploads/2020/09/15/inorder_1.jpg" style="width: 202px; h... | github_jupyter |
## Evolving Deep Echo State Networks
This notebook demonstrates using genetic search to find optimal hyperparameters for Deep Echo State Networks implemented using pytorch-esn.
The process will evolve the most fit ESN hyperparameters to solve a given problem, including the size, structure and layers in the ESN.
###... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
from cbrain.imports import *
from cbrain.utils import *
from cbrain.data_generator import DataGenerator, threadsafe_generator
from cbrain.models import *
from cbrain.model_diagnostics import ModelDiagnostics
limit_mem()
PREPROC_DIR = '/scratch/srasp/preprocessed... | github_jupyter |
# Scrape play-by-play data from ESPN
The code is a bit messy, but the idea is pretty simple. Profootballreference.com's play-by-play tables are one of the best resources out there, but they don't say which team has the ball. That's easy to figure out with context, but not for an algorithm that doesn't know what player... | 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 |
```
import numpy as np
import matplotlib.pyplot as plt
import random
%matplotlib inline
xs = np.linspace(-500, 500, 1000)
def f(x):
return -x**2
ys = f(xs)
plt.plot(xs, ys)
def coordinate_ascent_1D(xs, f, T=1000, step=20):
random_start = xs[0]
initial_params = [random_start]
ys = f(xs)
... | github_jupyter |
<a href="https://colab.research.google.com/github/JoshuaShunk/NSDropout/blob/main/mnist_numbers_implementation_of_Dropout.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# MNIST Numbers Implementation of Old Dropout
```
import matplotlib.pyplot as ... | github_jupyter |
## Create examples of network output for figure panels
Created by: Yarden Cohen\
Date: June 2021\
This notebook allows loading specific saved TweetyNet models and examining their outputs.
Cells in this notebook will also hold code to create figure panels showing such network outputs.
```
# imports
from argparse import... | github_jupyter |
# Export metadata to django fixture
```
import os, sys
import pandas as pd
import json
from datetime import datetime as dt
sys.path.append('../src')
import utils
import settings
def create_django_datetimestamp(dt_object=None):
if dt_object==None:
created_time = dt.now()
else:
created_time ... | github_jupyter |
# Custom Interactivity
```
import param
import numpy as np
import holoviews as hv
hv.extension('bokeh', 'matplotlib')
```
In previous notebooks we discovered how the ``DynamicMap`` class allows us to declare objects in a lazy way to enable exploratory analysis of large parameter spaces. In the [Responding to Events](... | github_jupyter |
# Creating config file names for t1s, masks
```
import numpy as np
import glob as gb
import random
```
### Making the list of t1s
```
paths = gb.glob('/home/despoB/cathwang/native/*/*Brain*nii.gz')
t1 = []
t1 += gb.glob("/Users/catherinewang/Desktop/despolab/deepmedic/atlas/native/native_1/c0001/*")
t1 += gb.glob("/... | github_jupyter |
# OpenDartReader - Users Guide
<img width="40%" src="https://i.imgur.com/FMsL0id.png" >
`OpenDartReader`는 금융감독원 전자공시 시스템의 "Open DART"서비스 API를 손쉽게 사용할 수 있도록 돕는 오픈소스 라이브러리 입니다.
#### 2020-2021 [FinanceData.KR](http://financedata.kr) | [facebook.com/financedata](http://facebook.com/financedata)
## OpenDartReader
`Open... | github_jupyter |
# Introduction to Functions
- [Download the lecture notes](https://philchodrow.github.io/PIC16A/content/functions/functions_1.ipynb).
**Functions** are one of the most important constructs in computer programming. A function is a single command which, when executed, performs some operations and may return a value. ... | github_jupyter |
```
import tensorflow as tf
# Import MNIST data (Numpy format)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Parameters
learning_rate = 0.01
num_steps = 1000
batch_size = 128
display_step = 100
# Network Parameters
n_input = 784 # MNIST data... | github_jupyter |
```
import pandas as pd
def load_data():
return pd.read_csv("../datasets/housing/housing.csv")
housingData = load_data()
housingData.head()
housingData.info()
housingData["ocean_proximity"].value_counts()
%matplotlib inline
import matplotlib.pyplot as plt
housingData.hist(bins = 50, figsize=(20,15))
plt.show()
imp... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
years=[1,1000,1500,1600,1700,1750,1800,1850,1900,1950,1955,1960,1965,1970,1980,1985,1990,
1995,2000,2005,2010,2015]
pops=[200,400,458,580,682,791,1000,1262,1650,2525,2758,3018,3322,3682,
4061,4440,4853,5310,5735,6127,6520,7349]
plt.plot(years,pops)
plt... | github_jupyter |
## Assignment:
Beat the performance of my Lasso regression by **using different feature engineering steps ONLY!!**.
