text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
<a href="https://colab.research.google.com/github/wileyw/DeepLearningDemos/blob/master/SinGAN/DoubleGAN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# SinGAN
[Official SinGAN Repository](https://github.com/tamarott/SinGAN)
In this notebook, we ... | github_jupyter |
# 12. Analysing proteins using python
In previous sections we have primarily focused on showing you the basic components of python. We have primarily looked at small example cases where we process some type of input data to generate some kind of text or numerical output.
In this section we want to show you how you c... | github_jupyter |
<img src="../meta/logo.png" width=400 align="left"/>
Contributors:
- *Liubov Elkhovskaya* <span style="color:blue">lelkhovskaya@itmo.ru</span>
- *Alexander Kshenin* <span style="color:blue">adkshenin@itmo.ru</span>
- *Marina Balakhontceva* <span style="color:blue">mbalakhontceva@itmo.ru</span>
- *Sergey Kovalchuk* <sp... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Data split
Data splitting is one of the most vital tasks in assessing recommendation systems. Splitting strategy greatly affects the evaluation protocol so that it should always be taken into careful considerati... | github_jupyter |
# Tutorial 1: Neural Nets and Datasets
In this first tutorial, we'll cover the basics of training neural networks and loading/generating datasets. We've extended pytorch neural networks to have a bunch of handy tools. We'll need all these tools to evaluate Lipschitz constants. Since we frequently operate with neural ne... | github_jupyter |
# Example: CanvasXpress correlation Chart No. 3
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/correlation-3.html
This example is generated using the reproducible JSON obtained from the abo... | github_jupyter |
# Personal Pools
Launch this tutorial in a Jupyter Notebook on Binder:
[](https://mybinder.org/v2/gh/htcondor/htcondor-python-bindings-tutorials/master?urlpath=lab/tree/Personal-Pools.ipynb)
A Personal HTCondor Pool is an HTCondor Pool that has a single owner, who is:
- ... | github_jupyter |
<table width="100%">
<tr style="border-bottom:solid 2pt #009EE3">
<td style="text-align:left" width="10%">
<a href="generation_of_time_axis.dwipynb" download><img src="../../images/icons/download.png"></a>
</td>
<td style="text-align:left" width="10%">
<a href="https:... | 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 |
TSG073 - InfluxDB logs
======================
Steps
-----
### Parameters
```
import re
tail_lines = 2000
pod = None # All
container = "influxdb"
log_files = [ "/var/log/supervisor/log/influxdb*.log" ]
expressions_to_analyze = []
```
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes clien... | github_jupyter |
<a href="https://colab.research.google.com/github/PGM-Lab/probai-2021-pyro/blob/main/Day1/notebooks/students_PPLs_Intro.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img src="https://github.com/PGM-Lab/probai-2021-pyro/blob/main/Day1/Figures/blue... | github_jupyter |
# DATA WRANGLING AND CLEANING
#### Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. Data wrangling is increasingly ubiquitous at today’s top firms. Data has become more diverse and unstructured, demanding increased time spent ... | github_jupyter |
Copyright (C) 2017 Ashish Gupta<br>
<br>
This program is free software: you can redistribute it and/or modify<br>
it under the terms of the GNU General Public License as published by<br>
the Free Software Foundation, either version 3 of the License, or<br>
(at your option) any later version.<br>
<br>
This program is di... | github_jupyter |
# 5.9 含并行连结的网络（GoogLeNet）
```
import time
import torch
from torch import nn, optim
import torch.nn.functional as F
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(torch.__version__)
print(device)
```
## 5.9.1 Inception 块
```
... | github_jupyter |
# Modeling and Simulation in Python
Chapter 23
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 a... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
```
# Functions For Reading Tint Data
```
import struct
import numpy.matlib
def getspikes(fullpath):
"""
This function will return the spike data, spike times, and spike parameters from Tint tetrode data.
Example:
tetrode_fullpath = 'C:\\exam... | github_jupyter |
# A brief intro to pydeck
pydeck is made for visualizing data points in 2D or 3D maps. Specifically, it handles
- rendering large (>1M points) data sets, like LIDAR point clouds or GPS pings
- large-scale updates to data points, like plotting points with motion
- making beautiful maps
Under the hood, it's powered by... | github_jupyter |
# Jupyter Notebook
Jupyter Notebooks is a good place to start new python experiences...
This page is self-editable, feel free to play...
* CTRL-Enter => Run a cells.
