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# <img src="https://img.icons8.com/bubbles/100/000000/3d-glasses.png" style="height:50px;display:inline"> EE 046746 - Technion - Computer Vision
#### Elias Nehme
## Tutorial 12 - Introduction to 3D Deep Learning
---
<img src="./assets/tut_09_teaser0.gif" style="width:800px">
* <a href="https://towardsdatascience.com... | github_jupyter |
# Classification with Neural Decision Forests
**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>
**Date created:** 2021/01/15<br>
**Last modified:** 2021/01/15<br>
**Description:** How to train differentiable decision trees for end-to-end learning in deep neural networks.
## Introduc... | github_jupyter |
```
import figure_traub_eigensources as fte
import numpy as np
import h5py as h5
import scipy
import matplotlib.pyplot as plt
from traub_data_kcsd_column_figure import (prepare_electrodes, prepare_pots,
set_axis)
import os
def plot_eigensources(k, v, start=0, stop=6):
letters = [... | github_jupyter |
```
# %matplotlib inline
%matplotlib notebook
from __future__ import print_function ## Force python3-like printing
try:
from importlib import reload
except:
pass
from matplotlib import pyplot as plt
import os
import warnings
import numpy as np
from astropy.table import Table
import pycoco as pcc
reload(pc... | 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 |
<a href="https://colab.research.google.com/github/deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes/blob/main/notebooks/03_Batch_Prediction_Performance.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Outline
1. Upload t... | github_jupyter |
Following on from [Guide to the Sequential Model](https://keras.io/getting-started/sequential-model-guide/)
10 May 2017 - WH Nixalo
### Getting started with the Keras Sequential model
The ```Sequential``` model is a linear stack of layers.
You can create a ```Sequential``` model by passing a list of layer instances... | github_jupyter |
##### Copyright 2020 The Cirq Developers
```
#@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 agre... | github_jupyter |
```
#get deep learning basics
import tensorflow as tf
from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
GPT2 = TFGPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
# settings
#for reproducability
SEED = 34
tf.random.set_... | github_jupyter |
```
"""
Created on Sun Feb 14 21:01:54 2016
@author: Walter Martins-Filho
e-mail: walter at on.br
waltersmartinsf at gmail.com
"""
#******************************************************************************
#Main Goal: include the time_info in the header of the images.
#************************************... | github_jupyter |
# ETL Processes
```
import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *
conn = psycopg2.connect("host=127.0.0.1 dbname=postgres user=huiren password=1234")
cur = conn.cursor()
def get_files(filepath):
all_files = []
for root, dirs, files in os.walk(filepath):
files = glo... | github_jupyter |
# Damaged Properties
```
import pandas as pd
import numpy as np
df = pd.read_csv('../../data/raw/Damaged_Property.csv')
df.head()
df.dtypes
df.info()
```
## Rename columns
```
df.rename(columns={'Latitude': 'Y',
'Longitude': 'X'}, inplace=True)
df.head()
```
## Empty values
```
for col in df:
... | github_jupyter |
# Qcamp - Terra
## IBMQ
### Donny Greenberg, Kevin Krsulich and Thomas Alexander
# Gameplan
* Basics
* What is Terra?