The performance of my current model, as shown in this notebook is:
- test rmse: 44798.497576784845
- test r2: 0.7079639526659389
To beat my model you will need a test r2 bigger than 0.71 and a rmse sma... | github_jupyter |
```
# Imágenes: Copyright a autores respectivos.
# Gráficos: Tomados de http://matplotlib.org/gallery.html y modificados.
```
# MAT281
## Aplicaciones de la Matemática en la Ingeniería
## ¿Porqué aprenderemos sobre visualización?
* Porque un resultado no sirve si no puede comunicarse correctamente.
* Porque una bue... | github_jupyter |
# **MODEL C: YOLOv3 + SORT + Early Fused Skeleton + ST-DenseNet**
## A unified framework for pedestrian intention prediction.
1. **YOLOv3** -> Object detector: responsible to identify and detect objects of interest in a given frame or image.
2. **SORT** -> Object Tracker: SORT is responsible tracking the detected obj... | github_jupyter |
```
#挑战性练习：仿照task5，将猜数游戏改成由用户随便选择一个整数，让计算机来猜测的猜数游戏，要求和task5中人猜测的方法类似，
#但是人机角色对换，由人来判断猜测是大、小还是相等，请写出完整的猜数游戏。
import random,math
def win ():
print(
'''
======恭喜你，你赢了=======
."". ."",
| | / /
| | / /
| | / ... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from imblearn.under_sampling import RandomUnderSampler
from sklearn.neighbors import KNeighborsClassifier
#import data in data frame
subbmission = pd.read_csv('./sample_submission_ejm25Dc.csv')
data = pd.read_excel('./Train/train_Data.xlsx')
... | github_jupyter |
# Multiclass Support Vector Machine 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 we... | github_jupyter |
<a href="https://colab.research.google.com/github/jkraybill/gpt-2/blob/finetuning/GPT2-finetuning2-345M.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
To try out GPT-2, do this:
- go to the "Runtime" menu and click "Change runtime type" and make s... | github_jupyter |
# Feature extraction with tsfresh transformer
In this tutorial, we show how you can use sktime with [tsfresh](https://tsfresh.readthedocs.io) to first extract features from time series, so that we can then use any scikit-learn estimator.
## Preliminaries
You have to install tsfresh if you haven't already. To install ... | github_jupyter |
```
# !wget https://cdn.commonvoice.mozilla.org/cv-corpus-5.1-2020-06-22/id.tar.gz
# !tar -zxf id.tar.gz
# !wget https://f000.backblazeb2.com/file/malay-dataset/speech/semisupervised-26-02-2021-part2.tar
# !mkdir part1-v2
# !tar -xf semisupervised-26-02-2021-part2.tar -C part1-v2
# !wget https://f000.backblazeb2.com/fi... | github_jupyter |
## Structure solving as meta-optimization (demo)
This is going to be so cool!
In the work of Senior et al. (2019), Yang et al. (2020), and others, static optimization constraints are predicted then provided to a static, general purpose optimization algorithm (with some amount of manual tuning of optimization paramete... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Crossentropy method
This notebook will teach you to solve reinforcement learning problems with crossentropy method. We'll follow-up by scaling everything up and using neural network policy.
```
# In google collab, uncomment this:
# !wget https://bit.ly/2FMJP5K -O setup.py && bash setup.py
# XVFB will be launched i... | github_jupyter |
# Linear regression estimate quality (bivariate with Gaussian noise)
Up to now, the regression models with [1](LinearRegressionUnivariate.ipynb) or [2](LinearRegressionBivariate.ipynb) features were based on a infinite length dataset. As a consequence, all estimates were (almost) perfect.
In a given "real life" appli... | github_jupyter |
```
import pandas as pd
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten,BatchNormalization,Activation
#from keras.model import sequential
train=pd.read_csv('train.csv')
train.head()
test=pd.read_csv('test.csv')
tes... | github_jupyter |
```
from sklearn.model_selection import cross_val_score, cross_val_predict, GridSearchCV, train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score, classification_report
import pandas as pd
import numpy as np
from time import time
from sklearn.preprocessing import MinMaxScaler
from sklearn.... | github_jupyter |
# Clustering
Wikipedia: Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Clustering is one of the main task of exploratory data min... | github_jupyter |
# Performance Tests of Apache Spark-Based DC2 Run 1.1 Object Catalog Access
Author: **Julien Peloton [@JulienPeloton](https://github.com/JulienPeloton)**
Last Run: **2018-11-22**
See also: [issue/249](https://github.com/LSSTDESC/DC2-production/issues/249)
The purpose of this notebook is twofold: introduce Apache S... | github_jupyter |
# Introduction
This notebook was used in order to create the **"Naive Early-fusion" row in TABLE II**.