* CTRL-S => Save your notebooks.
* H => More shortcuts...
What is it good for?
* Interactive documentation.
* Send-boxes.
* Write documented tests.
... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import accuracy_score
import pickle
from sklearn.metrics import r2_score
from sklea... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from numpy import linalg as LA
def list_files(directory):
if os.path.exists(directory) == False:
return None
return [x for x in os.listdir(directory) if os.path.isfile(os.path.join(directory, x))]
def LoadImageData(dPath, fil... | github_jupyter |
# Model Fitting - XGBoost
Fit the XGBoost model using the training dataset. XGBoost is faster and has potentially better accuracy. This allow me to use more features and test changes faster.
```
%load_ext autoreload
%autoreload 2
%matplotlib notebook
import numpy as np
from numpy import mean
from numpy import std
fr... | github_jupyter |
This program will prepare a basic automation routine for functionalizing CO molecules on a clean Cu(111) surface. This program is provided to demonstrate of the automation capabilities for CO-AFM utilizing a CreaTec STM/AFM system.
Programs from other publications may provide fully-autonomous construction and tip-pre... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import wikipedia
import xml.etree.ElementTree as ET
import re
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.model_selection import cross_val_score, KFold
import xgboost as xgb
from sklearn.metrics import r2... | github_jupyter |
```
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
```
# ExecComp
`ExecComp` is a component that provides a shortcut for building an ExplicitComponent that
represents a set of simple mathematical relationships between inputs and out... | github_jupyter |
### Generating `publications.json` partitions
This is a template notebook for generating metadata on publications - most importantly, the linkage between the publication and dataset (datasets are enumerated in `datasets.json`)
Process goes as follows:
1. Import CSV with publication-dataset linkages. Your csv should h... | github_jupyter |
# Bootstrap distances to the future
Estimate uncertainty of distance to the future values per sample and model using the bootstrap of observed distances across time.
## Define inputs, outputs, and parameters
```
# Define inputs.
model_distances = snakemake.input.model_distances
# Define outputs.
output_table = snak... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import optuna
df = pd.read_csv("../input/30days-folds/train_folds.csv")
df_test = pd.read_csv("../input/30-days-of-ml/test.csv")
sample_submission = pd.read_csv(".... | github_jupyter |
```
import pandas as pd
import numpy as np
from typing import List, Union
from scipy.special import erf, binom
from statdepth.depth._depthcalculations import _subsequences
def _norm_cdf(x: np.array, mu: float, sigma: float):
"""
Estimate the CDF at x for the normal distribution parametrized by mu and sigma^2... | github_jupyter |
# AR6 WG1 - SPM.4
This notebook reproduces the panel a) of **Figure SPM.4** of the IPCC's *Working Group I contribution to the Sixth Assessment Report* ([AR6 WG1](https://www.ipcc.ch/assessment-report/ar6/)).
The data supporting the SPM figure is published under a Creative Commons CC-BY license at
the [Centre for En... | github_jupyter |
# Linear Regression
Example from [Introduction to Computation and Programming Using Python](https://mitpress.mit.edu/books/introduction-computation-and-programming-using-python-revised-and-expanded-edition)
```
import matplotlib.pyplot as plot
from numpy import (
array,
asarray,
correlate,
cov,
ge... | github_jupyter |
Example 4 - Anisotropic Bearings.
====
In this example, we use the rotor seen in Example 5.9.2 from 'Dynamics of Rotating Machinery' by MI Friswell, JET Penny, SD Garvey & AW Lees, published by Cambridge University Press, 2010.
Both bearings have a stiffness of 1 MN/m in the x direction and 0.8 MN/m in the
y directio... | github_jupyter |
# Heterogeneous Effects
> **Author**
- [Paul Schrimpf *UBC*](https://economics.ubc.ca/faculty-and-staff/paul-schrimpf/)
**Prerequisites**
- [Regression](regression.ipynb)
- [Machine Learning in Economics](ml_in_economics.ipynb)
**Outcomes**
- Understand potential outcomes and treatment effects
- Apply gene... | github_jupyter |
# Construction of Regression Models using Data
Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Notebook version: 2.1 (Sep 27, 2019)
Changes: v.1.0 - First version. Extracted from regression_intro_knn v.1.0.