* Teleportation
* QPE a few ways
* Browsing device info
* Tips and t... | github_jupyter |
# Data Visualization in Python
## Introduction
In this module, you will learn to quickly and flexibly make a wide series of visualizations for exploratory data analysis and communicating to your audience. This module contains a practical introduction to data visualization in Python and covers important rules that any... | github_jupyter |
# Lesson on Pandas 25 October 2017
```
import pandas as pd
surveys_df = pd.read_csv("surveys.csv")
type(surveys_df)
a = 67
type(a)
surveys_df.dtypes
column_names = list(surveys_df.columns)
column_names
surveys_df.head(6)
surveys_df.shape
pd.unique(surveys_df['species_id'])
plot_names = pd.unique(surveys_df['plot_id'])... | github_jupyter |
```
from tqdm import tqdm
import re
def cleaning(string):
string = string.replace('\n', ' ').replace('\t', ' ')
string = re.sub(r'[ ]+', ' ', string).strip()
return string
import tensorflow as tf
import tensorflow_datasets as tfds
from t5.data import preprocessors as prep
import functools
import t5
import ... | github_jupyter |
# Multilayer Neural Networks in TensorFlow
### Goals:
- Auto-differentiation: the basics of `TensorFlow`
### Dataset:
- Similar as first Lab - Digits: 10 class handwritten digits
- http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits
```
%matplotlib inline ... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_5_yolo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 6: Convolutional Neural N... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import tensorview as tv
def simple_mnist(batch_num=1000, batch_size=32, image_shape=(28,28,1)):
image = tf.keras.Input(shape=image_shape)
x = tf.keras.layers.Conv2D(64, 3)(image)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.ReLU()(x)
... | github_jupyter |
```
import numpy as np
import pandas as pd
from tqdm import tqdm
trainData = open('../../../dataFinal/preprocessed_train_text.txt', 'r').readlines()
trainLabels = open('../../../dataFinal/finalTrainLabels.labels', 'r').readlines()
testData = open('../../../dataFinal/preprocessed_test_text.txt', 'r').readlines()
testLab... | github_jupyter |
<a href="https://colab.research.google.com/github/Madhav2204/LGMVIP-DataScience/blob/main/Task_9_Handwritten_equation_solver_using_CNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## **Author : Madhav Shrivastava**
Task-9 : Handwritten equation ... | github_jupyter |
# Read and view Delft3D grid, depth and enclosure files
* Move reading functionality to JulesD3D
```
%matplotlib widget
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import numpy as np
from JulesD3D.dep import Depth
from JulesD... | 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 |
# Foundations of Computational Economics #35
by Fedor Iskhakov, ANU
<img src="_static/img/dag3logo.png" style="width:256px;">
## Stochastic consumption-savings model with discretized choice
<img src="_static/img/lecture.png" style="width:64px;">
<img src="_static/img/youtube.png" style="width:65px;">
[https://you... | github_jupyter |
# LAB 4a: Creating a Sampled Dataset.
**Learning Objectives**
1. Setup up the environment
1. Sample the natality dataset to create train/eval/test sets
1. Preprocess the data in Pandas dataframe
## Introduction
In this notebook, we'll read data from BigQuery into our notebook to preprocess the data within a Panda... | github_jupyter |
# Molecular mechanisms of antibiotic resistance
```
# Housekeeping
library(ggplot2)
library(scales)
library(tidyr)
source("source.R")
# Read in data
multihit = read.table("../../data/deep_seq/multihit_nonsynonymous_variant_data.txt",
sep = "\t",
header = T)
dim(multihit... | github_jupyter |
# Algoritmizace a programování 2
## Cv.5. Zápočtový test - ukázka
Témata v 1. zápočtovém testu:
* OOP návrh entity (třída, datové členy, metody)
* Sekvenční struktury (zásobník, fronta, setříděný seznam)
* Vyhledávací algoritmy nad sekvenčními strukturami
* Řadící algoritmy nad sekvenčními strukturami
### 5.1 Model... | github_jupyter |
# How to download protein sequences from metagenomes belonging to thermal environment
User request:
```
I would like to download protein sequences from metagenomes belonging to thermal environment. Is there any way that this can be acheived.
```
```
# Requirements
!pip install requests
```
## Obtain the analysis for... | github_jupyter |
```
#import argparse
import datetime
import sys
import json
from collections import defaultdict
from pathlib import Path
from tempfile import mkdtemp
import numpy as np
import torch
from torch import optim
import models
import objectives
from utils import Logger, Timer, save_model, save_vars, unpack_data
from number... | github_jupyter |
```
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
import tensorflow as tf
from IPython.display import display, Image
from scipy import ndimage
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle
%matplotl... | github_jupyter |
# Text Mining
In diesem Notebook werden die von der Gruppe 1 bereitstellten Dokumenten aufbereitet und ein Text Mining Verfahren wird ausgeführt, sodass die Informationen dieser Dokumente in der Form einer Knowledge-Graph-Datenbank zur Gruppe 3 bereitgestellt wird.