Note that a lot of code is copy-pasted across notebooks, so you may find some functionality implemented here that is not used, for instance the network is implemented in a way to support late-fusion, which is not us... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
# The Truck Fleet puzzle
This tutorial includes everything you need to set up decision optimization engines, build constraint programming models.
When you finish this tutorial, you'll have a foundational knowledge of _Prescriptive Analytics_.
>This notebook is part of the **[Prescriptive Analytics for Python](https... | github_jupyter |
# LSTM
* We will implement it with tensorflow library together with LSTM tool for sentiment analysis in tweets.
* Unlike the LSTM (Long short-term memory) method, it is a deep learning method.
* Data preprocessing steps are similar to Naive Bayes vs Logistic Regression methods, but the classification of tweets is dif... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Common Functions for `GiRaFFEfood` Initial Data for `GiRa... | github_jupyter |
## Example 3: Sensitivity analysis for a NetLogo model with SALib and Multiprocessing
This is a short demo similar to example two but using the multiprocessing [Pool](https://docs.python.org/3.6/library/multiprocessing.html#module-multiprocessing.pool)
All files used in the example are available from the pyNetLogo rep... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
# Detecting Spam
*Curtis Miller*
Now, having seen how to load and prepare our e-mail collection, we can start training a classifier.
## Loading And Splitting E-Mails
Our first task is to load in the data. We will split the data into training and test data. The training data will be used to train a classifier while t... | github_jupyter |
TSG077 - Kibana logs
====================
Steps
-----
### Parameters
```
import re
tail_lines = 500
pod = None # All
container = "kibana"
log_files = [ "/var/log/supervisor/log/kibana*.log" ]
expressions_to_analyze = [ ]
```
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes client into '... | github_jupyter |
# Evaluation of SBMV for structured references
Dominika Tkaczyk
5.05.2019
This analysis contains the evaluation of the search-based matching algorithms for structured references.
## Methodology
The test dataset is composed of 2,000 randomly chosen structured references. Three algorithms are compared:
* the legac... | github_jupyter |
```
%%javascript
var kernel = IPython.notebook.kernel;
var body = document.body,
attribs = body.attributes;
var command = "__filename__ = " + "'" + decodeURIComponent(attribs['data-notebook-name'].value) + "'";
kernel.execute(command);
print(__filename__)
import os, sys, numpy as np, tensorflow as tf
from pathlib... | github_jupyter |
# Building a model of oxidative ATP synthesis from energetic components
Simulations in the preceding section illustrate how matrix ATP and ADP concentrations are governed by the contributors to the proton motive force. They also show how the matrix ATP/ADP ratio must typically be less than $1$, in contrast to the cyt... | github_jupyter |
# Tutorial 1 for R
## Solve Dantzig's Transport Problem using the *ix modeling platform* (ixmp)
<img style="float: right; height: 80px;" src="_static/R_logo.png">
### Aim and scope of the tutorial
This tutorial takes you through the steps to import the data for a very simple optimization model
and solve it using th... | github_jupyter |
```
#################### 2020 xilinx summer school ############
import sys
import numpy as np
import os
import time
import math
from PIL import Image,ImageDraw
from matplotlib import pyplot
import matplotlib.pylab as plt
import cv2
from datetime import datetime
from pynq import Xlnk
from pynq import Overlay
from summ... | github_jupyter |
# Доверительные интервалы для двух долей
```
import numpy as np
import pandas as pd
import scipy
from statsmodels.stats.weightstats import *
from statsmodels.stats.proportion import proportion_confint
```
## Загрузка данных
```
data = pd.read_csv('banner_click_stat.txt', header = None, sep = '\t')
data.columns = ['... | github_jupyter |
```
import re
import urllib
import urllib3
import requests
from bs4 import BeautifulSoup
urllib3.disable_warnings()
headers = {'User-Agent':'Mozilla/6.2'}
data_Stor1=[]
http_proxy = "http://76.76.76.154:53281"
https_proxy = "https://35.230.124.232:80"
ftp_proxy = "ftp://35.233.225.185:8080"
proxyDict = {
... | github_jupyter |
<a href="https://colab.research.google.com/github/timrocar/DS-Unit-2-Linear-Models/blob/master/module1-regression-1/LS_DS_211_assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 1, Module 1*
---
#... | github_jupyter |
# AI2S Deep Learning Day - Beginners notebook
<sub>Alessio Ansuini, AREA Research and Technology</sub>
<sub>Andrea Gasparin and Marco Zullich, Artificial Intelligence Student Society</sub>
## Pytorch
PyTorch is a Python library offering extensive support for the construction of deep Neural Networks (NNs).