v.1.1 - Compatibility with pyth... | github_jupyter |
# Web Data Scraping
[Spring 2021 ITSS Mini-Course](https://www.colorado.edu/cartss/programs/interdisciplinary-training-social-sciences-itss/mini-course-web-data-scraping) — ARSC 5040
[Brian C. Keegan, Ph.D.](http://brianckeegan.com/)
[Assistant Professor, Department of Information Science](https://www.colorado.edu... | github_jupyter |
Copyright © 2017-2021 ABBYY Production LLC
```
#@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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | github_jupyter |
# Descriptor Example: Attribute Validation
## LineItem Take #3: A Simple Descriptor
```
class Quantity:
def __init__(self, storage_name):
self.storage_name = storage_name
def __set__(self, instance, value):
if value > 0:
instance.__dict__[self.storage_name] = value
... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import mean_squared_error,r2_score
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import iqr
from keras.models import load_model
from keras.models import Sequential
from keras.models import Model
from keras... | github_jupyter |
# Introduction to GeoPandas
This quick tutorial introduces the key concepts and basic features of GeoPandas to help you get started with your projects.
## Concepts
GeoPandas, as the name suggests, extends the popular data science library [pandas](https://pandas.pydata.org) by adding support for geospatial data. If y... | github_jupyter |
# Advanced Usage Exampes for Seldon Client
## Istio Gateway Request with token over HTTPS - no SSL verification
Test against a current kubeflow cluster with Dex token authentication.
1. Install kubeflow with Dex authentication
```
INGRESS_HOST=!kubectl -n istio-system get service istio-ingressgateway -o jsonpath='... | github_jupyter |
## Introduction to PySpark
This article aims to give hands on experience in working with the DataFrame API in PySpark. You can download this article as a notebook and run the code yourself by clicking on the download button above and selecting `.ipynb`.
We will not aim to cover all the PySpark DataFrame functionality... | github_jupyter |
```
# import libraries
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
from tqdm import tqdm
from torch.utils.tensorboard i... | github_jupyter |
# Sentiment Analysis with TreeLSTMs in TensorFlow Fold
The [Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/treebank.html) is a corpus of ~10K one-sentence movie reviews from Rotten Tomatoes. The sentences have been parsed into binary trees with words at the leaves; every sub-tree has a label ranging fr... | github_jupyter |
Lists and Tuples
===
In this notebook, you will learn about lists, a super important data structure, that allows you to store more than one value in a single variable. This is one of the most powerful ideas in programming and introduces a number of other central concepts such as loops.
[Previous: Variables, Strings, a... | github_jupyter |
# Operations on word vectors
Welcome to your first assignment of this week!
Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings.
**After this assignment you will be able to:**
- Load pre-trained word vectors, and measure similarity usi... | github_jupyter |
# **Birth weight prediction**
---
## Load Libraries
```
import os
import numpy as np
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
from joblib import dump, load
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics impor... | github_jupyter |
```
from IPython.display import HTML
# Cell visibility - COMPLETE:
#tag = HTML('''<style>
#div.input {
# display:none;
#}
#</style>''')
#display(tag)
#Cell visibility - TOGGLE:
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$(... | github_jupyter |
# Name
Deploying a trained model to Cloud Machine Learning Engine
# Label
Cloud Storage, Cloud ML Engine, Kubeflow, Pipeline
# Summary
A Kubeflow Pipeline component to deploy a trained model from a Cloud Storage location to Cloud ML Engine.
# Details
## Intended use
Use the component to deploy a trained mo... | github_jupyter |
# MSTICpy - Mordor data provider and browser
### Description
This notebook provides a guided example of using the Mordor data provider and browser included with MSTICpy.
For more information on the Mordor data sets see the [Open Threat Research Forge Mordor GitHub repo](https://github.com/OTRF/mordor)
You must have ... | github_jupyter |
```
import tensorflow as tf
```
tf.train.Coordinator: help multiple thread stop together and report exceptions to a program that waits for them to stop.
tf.train.QueueRunner: create a number of threads cooperatiing to **enqueue** tensors in the **same** queue.
## Coordinator
### Key method
tf.train.Coordinator.sho... | github_jupyter |
```
class Opion():
def __init__(self):
self.dataroot= r'I:\irregular holes\paris_eval_gt' #image dataroot
self.maskroot= r'I:\irregular holes\testing_mask_dataset'#mask dataroot
self.batchSize= 1 # Need to be set to 1
self.fineSize=256 # image size
self.in... | github_jupyter |
#Errors and Exception Handling
In this lecture we will learn about Errors and Exception Handling in Python. You've definitely already encountered errors by this point in the course. For example:
```
print 'Hello
```
Note how we get a SyntaxError, with the further description that it was an EOL (End of Line Error) wh... | github_jupyter |
### Problem Statement
The task is to predict whether a potential promotee at checkpoint in the test set will be promoted or not after the evaluation process.