## Import der Bibliotheken
#### Basis-Bibliotheken f... | github_jupyter |
# _Modeling of Qubit Chain_
<img src="images/line_qubits.png" alt="Qubit Chain">
The model may be illustrated using images from composer.
First image is for one step of quantum walk.
Each step uses two partitions described earlier.
For five qubits each partition includes two two-qubit gates denoted here as m1 and m2... | github_jupyter |
<style>div.container { width: 100% }</style>
<img style="float:left; vertical-align:text-bottom;" height="65" width="172" src="../assets/holoviz-logo-unstacked.svg" />
<div style="float:right; vertical-align:text-bottom;"><h2>Tutorial 8. Advanced Dashboards</h2></div>
At this point we have learned how to build intera... | github_jupyter |
## [How to leverage TensorFlow's TFRecord to train Keras model](https://www.dlology.com/blog/how-to-leverage-tensorflows-tfrecord-to-train-keras-model/)
Import packages,
realize how we import keras from tensorflow
`tensorflow.keras`
```
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras impo... | github_jupyter |
```
import numpy as np
import pandas as pd
df_movies = pd.read_csv('movies.csv')
df_links = pd.read_csv('links.csv')
df_ratings = pd.read_csv('ratings.csv')
df_tags = pd.read_csv('tags.csv')
df_movies.head()
df_ratings.head()
df_tags.head()
import math
tf = df_tags.groupby(['movieId','tag'], as_index=False, sort=False... | github_jupyter |
# Title: IP Explorer
<details>
<summary> <u>Details...</u></summary>
**Notebook Version:** 1.0<br>
**Python Version:** Python 3.7 (including Python 3.6 - AzureML)<br>
**Required Packages**: kqlmagic, msticpy, pandas, numpy, matplotlib, networkx, ipywidgets, ipython, scikit_learn, dnspython, ipwhois, foli... | github_jupyter |
# MNIST Image Classification Using LeNet
In this tutorial, we are going to walk through the logic in `lenet_mnist.py` shown below and provide step-by-step instructions.
```
!cat lenet_mnist.py
```
## Step 1: Prepare training and evaluation dataset, create FastEstimator `Pipeline`
`Pipeline` can take both data in me... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Sequential Recommender Quick Start
### Example: SLi_Rec : Adaptive User Modeling with Long and Short-Term Preferences for Personailzed Recommendation
Unlike a general recommender such as Matrix Factorization or ... | github_jupyter |
## Importing the required libraries
```
import librosa
import librosa.display
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from matplotlib.pyplot import specgram
import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embe... | github_jupyter |
```
import numpy as np
from scipy.sparse import linalg
import matplotlib.pyplot as plt
from matplotlib import rcParams
from scipy import integrate
from scipy.linalg import qr
from mpl_toolkits.mplot3d import Axes3D
plt.rcParams['figure.figsize'] = [10,10]
plt.rcParams.update({'font.size': 18})
# spatial discretizati... | github_jupyter |
# OWSLib versus Birdy
This notebook shows a side-by-side comparison of `owslib.wps.WebProcessingService` and `birdy.WPSClient`.
```
from owslib.wps import WebProcessingService
from birdy import WPSClient
url = "https://bovec.dkrz.de/ows/proxy/emu?Service=WPS&Request=GetCapabilities&Version=1.0.0"
wps = WebProcessin... | github_jupyter |
# NOAA RATPAC-B Data
-----
## Initial Data Exploration
Initial data exploration for the NOAA RATPAC-B temperature data.
