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# Travelling Salesman Problem (TSP)
If we have a list of city and distance between cities, travelling salesman problem is to find out the least sum of the distance visiting all the cities only once.
<img src="https://user-images.githubusercontent.com/5043340/45661145-2f8a7a80-bb37-11e8-99d1-42368906cfff.png" width="4... | github_jupyter |
# Spark on Tour
## Ejemplo de procesamiento de datos en streaming para generar un dashboard en NRT
En este notebook vamos a ver un ejemplo completo de como se podría utilizar la API de streaming estructurado de Spark para procesar un stream de eventos de puntuación en vivo, en el tiempo real, y generar como salida un ... | github_jupyter |
# Federated Tensorflow Mnist Tutorial
# Long-Living entities update
* We now may have director running on another machine.
* We use Federation API to communicate with Director.
* Federation object should hold a Director's client (for user service)
* Keeping in mind that several API instances may be connacted to one D... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import os
print(os.getcwd())
os.chdir('/content/drive/My Drive/Colab Notebooks/summarization')
print(os.listdir())
import os
import numpy as np
import pandas as pd
import sys
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/2_transfer_learning_roadmap/6_freeze_base_network/1.1)%20Understand%20the%20effect%20of%20freezing%20base%20model%20in%20transfer%20learning%20-%201%20-%20mxnet.ipynb" target="_parent"><img src="https://colab.researc... | github_jupyter |
```
from nornir import InitNornir
nr = InitNornir(config_file="config.yaml")
```
# Executing tasks
Now that you know how to initialize nornir and work with the inventory let's see how we can leverage it to run tasks on groups of hosts.
Nornir ships a bunch of tasks you can use directly without having to code them yo... | github_jupyter |
# 2040 le cap des 100% de voitures électriques
*Etude data - Projet 8 - @Nalron (août 2020)*\
*Traitement des données sur Jupyter Notebook (Distribution Anaconda)*\
*Etude réalisée en langage Python*
Visualisation des Tableaux de bord: [Tableau Public](https://public.tableau.com/profile/nalron#!/vizhome/ElectricCarsF... | github_jupyter |
# Linear Regression
## Setup
First, let's set up some environmental dependencies. These just make the numerics easier and adjust some of the plotting defaults to make things more legible.
```
# Python 3 compatability
from __future__ import division, print_function
from six.moves import range
# system functions that... | github_jupyter |
# LASSO and Ridge Regression
This function shows how to use TensorFlow to solve lasso or ridge regression for $\boldsymbol{y} = \boldsymbol{Ax} + \boldsymbol{b}$
We will use the iris data, specifically: $\boldsymbol{y}$ = Sepal Length, $\boldsymbol{x}$ = Petal Width
```
# import required libraries
import matplotlib.... | github_jupyter |
## Use a Decision Optimization model deployed in Watson Machine Learning
This notebook shows you how to create and monitor jobs, and get solutions using the Watson Machine Learning Python Client.
This example only applies to Decision Optimization in Watson Machine Learning Local and Cloud Pak for Data/Watson Studio L... | github_jupyter |
## Preparation
Welcome to the Vectice tutorial notebook!