```
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics
from sk... | github_jupyter |
# Sentiment Analysis Using RNN
We use an Sequential LSTM to create a supervised learning approach for predicting the sentiment of an article. This notebook was adapted from https://www.kaggle.com/ngyptr/lstm-sentiment-analysis-keras.
#### Data and Packages Importing
```
import numpy as np
import pandas as pd
from s... | github_jupyter |
## Section 7.1: A First Plotly Streaming Plot
Welcome to Plotly's Python API User Guide.
> Links to the other sections can be found on the User Guide's [homepage](https://plot.ly/python/user-guide#Table-of-Contents:)
Section 7 is divided, into separate notebooks, as follows:
* [7.0 Streaming API introduction](http... | github_jupyter |
<a href="https://colab.research.google.com/github/maragraziani/interpretAI_DigiPath/blob/main/hands-on-session-2/hands-on-session-2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# <center> Hands-on Session 2</center>
## <center> Explainable Graph ... | github_jupyter |
<a href="https://colab.research.google.com/github/jonkrohn/ML-foundations/blob/master/notebooks/single-point-regression-gradient.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Gradient of a Single-Point Regression
In this notebook, we calculate ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Machine Learning
_**Text Classification Using Deep Learning**_
## Contents
1. [Introduction](#Introduction)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Train](#Train)
1. [Evaluate](#Evaluate)
## Introduction
This noteb... | github_jupyter |
# How do I _create_, _start_, & _monitor_ a task?
### Overview
We are getting into advanced techniques here and will need to leverage a few other cookbooks. You will need an app and some files in your project, then it is easy to start one. The beginning of this notebook **replicates** the <a href="tasks_create.ipynb"> ... | github_jupyter |
```
import pandas as pd
import sqlalchemy
import multiprocessing
import numpy as np
data = pd.read_excel('data/Budget-2018-19_Corrected.xlsx')
data.head()
data.columns = data.iloc[1]
data.drop([0,1], axis=0, inplace=True)
data.head()
data.columns
data['HEAD OF ACCOUNT'].head()
# Scheme Names
schemes = {'CSS': 'Centrall... | github_jupyter |
## Logisitic Regression classifier with L2 Regularization
### Load the libraries
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn... | github_jupyter |
```
import torch
import random
import torchvision
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.nn.functional as F
import argparse,os,time
import os
import time
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import trai... | github_jupyter |
<a href="https://colab.research.google.com/github/kevincong95/cs231n-emotiw/blob/master/notebooks/audio/1.0-la-audio-error-analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!git clone 'https://github.com/kevincong95/cs231n-emotiw.git'
``... | github_jupyter |
# Interpret Models
You can use Azure Machine Learning to interpret a model by using an *explainer* that quantifies the amount of influence each feature contribues to the predicted label. There are many common explainers, each suitable for different kinds of modeling algorithm; but the basic approach to using them is t... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
import math
from sklearn.model_selection import train_test_split
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline
df_train = pd.read_csv("../data/train.c... | github_jupyter |
```
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
%matplotlib inline
```
### Device configuration
```
# device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
```
### Hyper parameters
```
# hy... | github_jupyter |
(pandas_intro)=
# Introduction
```{index} Pandas: basics
```
[Pandas](https://pandas.pydata.org/docs/) is an open source library for Python that can be used for data manipulation and analysis. If your data can be put into a spreadsheet, Pandas is exactly what you need!
Pandas is a very powerful tool with highly opt... | github_jupyter |
### train
```
import multiprocessing
import threading
import tensorflow as tf
from agent.access import Access
from agent.main import Agent
NUMS_CPU = multiprocessing.cpu_count()
state_size = 58
batch_size = 50
action_size = 3
max_episodes = 1
GD = {}
class Worker(Agent):
def __init__(self, name, access, batch_size... | github_jupyter |
# String Formatting
String formatting lets you inject items into a string rather than trying to chain items together using commas or string concatenation. As a quick comparison, consider:
player = 'Thomas'
points = 33
'Last night, '+player+' scored '+str(points)+' points.' # concatenation
f... | github_jupyter |
# The Exponential Distribution and the Poisson Process
## Introduction
One simplifying assumption that is often made is to assume that certain $r.v.\DeclareMathOperator*{\argmin}{argmin}
\DeclareMathOperator*{\argmax}{argmax}
\DeclareMathOperator*{\plim}{plim}
\newcommand{\using}[1]{\stackrel{\mathrm{#1}}{=}}
\newcomm... | github_jupyter |
# Nonlinear Equations
We want to find a root of the nonlinear function $f$ using different methods.