```
processed_data_dir = '../data/processed'
# Imports
import calendar
from datetime import datetime, timedelta
import os
import pickle
import sys
import cartopy.crs as ccrs
import matplotlib.pyp... | github_jupyter |
# Sequential Monte Carlo with two gaussians
```
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import theano.tensor as tt
import shutil
plt.style.use('seaborn-darkgrid')
print('Running on PyMC3 v{}'.format(pm.__version__))
```
Sampling from $n$-dimensional distributions with multiple peaks wi... | github_jupyter |
```
# Install the environnement
%pip install git+https://github.com/AwePhD/NotebooksLabsessionImage.git
# Import dataset
# Can be found at https://www.kaggle.com/vishalsubbiah/pokemon-images-and-types
!rm -rf ./*
!curl -LO https://github.com/AwePhD/NotebooksLabsessionImage/raw/main/pokemon_dataset.zip
!unzip -qq pokem... | github_jupyter |
<table width = "100%">
<tr style="background-color:white;">
<!-- QWorld Logo -->
<td style="text-align:left;width:200px;">
<a href="https://qworld.net/" target="_blank"><img src="../images/QWorld.png"> </a></td>
<td style="text-align:right;vertical-align:bottom;font-size:16px;">
Prepared... | github_jupyter |
## bayespropestimation usage guide
The BayesProportionsEstimation class and its methods use a series of defaults which means that user need not provide any information other than the data for samples A and B. This notebook covers usage where a user may want to use non-default parameters.
#### Sections
##### Class B... | github_jupyter |
# **pix2pix**
---
<font size = 4>pix2pix is a deep-learning method allowing image-to-image translation from one image domain type to another image domain type. It was first published by [Isola *et al.* in 2016](https://arxiv.org/abs/1611.07004). The image transformation requires paired images for training (supervised... | github_jupyter |
# Welter issue #6
## Set up the Starfish config.yaml files and directories
### Part 2- Git-er-done
Michael Gully-Santiago
Thursday, December 17, 2015
Let's do it.
```
import warnings
warnings.filterwarnings("ignore")
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
% matplotlib inli... | github_jupyter |
# Sampling High-Dimensional Vectors
Aaron R. Voelker (January 15, 2016)
```
%pylab inline
import numpy as np
import pylab
try:
import seaborn as sns # optional; prettier graphs
except ImportError:
sns = None
import nengo
from nengolib.compat import get_activities
from nengolib.stats import ScatteredHypersphe... | github_jupyter |
```
def __Version__():
return('1.1.0')
import warnings
warnings.filterwarnings('ignore')
import ipywidgets as widgets
from IPython.display import display, clear_output
%pylab inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from PIL import Image
import datetime
import time
from ip... | github_jupyter |
##### 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 |
> **Note:** In most sessions you will be solving exercises posed in a Jupyter notebook that looks like this one. Because you are cloning a Github repository that only we can push to, you should **NEVER EDIT** any of the files you pull from Github. Instead, what you should do, is either make a new notebook and write you... | github_jupyter |
# Multi-frequency FDFD
### Introductory example
This example shows how to simulate using FDFD_MF the frequency and spatial profile conversion of an inserted waveguide mode by appropriately choosing the modulation depth and phase profiles. Reproduces the unoptimized structure in J. Wang et al., "Adjoint-based optimizat... | github_jupyter |
# Embedding and Filtering Inference
Set default input and output directories according to local paths for data
```
import os
os.environ['TRKXINPUTDIR']="/global/cfs/cdirs/m3443/data/trackml-kaggle/train_10evts"
os.environ['TRKXOUTPUTDIR']= "/global/cfs/projectdirs/m3443/usr/caditi97/iml2020/outtest"
```
Import neces... | github_jupyter |
# Hidden Markov Model Demo
A Hidden Markov Model (HMM) is one of the simpler graphical models available in _SSM_. This notebook demonstrates creating and sampling from and HMM using SSM, and fitting an HMM to synthetic data. A full treatment of HMMs is beyond the scope of this notebook, but there are many good resourc... | github_jupyter |
# Part 7 - Federated Learning with FederatedDataset
Here we introduce a new tool for using federated datasets. We have created a `FederatedDataset` class which is intended to be used like the PyTorch Dataset class, and is given to a federated data loader `FederatedDataLoader` which will iterate on it in a federated fa... | github_jupyter |
```
import numpy as np
import scipy as sp
from scipy import spatial
import matplotlib.pyplot as plt
from nltk.stem.lancaster import LancasterStemmer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import pairwis... | github_jupyter |
```
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
# Ignore warnings
imp... | github_jupyter |
# hana-ml Tutorial - Clustering
**Author: TI HDA DB HANA Core CN**
In this tutorial, we will show you how to use clustering functions in hana-ml to preprocess data and train a model with a public Iris dataset.