Through this notebook, we will be illustrating how to log the following information into Vectice using the Vectice Python library:
- Dataset versions
- Model versions
- Runs and lineage
For more information on the tutorial, please refer to the "Vectice Tutori... | github_jupyter |
# Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is based off of Andrej Karpathy's [post on RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and [i... | github_jupyter |
```
%load_ext autoreload
%autoreload 2 """Reloads all functions automatically"""
%matplotlib notebook
from irreversible_stressstrain import StressStrain as strainmodel
import test_suite as suite
import graph_suite as plot
import numpy as np
model = strainmodel('ref/HSRS/22').get_experimental_data()
slopes = suite.g... | github_jupyter |
# `scinum` example
```
from scinum import Number, Correlation, NOMINAL, UP, DOWN, ABS, REL
```
The examples below demonstrate
- [Numbers and formatting](#Numbers-and-formatting)
- [Defining uncertainties](#Defining-uncertainties)
- [Multiple uncertainties](#Multiple-uncertainties)
- [Configuration of correlations](#... | github_jupyter |
```
# !wget http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv
import tensorflow as tf
import re
import numpy as np
import pandas as pd
from tqdm import tqdm
import collections
from unidecode import unidecode
from sklearn.cross_validation import train_test_split
def build_dataset(words, n_words):
count = [['P... | github_jupyter |
# VAE outlier detection on CIFAR10
## Method
The Variational Auto-Encoder ([VAE](https://arxiv.org/abs/1312.6114)) outlier detector is first trained on a batch of unlabeled, but normal (*inlier*) data. Unsupervised training is desireable since labeled data is often scarce. The VAE detector tries to reconstruct the in... | github_jupyter |
# Notes:
This notebook is used to predict demand of Victoria state (without using any future dataset)
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from tsa_utils import *
from statsmodels.tsa.stattools import pacf
from sklearn.ensemble import RandomForestRegressor
... | github_jupyter |
# Converters for Quadratic Programs
Optimization problems in Qiskit's optimization module are represented with the `QuadraticProgram` class, which is generic and powerful representation for optimization problems. In general, optimization algorithms are defined for a certain formulation of a quadratic program and we ne... | github_jupyter |
# Object Detection
*Object detection* is a form of computer vision in which a machine learning model is trained to classify individual instances of objects in an image, and indicate a *bounding box* that marks its location. Youi can think of this as a progression from *image classification* (in which the model answers... | github_jupyter |
# Essential: Static file management with SourceLoader
Data pipelines usually interact with external systems such as SQL databases. Using relative paths to find such files is error-prone as the path to the file depends on the file loading it, on the other hand, absolute paths are to restrictive, the path will only work... | github_jupyter |
## Simulation Procedures
## The progress of simulation
We simulate paired scDNA and RNA data following the procedure as illustrated in supplement (Figure S1). The simulation principle is to coherently generate scRNA and scDNA data from the same ground truth genetic copy number and clonality while also allowing adding... | github_jupyter |
## Compile a training set using ASPCAP normalization
```
from utils_h5 import H5Compiler
from astropy.io import fits
import numpy as np
# To create a astroNN compiler instance
compiler_aspcap_train = H5Compiler()
compiler_aspcap_train.teff_low = 4000 # Effective Temperature Upper
compiler_aspcap_train.teff_high = 55... | github_jupyter |
```
import os; os.chdir('../')
from tqdm import tqdm
import pandas as pd
import numpy as np
from sklearn.neighbors import BallTree
%matplotlib inline
from urbansim_templates import modelmanager as mm
from urbansim_templates.models import MNLDiscreteChoiceStep
from urbansim.utils import misc
from scripts import datasour... | github_jupyter |
# Semantic Text Summarization
Here we are using the semantic method to understand the text and also keep up the standards of the extractive summarization. The task is implemnted using the various pre-defined models such **BERT, BART, T5, XLNet and GPT2** for summarizing the articles. It is also comapared with a classic... | github_jupyter |
# Importing the libraries
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
```
# Importing the datasets
```
dataset = pd.read_csv("train_ctrUa4K.csv")
dataset2 = pd.read_csv("test_lAUu6dG.csv")
dataset = dataset.drop(['Loan_ID'], axis = 1)
dataset2 = dataset2.drop(['Lo... | github_jupyter |
```
%matplotlib inline
```
Failed Model Fits
=================
Example of model fit failures and how to debug them.
```
# Import the FOOOFGroup object
from fooof import FOOOFGroup
# Import simulation code to create test power spectra
from fooof.sim.gen import gen_group_power_spectra
# Import FitError, which we wi... | github_jupyter |
# Transfer Learning
In this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html).
ImageNet is a m... | github_jupyter |
```
#cell-width control
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:80% !important; }</style>"))
```
# Imports
```
#packages
import numpy
import tensorflow as tf
from tensorflow.core.example import example_pb2
#utils
import os
import random
import pickle
import struct
impor... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter |
# Quantum Machine Learning and TTN
Let's look at the Tree Tensor Network as a model for quantum machine learning.
## What you will learn
1. TTN model
2. Optimization
## Install Blueqat
```
!pip install blueqat
```
The model we are going to build is called TTN. The quantum circuit is as follows.