One of t... | github_jupyter |
# Importing Libraries
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')
import plotly
from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.offline as py
py.init_noteb... | github_jupyter |
<h1>VAST Challenge 2017</h1>
<h2><i>Mini Challenge 1</i></h2>
<br />
<h3>1. Introduction</h3>
<p>At this present work we present our solution for the first challenge proposed at the 2017 VAST Challenge, where contestants, using visual analytics tools, are expected to find patterns in the data of the vehicle traffic... | github_jupyter |
# Foundations of Computational Economics #38
by Fedor Iskhakov, ANU
<img src="_static/img/dag3logo.png" style="width:256px;">
## Dynamic programming with continuous choice
<img src="_static/img/lecture.png" style="width:64px;">
<img src="_static/img/youtube.png" style="width:65px;">
[https://youtu.be/pAEm9cZd92Y]... | github_jupyter |
This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:
<table>
<tr><td>
<img src="https://tensorflow.org/images/fashio... | github_jupyter |
# SageMaker Processing Script: HuggingFace
This notebook shows a very basic example of using SageMaker Processing to create train, test and validation datasets. SageMaker Processing is used to create these datasets, which then are written back to S3.
In a nutshell, we will create a `HuggingFaceProcessor` object, pass... | github_jupyter |
# InSAR Time Series Analysis using MintPy and ARIA products
**Author:** Eric Fielding, David Bekaert, Heresh Fattahi and Zhang Yunjun
This notebook is a second modification by Eric Fielding from an earlier version of the notebook (https://nbviewer.jupyter.org/github/aria-tools/ARIA-tools-docs/blob/master/JupyterDoc... | github_jupyter |
# Recommendations on GCP with TensorFlow and WALS with Cloud Composer
***
This lab is adapted from the original [solution](https://github.com/GoogleCloudPlatform/tensorflow-recommendation-wals) created by [lukmanr](https://github.com/GoogleCloudPlatform/tensorflow-recommendation-wals/commits?author=lukmanr)
This proje... | github_jupyter |
# Assignment 4 - Average Reward Softmax Actor-Critic
Welcome to your Course 3 Programming Assignment 4. In this assignment, you will implement **Average Reward Softmax Actor-Critic** in the Pendulum Swing-Up problem that you have seen earlier in the lecture. Through this assignment you will get hands-on experience in ... | github_jupyter |
# Downloads markdown generator for academicpages
Takes a TSV of downloads with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html))... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# 01. Train in the Notebook & Deploy Model to ACI
* Load workspace
* Train a simple regression model directly in the Notebook python kernel
* Record run history
* Find the best model in run history and download it.
* Deploy the... | github_jupyter |
# Forecasting with sktime
In forecasting, we're interested in using past data to make temporal forward predictions. sktime provides common statistical forecasting algorithms and tools for building composite machine learning models.
For more details, take a look at [our paper on forecasting with sktime](https://arxiv.... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
<a href="https://colab.research.google.com/github/sayakpaul/Handwriting-Recognizer-in-Keras/blob/main/Recognizer_KerasOCR.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## References:
* https://keras-ocr.readthedocs.io/en/latest/examples/fine_tunin... | github_jupyter |
```
import os
os.chdir('/home/yuke/PythonProject/DrugEmbedding/')
import warnings
warnings.simplefilter(action='ignore')
from tqdm import tnrange
import json
import numpy as np
import pandas as pd
import random
from decode import *
random.seed(1)
def recon_acc_score(configs, model, smiles_sample_lst):
match_lst = [... | github_jupyter |
# Growth media VMH high fat low carb diet
Similar to the western-style diet we will again start by loading the diet and depleting components absorbed by the host. In this case we have no manual annotation for which components should be diluted so we will use a generic human metabolic model to find those.
The growth me... | github_jupyter |
# Getting Physical Compute Inventory from Intersight using the Cisco Intersight Python SDK
In this lab you learn how to retrieve a list of physical compute inventory from Cisco Intersight using the Intersigight Python SDK.