## Import necessary libraries and functions
```
from hana_ml.dataframe import ConnectionContext
from ha... | github_jupyter |
# 3D Transformation Matrices
---
- Author: Diego Inácio
- GitHub: [github.com/diegoinacio](https://github.com/diegoinacio)
- Notebook: [3DTransformation_Matrix.ipynb](https://github.com/diegoinacio/creative-coding-notebooks/blob/master/Computer-Graphics/3DTransformation_Matrix.ipynb)
---
Overview and application of tri... | github_jupyter |
<p>
<img src="https://s3.amazonaws.com/iotanalytics-templates/Logo.png" style="float:left;">
<h1 style="color:#1A5276;padding-left:115px;padding-bottom:0px;font-size:28px;">AWS IoT Analytics | Smart Building Energy Consumption</h1>
</p>
<p style="color:#1A5276;padding-left:90px;padding-top:0px;position:relative... | github_jupyter |
```
# Install libraries
%%capture
! pip install "flaml[ts_forecast]"
# Download the dataset
%%capture
! rm -rf *
! gdown --id 15vtwJVePVrbhzcS2gr4xY7qWOPi3Hfzo
! unzip cab.zip
! rm cab.zip
# Import libraries
%%capture
import pandas as pd
import numpy as np
from flaml import AutoML
from pathlib import Path
import ... | github_jupyter |
<a href="https://colab.research.google.com/github/Vaibhavsharma0209/Markowitz-Model/blob/master/Markowitz_Model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import quandl as q
from datetime import datetime
from pandas_data... | github_jupyter |
# 1.x to 3.x Rule Migration Guide
This guide describes changes needed for rules to run under Insights Core 3.x.
It covers the following topics:
- filtering
- decorator interfaces
- function signatures
- cluster rules
- testing
- new style specs
## Filtering
Filters are now applied to registry points or datasources in... | github_jupyter |
## Checkpoint Inspector
Loads folders of `json` checkpoints dumped by `train.lua` and visualizes training statistics.
```
import json
from scipy.misc import imread, imresize
import os
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = '... | github_jupyter |
## Questionário 21
Orientações:
- Registre suas respostas no questionário de mesmo nome no SIGAA.
- O tempo de registro das respostas no questionário será de 10 minutos. Portanto, resolva primeiro as questões e depois registre-as.
- Haverá apenas 1 (uma) tentativa de resposta.
- Submeta seu arquivo-fonte (utilizado ... | github_jupyter |
```
from google.colab import files
uploaded = files.upload()
import io
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
from sklearn import metrics
from sklearn.model_selection import StratifiedShuffleSplit
from keras.models import Sequential
from keras.layers import Dense, Dropout, L... | github_jupyter |
# Stage 1 Analysis
#### Partial rank correlation coefficient analysis (PRCC) - regression based analysis code
### Step 1: Input preparation
```
#####NIMML######
### Author: Meghna Verma
### Date : August 10, 2017
#Set it to the directory that has all the csv files obatined after converting the tsv file outptus from ... | github_jupyter |
# Spectral Clustering
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. Please check the pdf file for more details.*
In this exercise you will:
- implement the **KNN graph** and other necessary algorithms for **... | github_jupyter |
## Installation
```
$ pip install requests mapboxgl supermercado
```
```
%pylab inline
import os
import json
import random
import requests
import datetime
from io import BytesIO
import urllib.parse
from supermercado.burntiles import tile_extrema
from mapboxgl.utils import *
from mapboxgl.viz import *
token = os.e... | github_jupyter |
# **Getting Started with NETS**
**NETS** is a vanilla Deep Learning framework, made using only **NumPy**.
This project was first introduced as an assignment I made at the [University of Oslo](https://www.uio.no/studier/emner/matnat/ifi/IN5400/) and [Stanford University](http://cs231n.stanford.edu/syllabus.html)
.