<img src="../tutori... | github_jupyter |
```
# Importing needed libraries
import datetime
import pandas as pd
# Fetching the data from official site of Ministry of Health and Family Welfare | Government of India
try:
url = "https://www.mohfw.gov.in/"
dfs = pd.read_html(url)
for i in range(len(dfs)):
df = dfs[i]
if (len(df.columns... | github_jupyter |
# Artificial Intelligence Nanodegree
## Machine Translation Project
In this notebook, sections that end with **'(IMPLEMENTATION)'** in the header indicate that the following blocks of code will require additional functionality which you must provide. Please be sure to read the instructions carefully!
## Introduction
I... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import math
from scipy import stats
import pickle
from causality.analysis.dataframe import CausalDataFrame
from sklearn.linear_model import LinearRegression
import datetime
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['font.sans-seri... | github_jupyter |
```
import git_access,api_access,git2repo
import json
from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import networkx as nx
import re
import git2data
import social_interaction
access_token = '--'
repo_owner = 'jankotek'
source_type = 'github_repo'
git_u... | github_jupyter |
# 数据抓取：
> # Beautifulsoup简介
***
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
# 需要解决的问题
- 页面解析
- 获取Javascript隐藏源数据
- 自动翻页
- 自动登录
- 连接API接口
```
import urllib2
from bs4 import BeautifulSoup
```
- 一般的数据抓取，使用urllib2和beautifulsoup配合就可以了。
- 尤其是对于翻页时url出现规则变化的网页，只需要处理规则化的url就可以了。
- 以简单的例子是... | github_jupyter |
# Library
```
import numpy as np
import torch
import torch.nn as nn
from utils import *
from dataset import TossingDataset
from torch.utils.data import DataLoader
```
# Model
```
class NaiveMLP(nn.Module):
def __init__(self, in_traj_num, pre_traj_num):
super(NaiveMLP, self).__init__()
self.hidd... | github_jupyter |
## Appendix 1: Optional Refresher on the Unix Environment
### A1.1) A Quick Unix Overview
In Jupyter, many of the same Unix commands we use to navigate in the regular terminal can be used. (However, this is not true when we write standalone code outside Jupyter.) As a quick refresher, try each of the following:
```
l... | github_jupyter |
<h1> Logistic Regression using Spark ML </h1>
Set up bucket
```
BUCKET='cloud-training-demos-ml' # CHANGE ME
os.environ['BUCKET'] = BUCKET
# Create spark session
from pyspark.sql import SparkSession
from pyspark import SparkContext
sc = SparkContext('local', 'logistic')
spark = SparkSession \
.builder \
.... | github_jupyter |
```
import pandas as pd
import warnings
import altair as alt
from urllib import request
import json
# fetch & enable a Spanish timeFormat locale.
with request.urlopen('https://raw.githubusercontent.com/d3/d3-time-format/master/locale/es-ES.json') as f:
es_time_format = json.load(f)
alt.renderers.set_embed_options(tim... | github_jupyter |
```
%cd ~/NetBeansProjects/ExpLosion/
from notebooks.common_imports import *
from gui.output_utils import *
from gui.user_code import pairwise_significance_exp_ids
query = {'expansions__decode_handler': 'SignifiedOnlyFeatureHandler',
'expansions__vectors__dimensionality': 100,
'expansions__vectors__r... | github_jupyter |
# Lussen
Looping `for` a `while`
## `for` lussen
```
for i in [0, 1, 2]:
print("i is", i)
for i in range(0, 3):
print("i is", i)
for x in [10, 15, 2020]:
print("x is", x)
```
```python
for i in ...:
print("Gefeliciteerd")
```
Hoe kan dit 10 keer worden uitgevoerd? Hier is een reeks aan oplossingen ... | github_jupyter |
```
# Setup directories
from pathlib import Path
basedir = Path().absolute()
libdir = basedir.parent.parent.parent
# Other imports
import pandas as pd
import numpy as np
from datetime import datetime
from ioos_qc.plotting import bokeh_plot_collected_results
from bokeh import plotting
from bokeh.io import output_note... | github_jupyter |
Early stopping of model simulations
===================
For certain distance functions and certain models it is possible to calculate the
distance on-the-fly while the model is running. This is e.g. possible if the distance is calculated as a cumulative sum and the model is a stochastic process. For example, Markov Ju... | github_jupyter |
### This notebook covers how to get statistics on videos returned for a list of search terms on YouTube with the use of YouTube Data API v3.