1. Bisection method
2. Newton method
3. Chord method
4. Secant method
5. Fixed point iterations
```
%matplotlib inline
from numpy import *
from matplotlib.pyplot import *
import sympy as sym
t = sym.symbols('t')
f_sy... | github_jupyter |
# Example: Human segmentation with TransUnet and transfer learning from ImageNet-trained VGG16 model
```
import numpy as np
from glob import glob
import tensorflow as tf
from PIL import Image
import matplotlib.pyplot as plt
from tensorflow import keras
from keras.models import load_model
from keras_unet_collection imp... | github_jupyter |
# Introduction to Modeling Libraries
```
import numpy as np
import pandas as pd
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_rows = 20
np.set_printoptions(precision=4, suppress=True)
```
## Interfacing Be... | github_jupyter |
# Neural Network
**Learning Objectives:**
* Use the `DNNRegressor` class in TensorFlow to predict median housing price
The data is based on 1990 census data from California. This data is at the city block level, so these features reflect the total number of rooms in that block, or the total number of people who liv... | github_jupyter |
# Heat equation
Numerical resolution of the one-dimensional heat equation:
$$
\alpha \frac{\partial^2 p}{\partial x^2} = \frac{\partial^2 p}{\partial t}
$$
with given boundary conditions in the ending points of a line.
```
#We'll need these libraries
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolk... | github_jupyter |
# End-to-End Machine Leanrning Project
In this chapter you will work through an example project end to end, pretending to be a recently hired data scientist at a real estate company. Here are the main steps you will go through:
1. Look at the big picture
2. Get the data
3. Discover and visualize the data to gain insig... | github_jupyter |
# Relativistic Kinematics Tutorial
## Brief Intro to Special Relativity
Before talking about relativisic kinematics, let's briefly run through Einstein's theories of Special and General Relavitity. Einstein's theory of special relativity was derived from the following two postulates:
* Postulate 1: The laws of physi... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# 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 agreed to in writi... | github_jupyter |
```
top_directory = '/Users/iaincarmichael/Dropbox/Research/law/law-net/'
from __future__ import division
import os
import sys
import time
from math import *
import copy
import cPickle as pickle
# data
import numpy as np
import pandas as pd
# viz
import matplotlib.pyplot as plt
# graph
import igraph as ig
# NLP... | github_jupyter |
# Determining the difference in variant calling in human-only samples `004` and `005`
**Gregory Way 2018**
Samples `004` and `005` are human tumors.
They were previously included in the entire `disambiguate` pipeline, where the WES reads were aligned to both human and mouse genomes.
In the pipeline, all WES reads ar... | github_jupyter |
<a href="https://colab.research.google.com/github/jwkanggist/EverybodyTensorflow2.0/blob/master/lab24_basic_bilstm_timepredict_tf2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# LAB24: Basic BiLSTM to Predict Time Series
- Train a basic BiLSTM to... | github_jupyter |
```
import open3d as o3d
import numpy as np
import os
import sys
# monkey patches visualization and provides helpers to load geometries
sys.path.append('..')
import open3d_tutorial as o3dtut
# change to True if you want to interact with the visualization windows
o3dtut.interactive = not "CI" in os.environ
```
# Multi... | github_jupyter |
# Coupling a Landlab groundwater with a Mesa agent-based model
This notebook shows a toy example of how one might couple a simple groundwater model (Landlab's `GroundwaterDupuitPercolator`, by [Litwin et al. (2020)](https://joss.theoj.org/papers/10.21105/joss.01935)) with an agent-based model (ABM) written using the [... | github_jupyter |
```
import numpy as np
import torch
import random
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import os,sys
opj = os.path.join
from tqdm import tqdm
# import acd
from random import randint
from copy import deepcopy
import pickle as pkl
import argparse
sys.path.append('../../lib/disentangling-vae')
import m... | github_jupyter |
## Compressing Word Embeddings
Downloadable version of GloVe embedding (with fallback source).