Howe... | github_jupyter |
```
# importing libraries
import pandas as pd
import numpy as np
import os
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score
... | github_jupyter |
<a href="https://colab.research.google.com/github/tlamadon/pygrpfe/blob/main/docs-src/notebooks/gfe_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Discretizing Unobserved Heterogeneity: A Step-by-Step Example
Welcome to the example on u... | github_jupyter |
# Phase 3 Weighted Bagging
```
import pandas as pd
import matplotlib.pyplot as plt
from os import listdir
from os.path import isfile, join
import os
import re
import csv
import codecs
import gensim
import itertools
import numpy as np
import pandas as pd
import operator
import sys
from nltk import ngrams
from collec... | github_jupyter |
# [Module 3.2] Custom PCA Docker Image 생성 및 ECR Model 학습
이 노트뷱은 Bring Your Own Container(BYOC)를 위해서 Custom Docker Image를 생성 합니다. 이 docker image는 학습 및 추론에 사용 됩니다.
구체적으로 이 노트북은 아래와 같은 작업을 합니다.
- Custom docker image name 정의
- PCA 학습 코드를 docker container 폴더로 복사
- Dockerfile 작성
- Docker Image 빌드 및 ECR에 등록
- Docker Image에... | github_jupyter |
# Peform statistical analyses of GNSS station locations and tropospheric zenith delays
**Author**: Simran Sangha, David Bekaert - Jet Propulsion Laboratory
This notebook provides an overview of the functionality included in the **`raiderStats.py`** program. Specifically, we outline examples on how to perform basic st... | github_jupyter |
# Annealing with `qubovert`
*qubovert* must be pip installed.
Import `qubovert`.
```
import qubovert as qv
```
In this notebook, we will review some basics of the annealing and simulation functionality provided by `qubovert`. Let's look at everything in the simulation (`sim`) library.
```
print(qv.sim.__all__)
```... | github_jupyter |
# Part 4 - Application
The main idea, which I'll cover in the post focuses on taking our predictions and using the "probability" distriubtions generated by the model in the wOBA calculation - rather than evaluating based on result entirely, we'll evaluate based on likelihoood of possible results.
The heavy lifting f... | github_jupyter |
# Linked List
### Each element is called Node and that stores two things one is the data and one the refernce to next node
**Head** stores the reference to the very first node, after knowing that we can travel through the complete linkedList
Every **node** will have a data and a ... | github_jupyter |
```
#akyork
#written to analyze the 16 HMO glycans provided by Ben
import sys
# add the path to glycompare into the sys PATH
sys.path.insert(0, '/Users/apple/PycharmProjects/GlyCompare/glycompare/')
import __init__
import json_utility
from glypy.io import glycoct, iupac
import extract_motif
import customize_motif_vec... | github_jupyter |
# Part D: Comparison of toroidal meniscus models with different profile shapes
## Introduction
So far all the capillary entry pressures for the percoaltion examples were calculated using the ``Standard`` physics model which is the ``Washburn`` model for straight walled capillary tubes. This has been shown to be a bad... | github_jupyter |
```
import astropy.coordinates as coord
import astropy.units as u
from astropy.table import Table, join, vstack
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
from astropy.io import ascii
from scipy.interpolate import interp1d
from scipy.stats import binned_statistic
im... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
## AKS Load Testing
Once a model has been deployed to production it is important to ensure that the deployment target can support the expected load (number of users and expected response speed). This is critical for providing r... | github_jupyter |
# Orchestration of prediction experiments with sktime
* Evaluate the predictive performance one or more strategies on one or more datasets
[Github weblink](https://github.com/alan-turing-institute/sktime/blob/master/examples/experiment_orchestration.ipynb)
```
from sktime.experiments.orchestrator import Orchestrator... | github_jupyter |
```
from google.colab import files
uploaded = files.upload()
!unzip df.zip
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf,plot_pa... | github_jupyter |
# Setup for Screen Reader
```
!pip install colab-a11y-utils
!pip install audio-plot-lib
import audio_plot_lib as apl
from colab_a11y_utils import set_sound_notifications
set_sound_notifications()
```
# From Python Data Science Handbook
The original is at the following URL, with the text unchanged and only some of t... | github_jupyter |
```
Chapter 2 – End-to-end Machine Learning project
Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.
This notebook contains all the sample code and solutions to the exercices in chapter 2.