First go to [Google Developer](http://console.developers.google.com/) and enable YouTube Data API v3 by clicking on the button "+ ENABLE APIS AND SERVICES" and searching for YouT... | github_jupyter |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import time
import tensorflow as tf
TRAIN_FILE = 'train.tfrecords'
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example ... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import numpy as np
from numpy.random import default_rng
import random
import collections
import re
import tensorflow as tf
from tqdm import tqdm
max_seq_length_encoder = 512
max_seq_length_decoder = 128
masked_lm_prob = 0.2
max_predictions_per_seq = int(masked_lm_p... | github_jupyter |
# Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need.
**Instructions:**
- You will be using Python 3.
- Avoid using for-loops and while-lo... | github_jupyter |
# Explore the generated data
Here we explore the data that is generated with the [generate-data.ipynb](generate-data.ipynb) notebook.
You can either run the simulations or download the data set. See [README.md](README.md) for the download link and instructions.
### Joining the seperate data files of one simulation tog... | github_jupyter |
## In this notebook, images and their corresponding metadata are organized. We take note of the actual existing images, combine with available metadata, and scraped follower counts. After merging and dropping image duplicates, we obtain 7702 total images.
```
import pandas as pd
import numpy as np
import os
from PIL i... | github_jupyter |
<img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/branding-iss.png' width=15% style="float: right;">
<img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/branding-nus.png' width=15% style="float: right;">
---
```
import IPython.display
IPy... | github_jupyter |
# Sequence to Sequence attention model for machine translation
This notebook trains a sequence to sequence (seq2seq) model with two different attentions implemented for Spanish to English translation.
The codes are built on TensorFlow Core tutorials: https://www.tensorflow.org/tutorials/text/nmt_with_attention
```
i... | github_jupyter |
# QCoDeS Example with DynaCool PPMS
This notebook explains how to control the DynaCool PPMS from QCoDeS.
For this setup to work, the proprietary `PPMS Dynacool` application (or, alternatively `Simulate PPMS Dynacool`) must be running on some PC. On that same PC, the `server.py` script (found in `qcodes/instrument_dri... | github_jupyter |
```
import json
import os
import random
import re
from itertools import product
import numpy as np
import pandas as pd
from more_itertools import distinct_combinations
from plotnine import *
from sklearn import feature_extraction, metrics
ROOT_PATH = os.path.dirname(os.path.abspath(os.getcwd()))
def inspect_df(df: p... | github_jupyter |
```
# default_exp checker
```
# Dependency Checker
> A pragmatic way to talk with pypi and find out what dependencies are out of date
```
#hide
from nbverbose.showdoc import *
```
## Dependency Traversing
Sometimes, we may want to check the current installed versions of a project's basic dependencies, and further ... | github_jupyter |
SPARQL Transformer evaluation
=========================
This notebook contains some quantitative measures for the evaluation of SPARQL Transformer.
```
import json
import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ipywidgets import FloatProgress
from IPython.display i... | github_jupyter |
```
#Import the necessary methods from tweepy library
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
#Variables that contains the user credentials to access Twitter API
access_token = "your_access_token"
access_token_secret = "your_access_secret_token"
consumer_k... | github_jupyter |
# hello paddle: 从普通程序走向机器学习程序
**作者:** [PaddlePaddle](https://github.com/PaddlePaddle) <br>
**日期:** 2021.12 <br>
**摘要:** 这篇示例向你介绍普通程序跟机器学习程序的区别，并带着你用飞桨框架，实现第一个机器学习程序。
## 一、普通程序跟机器学习程序的逻辑区别
作为一名开发者，你最熟悉的开始学习一门编程语言，或者一个深度学习框架的方式，可能是通过一个hello world程序。
学习飞桨也可以这样，这篇小示例教程将会通过一个非常简单的示例来向你展示如何开始使用飞桨。
机器学习程序跟通常的程序最大的不同是，通常的... | github_jupyter |
# Accumulation of roundoof error
In this notebook we'll study some effects of accumulation of roundoof error.