## Objectives
The objective of this lab is to show how to:
* Authenticate with the Intersight... | github_jupyter |
```
#!python -m spacy download de_core_news_md --user
#!python -m spacy download en_core_web_lg --user
#nltk.download('vader_lexicon')
#!pip install --user xgboost
en_nlp = spacy.load("en_core_web_lg")
de_nlp = spacy.load("de_core_news_md")
import re
import spacy
#!python -m spacy download de_core_news_md
#!python -m s... | github_jupyter |
# Convolutional Neural Networks
## Project: Write an Algorithm for a Dog Identification App
---
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond ... | github_jupyter |
# Retail Demo Store Experimentation Workshop - Interleaving Recommendation Exercise
In this exercise we will define, launch, and evaluate the results of an experiment using recommendation interleaving using the experimentation framework implemented in the Retail Demo Store project. If you have not already stepped thro... | github_jupyter |
```
import sys
import os
sys.path.append('/Users/zhengz11/myscripts/git_clone/pn_kc/')
import json
import mushroom_2to3.connect_path as cp
import mushroom_2to3.analysis_routine as ar
# credential, to delete when push to remote
sys.path.append('/Users/zhengz11/myscripts/mushroom_v9/credential/')
from fafb_tokens impo... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import io
qpcr_results = pd.read_excel("./qpcr-data/2020324 LOD Study 1.xlsx", sheet_name="Results", skiprows=42, na_values=['Undetermined'])
```
# Standard Curve
```
sc = qpcr_results[qpcr_results['Sample Name'].str.cont... | github_jupyter |
# Deep Reinforcement Learning for the CartPole Environment
```
# Install packages
import gym
import copy
import torch
from torch.autograd import Variable
import random
import matplotlib.pyplot as plt
from PIL import Image
from IPython.display import clear_output
import math
import torchvision.transforms as T
import nu... | github_jupyter |
```
#Importing the basic libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings... | github_jupyter |
## Introduction to Spark Notebooks
Let's look at how to do data discovery/sandboxing with Spark Pools.
A few pointers to get started:
* only run 1 cell at a time
* you will need to change the connection strings to the storage
* `ESC + a` to add a cell `above` the current cell
* `ESC + b` to add a cell `belo... | github_jupyter |
<a href="https://colab.research.google.com/github/yohanesnuwara/reservoir-geomechanics/blob/master/homework%208/homework8_resgeomech_finally.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Homework 8. Identifying Critically Stressed Fractures**
... | github_jupyter |
```
# To enable plotting graphs in Jupyter notebook
%matplotlib inline
import pandas as pd
from sklearn.linear_model import LogisticRegression
# importing ploting libraries
import matplotlib.pyplot as plt
#importing seaborn for statistical plots
import seaborn as sns
#Let us break the X and y dataframes into trai... | github_jupyter |
```
from glob import glob
import datetime
import numpy as np
from astropy.table import Table
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
fr... | github_jupyter |
# Spam Text Classification
In second week of inzva Applied AI program, we are going to create a spam text classifier using RNN's. Our data have 2 columns. The first column is the label and the second column is text message itself. We are going to create our model using following techniques
- Embeddings
- SimpleRNN
- ... | github_jupyter |
# *Insight*-HXMT 相位分解谱处理示例
## ——[概览](#概览)、[数据预处理](#数据预处理)、[计时分析](#计时分析)、[能谱分析](#能谱分析)
庹攸隶 (tuoyl@ihep.ac.cn)
##### 最终结果：使用慧眼一次 Crab 的观测数据，产生 Crab 脉冲星的相位分解谱
## 概览
### 准备工作
该 Jupyter 文本使用 Python3 环境，若想执行以下所有命令，需要做这些准备：
* 安装并初始化 HXMTDAS 环境（例如能在终端中运行```hepical```命令）
* 使用 Python3.* 版本，并安装有 astropy, numpy, matplotlib 模块
... | github_jupyter |
```
#hide
#default_exp dev.nbdev
```
# NB-Dev Modification
<br>
### Imports
```
#exports
from fastcore.foundation import Config, Path
from nbdev import export
import os
import re
#exports
_re_version = re.compile('^__version__\s*=.*$', re.MULTILINE)
def update_version():
"Add or update `__version__` in the mai... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import skfuzzy as fuzz
from sklearn.datasets import make_moons
from deepART import dataset
np.random.seed(0)
X, y = make_moons(n_samples=200, noise=... | github_jupyter |
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