Note: You may f... | github_jupyter |
# <center>Using Optimization in Simulating 2-D Wildland Fire Behavior </center>
<center>by Diane Wang</center>
---
# Optimization methods used in fire behavior simulation
Fire behavior refers to the gross characteristics of fire, including fireline intensity (power per unit length of the flaming front), spread rate,... | github_jupyter |
This exercise is to test your understanding of Python basics.
Answer the questions and complete the tasks outlined below; use the specific method described if applicable. In order to get complete points on your homework assigment you have to a) complete this notebook, b) based on your results answer the multiple choic... | github_jupyter |
# Aggregating and downscaling timeseries data
The **pyam** package offers many tools to facilitate processing of scenario data.
In this notebook, we illustrate methods to aggregate and downscale timeseries data of an `IamDataFrame` across regions and sectors, as well as checking consistency of given data along these d... | github_jupyter |
# Using PyTorch with TensorRT through ONNX:
TensorRT is a great way to take a trained PyTorch model and optimize it to run more efficiently during inference on an NVIDIA GPU.
One approach to convert a PyTorch model to TensorRT is to export a PyTorch model to ONNX (an open format exchange for deep learning models) and... | github_jupyter |
```
from bs4 import BeautifulSoup as soup
import requests
url= "https://www.tutorialspoint.com/index.htm" # jere we are storing the URL address
print("The URL choosen is", url)
req = requests.get(url) # using request package we are obtaining the url address and stroing the request in req
print(req)
soup_var = soup(re... | github_jupyter |
```
import pathlib
import tensorflow as tf
import tensorflow.keras.backend as K
import skimage
import imageio
import numpy as np
import matplotlib.pyplot as plt
# Makes it so any changes in pymedphys is automatically
# propagated into the notebook without needing a kernel reset.
from IPython.lib.deepreload import rel... | github_jupyter |
# Magenta魔改记-4：Melody RNN的数据表示和tfrecord读取
本文介绍Melody RNN数据表示的具体形式，以及如何读取Melody RNN转换后保存的.tfrecord文件。
###### Magenta version:1.1.1
# 数据表示和tfrecord读取
首先，我们以一首最简单的歌曲《小星星》为例。
在一切之前，导入我们需要的库：
```
import tensorflow as tf
import magenta as mgt
import numpy as np
#加这行是因为jupyter notebook对tf.app.flags.... | github_jupyter |
# 1. Introduction to Numpy
Numpy is a Library in python that specializes in dealing with multidimensional Arrays. The cool features of Numpy are
* **Automatic Checking :** Numpy ndArrays automatically check the consistancy of data. For instance, it is not possible to have 1st row with 2 elements and 2nd row with 3 elem... | github_jupyter |
# WBIC
This notebook gives a tutorial on how to use Watanabe-Bayesian information criterion (WBIC) for feature selection. The WBIC is an information criterion. Similarly to other criteria (AIC, BIC, DIC) the WBIC endeavors to find the most parsimonious model, i.e., the model that balances fit with complexity. In other... | github_jupyter |
# Denoising Autoencoder
Sticking with the MNIST dataset, let's add noise to our data and see if we can define and train an autoencoder to _de_-noise the images.
<img src='https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/autoencoder/denoising-autoencoder/notebook_ims/autoencoder_denoise.png' w... | github_jupyter |
## 1. load and convert data into common training format
```
import pandas
from tqdm.auto import tqdm
import spacy.gold
ROOT = '../../data/kaggle-ru/'
train_data = pandas.read_csv(ROOT+'ru_train.csv')
train_data[['before', 'after']] = train_data[['before', 'after']].astype(str)
train_data.head(15)
fix = train_data['cla... | github_jupyter |
```
%%writefile train.py
import argparse
import os
import lightgbm as lgb
import pandas as pd
from azureml.core import Run
import joblib
from sklearn.feature_extraction import text
from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
from azure_utils.machine_learning.item_selector import ItemSelector
i... | github_jupyter |
<a href="https://colab.research.google.com/github/johnhallman/tigercontrol/blob/tutorials/tutorials/notebooks/Environments.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Environments: Load Different Tasks
```
!git clone https://github.com/johnha... | github_jupyter |
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