# Unstable Algorithms
We need to solve this integral for $n=1,2,....8$
$$y_n=\int_0^1\frac{x^n}{x+5}$$
We write the equation like this:
$$y_n = \frac{1}{n} - 5y_{n-1}$$
$$y_{1}=1-5(y_{0}+\epsilon )=1-5y_{0... | github_jupyter |
# Inventory Control with Lead Times and Multiple Suppliers
## Description
One potential application of reinforcement learning involves ordering supplies with mutliple suppliers having various lead times and costs in order to meet a changing demand. Lead time in inventory management is the lapse in time between when ... | github_jupyter |
```
import numpy as np
import pandas as pd
import tensorflow as tf
from data_process import build_vocab, batch_iter, sentence_to_index
from models import LSTM, biLSTM, deepBiLSTM
train = pd.read_csv('./data/train-5T.txt', delimiter='\t')
test = pd.read_csv('./data/test-1T.txt', delimiter='\t')
X_train = train.document
... | github_jupyter |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Nonlinear Filtering
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
## Introduction
T... | github_jupyter |
# Classifying Business Documents using Deep Learning
## IBM Coursera Advanced Data Science Capstone - Results Demo
## Sumudu Tennakoon
```
import pandas as pd
import numpy as np
import sys
import os
import re
import matplotlib.pyplot as plt
from datetime import date
from sklearn.model_selection import train_test_spl... | github_jupyter |
# Numpy
The basis of most scientific programming in Pyhton is the *numerical Python* library, `numpy`. NumPy gives us many tools - including a fast and efficient data type, the `numpy Array` - for working with numerical data.
## Numpy Array
NumPy is built around the `array`. This is a data structure defined in NumP... | github_jupyter |
# Ray RLlib - Sample Application: CartPole
© 2019-2021, Anyscale. All Rights Reserved
We were briefly introduced to the `CartPole` example and the OpenAI gym `CartPole-v1` environment ([gym.openai.com/envs/CartPole-v1/](https://gym.openai.com/envs/CartPole-v1/))... | github_jupyter |
```
import tensorflow as tf
tf.config.experimental.list_physical_devices()
tf.test.is_built_with_cuda()
```
# Importing Libraries
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os.path as op
import pickle
import tensorflow as tf
from tensorflow import keras
from keras.models im... | github_jupyter |
# Paper Trends
## Imports
```
%load_ext autoreload
%autoreload 2
%aimport
%matplotlib inline
import os
import sys
nb_dir = os.path.dirname(os.path.split(os.getcwd())[0])
if nb_dir not in sys.path:
sys.path.append(nb_dir)
from tqdm import tqdm_notebook as tqdm
import pandas as pd
from turicreate import SFrame, loa... | github_jupyter |
# Residual Networks
Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](h... | github_jupyter |
# Training Job in Internet-free Mode
If you want to isolate your training data and training container from the rest of the Internet, then you should create the training job in a private subnet. A private subnet is a subnet in your VPC without a route to an Internet Gateway. This means, by default, no inbound calls to ... | github_jupyter |
# Algorithm used :
```
%matplotlib inline
import gym
import itertools
import matplotlib
import numpy as np
import pandas as pd
import sys
if "../" not in sys.path:
sys.path.append("../")
from collections import defaultdict
from lib.envs.windy_gridworld import WindyGridworldEnv
from lib ... | github_jupyter |
```
# Deprecated
# packages: random
import random
# packages: data structure
import numpy as np
import pandas as pd
import astropy.io as io
# packages: image generation and plot generation
from matplotlib import pyplot as plt
# pandas
# https://pandas.pydata.org/pandas-docs/stable/tutorials.html
# https://pandas.pyda... | github_jupyter |
# Chapter 2: Conditional probability
----
```
import numpy as np
```
## Simulating the frequentist interpretation
Recall that the frequentist interpretation of conditional probability based on a large number `n` of repetitions of an experiment is $P(A|B) ≈ n_{AB}/n_{B}$, where $n_{AB}$ is the number of times that... | github_jupyter |
# Building your Deep Neural Network: Step by Step
Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!