Then require two main sections :
* Lloyd embedding generation
* Sparsified embedding generation
and then saving of the created embeddings to ```.hkl``` files.
### Download Source Embedding(s)
The following needs to... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from metrics import CorefEvaluator
from document import Document
import json
import os
from ClEval import ClEval, print_clusters
from datetime import datetime
def get_timestamp():
return str(datetime.timestamp(datetime.now())).split('.')[0]
get_timestamp()
```
# Dummy text
... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
#run
import os
import re
import gc
import json
import glob
import math
import time
import torch
import os,sys
import random
import string
import pickle
import logging
import itertools
import unicodedata
import torch.nn as nn
from fastai.imports import *
... | github_jupyter |
# Joint TV for multi-contrast MR
This demonstration shows how to do a synergistic reconstruction of two MR images with different contrast. Both MR images show the same underlying anatomy but of course with different contrast. In order to make use of this similarity a joint total variation (TV) operator is used as a reg... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/get_image_resolution.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" h... | github_jupyter |
```
%matplotlib inline
import importlib
importlib.reload(RooFitMP_analysis)
import RooFitMP_analysis
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
from RooFitMP_analysis import *
df_split_timings_1538069 = build_comb_df_split_timing_info('../rootbench/1538069.burrell.nikhef.nl.out... | github_jupyter |
## Importing UK Postcodes into Amazon Lex to create a custom slot
This is a sample notebook that shows how to use pandas together the AWS Python SDK, boto3, to process a publicly available postcode file, sample it and create/update a custom slot type in Amazon Lex using the sample to train for slot recognition.
I am... | github_jupyter |
<a href="https://colab.research.google.com/github/Het-Shah/Meme-Classification/blob/master/nnfl_proj_v1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!nvidia-smi
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name !... | github_jupyter |
# Malware Classification
**Data taken from**<br>
https://github.com/Te-k/malware-classification
Here is the plan that we will follow :
- Extract as many features as we can from binaries to have a good training data set. The features have to be integers or floats to be usable by the algorithms
- Identify the best f... | github_jupyter |
# Welcome to PySyft
The goal of this notebook is to provide step by step explanation of the internal workings of PySyft for developers and have working examples of the API to play with.
**Note:** You should be able to run these without any issues. This notebook will be automatically run by CI and flagged if it fails.... | github_jupyter |
```
# Uncomment and run this cell if you're on Colab or Kaggle
# !git clone https://github.com/nlp-with-transformers/notebooks.git
# %cd notebooks
# from install import *
# install_requirements()
#hide
from utils import *
setup_chapter()
```
# Multilingual Named Entity Recognition
## The Dataset
```
#id jeff-dean-ne... | github_jupyter |
# Lesson 1 - FastAI
## New to ML? Don't know where to start?
Machine learning may seem complex at first, given the math, background understanding, and code involved. However, if you truly want to learn, the best place to start is by building and messing around with a model. FastiAI makes it super easy to create and m... | github_jupyter |
# Optical Flow
Optical flow tracks objects by looking at where the *same* points have moved from one image frame to the next. Let's load in a few example frames of a pacman-like face moving to the right and down and see how optical flow finds **motion vectors** that describe the motion of the face!
As usual, let's fi... | github_jupyter |
# 1. Import Library
```
from keras.datasets import cifar10
import numpy as np
np.random.seed(10)
```
# 資料準備
```
(x_img_train,y_label_train),(x_img_test,y_label_test)=cifar10.load_data()
print("train data:",'images:',x_img_train.shape,
" labels:",y_label_train.shape)
print("test data:",'images:',x_img_test.sh... | github_jupyter |
# Detecting and examining gender bias in the MIND dataset
The primary goal of this project is to build metrics of bias (here focusing on gender bias).
Author: <b>Jamell Dacon</b> (daconjam@msu.edu)
```
import pandas as pd
import sys
import matplotlib.pyplot as plt
%matplotlib inline
import nltk
from nltk.tokenize... | github_jupyter |
# Using Interrupts and asyncio for Buttons and Switches
This notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created for each input device and coroutines used to process the results. To demonstrate, we recreate the flashing LEDs example in the ... | github_jupyter |
# The number of cats
You are working on a natural language processing project to determine what makes great writers so great. Your current hypothesis is that great writers talk about cats a lot. To prove it, you want to count the number of times the word "cat" appears in "Alice's Adventures in Wonderland" by Lewis Carr... | github_jupyter |
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