- In this notebook, you will implement all the functio... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
# Text Classification of Movie Reviews
```
from helpers import Timer
from sklearn.datasets import load_files
reviews_train = load_files("aclImdb/train/")
text_train, y_train = reviews_train.data, reviews_train.target
print("Number of docum... | github_jupyter |
<p style="font-family: Arial; font-size:3.75vw;color:purple; font-style:bold"><br>
matplotlib Exercise Notebook
</p><br>
# Exercise Notebook Instructions
### 1. Important: Only modify the cells which instruct you to modify them - leave "do not modify" cells alone.
The code which tests your responses assumes you ha... | github_jupyter |
# Variational Inference and Learning in the Big Data Regime
Many real-world modelling solutions require fitting models with large numbers of data-points and parameters, which is made convenient recently through software implementing automatic differentiation, but also require uncertainty quantification. Variational in... | github_jupyter |
# ML Strategy
* Collect more data
* Collect more diverse trainign set
* Train algorithm longer with gradient descetn
* Try adam isntead of gradient descent
* Try bigger networks
* Try smaller networks
* Try dropout
* Add L2 regularizatión
* Network architecture
* Network archicteture
- Activvation
- \# hidden... | github_jupyter |
# Travail Ecrit - Python
* Gymnase du Bugnon, site de l'Ours
* OC informatique
* Sujet : chapitres 1-10 du livre *Pensez en Python*
* Mirko Pirona
* Date : jeudi 13 novembre 2018
## **Exercice : expression arithmétique**
Initialisez les variables `(a, b, c, x)` avec les valeurs `(2, 3, 4, 5)`.
Calculez l'expressio... | github_jupyter |
```
# Visualization of the KO+ChIP Gold Standard from:
# Miraldi et al. (2018) "Leveraging chromatin accessibility for transcriptional regulatory network inference in Th17 Cells"
# TO START: In the menu above, choose "Cell" --> "Run All", and network + heatmap will load
# NOTE: Default limits networks to TF-TF edges i... | github_jupyter |
# <center> #DHBSI 2016: Computational Text Analysis </center>
## <center> Laura Nelson <br/> <em>Postdoctoral Fellow | Digital Humanities @ Berkeley | Berkeley Institute for Data Science </em> </center>
## <center> Teddy Roland <br/> <em> Coordinator, Digital Humanities @ Berkeley <br/> Lecturer, UC Berkeley </em> ... | github_jupyter |
# Data Preprocessing for Topic Monitoring(Facebook)
```
import pandas as pd
import numpy as np
import re
import csv
from langdetect import detect
import nltk
# nltk.download('punkt')
# nltk.download('maxent_treebank_pos_tagger')
# nltk.download('wordnet')
# nltk.download('averaged_perceptron_tagger')
# nltk.download('... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
import warnings
warnings.filterwarnings("ignore")
sys.path.append("../")
from modules.data.conll2003.prc import conll2003_preprocess
data_dir = "/home/eartemov/ae/work/conll2003/"
conll2003_preprocess(data_dir)
```
## IO markup
### Train
```
from modules.data imp... | github_jupyter |
```
import torch
from torchvision import transforms
import torch.nn.functional as F
import torch.nn as nn
from PIL import Image
import imageio
import os
from google.colab import drive
from google.colab import drive
drive.mount('/content/drive')
class YOLO(nn.Module):
def __init__(self, img_width, row_size):
... | github_jupyter |
# Db2 11.5.4 RESTful Programming
The following notebook is a brief example of how to use the Db2 11.5.4 RESTful Endpoint service to extend the capabilies of Db2.
Programmers can create Representational State Transfer (REST) endpoints that can be used to interact with Db2.
Each endpoint is associated with a single SQL... | github_jupyter |
# Inference acceleration of `T5` for large batch size / long sequence length / > large models
Every week or so, a new impressive few shots learning work taking advantage of autoregressive models is released by some team around the world.
Still `LLM` inference is rarely discussed and few projects are focusing on this... | github_jupyter |
```
!wget -q https://github.com/CISC-372/Notebook/releases/download/a4/test.csv
!wget -q https://github.com/CISC-372/Notebook/releases/download/a4/train.csv
# comment your understanding of each function
import pandas as pd
import csv
xy_train_df = pd.read_csv('train.csv')
x_test_df = pd.read_csv('test.csv', index_c... | github_jupyter |
## Exploratory analysis of the US Airport Dataset
This dataset contains data for 25 years[1995-2015] of flights between various US airports and metadata about these routes. Taken from Bureau of Transportation Statistics, United States Department of Transportation.
Let's see what can we make out of this!
```
%matplot... | github_jupyter |
500 hPa Vorticity Advection
===========================
Plot an 500-hPa map with calculating vorticity advection using MetPy calculations.
Beyond just plotting 500-hPa level data, this uses calculations from `metpy.calc` to find
the vorticity and vorticity advection. Currently, this needs an extra helper function to
... | github_jupyter |
```
"""
Snowflake Batch Prediction API Snowflake S3 scoring job
v1.0 Mike Taveirne (doyouevendata) 3/21/2020
"""
import pandas as pd
import requests
import time
from pandas.io.json import json_normalize
import snowflake.connector
import my_creds
#from imp import reload
#reload(my_creds)
# datarobot parameters
API_KEY... | github_jupyter |
# Inference and Validation
Now that you have a trained network, you can use it for making predictions. This is typically called **inference**, a term borrowed from statistics. However, neural networks have a tendency to perform *too well* on the training data and aren't able to generalize to data that hasn't been seen... | github_jupyter |
**Import library**
```
import pandas as pd
import numpy as np
import calendar
from datetime import datetime
import time
# Standard plotly imports
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("paper", font_scale=1.3)
sns.set_style('w... | github_jupyter |
<a href="https://colab.research.google.com/github/ewotawa/secure_private_ai/blob/master/Section_2_Federated_Learning_Final_Project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Federated Learning Final Project
## Overview
* See <a href="https:... | github_jupyter |
```
% load_ext autoreload
% autoreload 2
% matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import os, sys
opj = os.path.join
from tqdm import tqdm
from ex_mnist import p
from dset import get_dataloader
sys.path.append('../../s... | github_jupyter |
# Twitter Konversationen zu einem Thema als Netzwerk untersuchen
- Aus Twitter-Daten kann man besonders gut Netzwerke basteln.
- Dabei können wir frei definieren,wann eigentlich ein Nutzer mit einem anderen verbunden ist. Die gebräuchlichsten Definitionen sind:
1. Nutzer A retweetet Nutzer B (RT plotti was für ein... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt # for plotting
import numpy as np # for matrix and vector computations
import pandas as pd
import seaborn as sns
```
### Debugging
* Python array indices start from zero
* Vector/matrix operations work only with numpy arrays.Inspect matrix operations to make sur... | github_jupyter |
<a href="https://colab.research.google.com/github/hf2000510/infectious_disease_modelling/blob/master/part_two.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Make sure to open in Colab to see the plots!
### Importing the libraries
```
from scipy.i... | github_jupyter |
<a href="https://colab.research.google.com/github/ayulockin/Explore-NFNet/blob/main/Train_Basline_With_Gradient_Clipping.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 🧰 Setups, Installations and Imports
```
%%capture
!pip install wandb --upgr... | github_jupyter |
# Expression Quality Control (Part 2)
This is a template notebook for performing the final quality control on your organism's expression data. This requires a curated metadata sheet.
## Setup
```
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from os i... | github_jupyter |
# Hyperparams And Distributions
This page introduces the hyperparams, and distributions in Neuraxle. You can find [Hyperparams Distribution API here](https://www.neuraxle.org/stable/api/neuraxle.hyperparams.distributions.html), and
[Hyperparameter Samples API here](https://www.neuraxle.org/stable/api/neuraxle.hyperpa... | github_jupyter |
# Hyperparameter Tuning using SageMaker Tensorflow Container
This tutorial focuses on how to create a convolutional neural network model to train the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) using **SageMaker TensorFlow container**. It leverages hyperparameter tuning to kick off multiple training jobs with d... | github_jupyter |
```
import numpy as np
import os
import sys
import xarray as xr
import scipy.io as sio
import matplotlib.pyplot as plt
import datetime
from dotenv import load_dotenv, find_dotenv
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()
load_dotenv(dotenv_path)
src_dir = os.env... | github_jupyter |
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