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# Monkey Bread
```
from SpectralCV import ecog_pipe as ep
import numpy as np
import scipy as sp
import scipy.io as io
import scipy.signal as sig
import math as math
import random
from scipy import integrate
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
plt.style.use('seabo... | github_jupyter |
```
import sklearn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
from sklearn import svm
from sklearn.metrics import mean_squared_error, accura... | github_jupyter |
<div class="alert alert-block alert-info">
Section of the book chapter: <b>5.2.2 Active Learning</b>
</div>
# 4. Active learning
**Table of Contents**
* [4.1 Active Learning Setup](#4.1-Active-Learning-Setup)
* [4.2 Initial Estimation](#4.2-Initial-Estimation)
* [4.3 Including Active Learning](#4.3-Including-Active-... | github_jupyter |
```
import os
import random
import torch
import numpy as np
from torch.nn import functional as F
dataset_dir = "./family/"
all_trip_file = os.path.join(dataset_dir, "all.txt")
relations_file = os.path.join(dataset_dir, "relations.txt")
entities_file = os.path.join(dataset_dir, "entities.txt")
def read_xxx_to_id(file_p... | github_jupyter |
##### Copyright 2021 The TF-Agents 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 a... | github_jupyter |
<a href="https://colab.research.google.com/github/henrywoo/MyML/blob/master/Copy_of_nlu_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### Copyright 2018 Google LLC.
```
# Licensed under the Apache License, Version 2.0 (the "License");
# you ma... | github_jupyter |
<a href="https://colab.research.google.com/github/AryanMethil/Brain_Tumor_Detection/blob/master/constants.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Folders Details :
**brain_tumor_dataset => Input Dataset which also contains the test dataset... | github_jupyter |
```
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import os
from scipy.misc import imread,imresize
from random import shuffle
from sklearn.preprocessing import LabelEncoder
tf.__version__
```
Make sure you download this data and extract in the same directory,
https://drive.google.c... | github_jupyter |
```
import sys
sys.path.append("/remote-home/xtzhang/CTC/CTC2021/SpecialEdition")
import os
import random
import time
import logging
import argparse
from dataclasses import dataclass, field
from typing import Optional,Dict, Union, Any, Tuple, List
import numpy as np
import datasets
import torch
import torch.nn as nn
... | github_jupyter |
# Notebook-10: Wrapping Up (A Matter of Style)
### Lesson Content
- Style
- Why style matters
- Python style
- Why???
- Why did I enter this world of pain?
- Where am I going?
Welcome to the ninth, and currently _last_, Code Camp notebook! In this lesson we'll cover a number of things that don't fit... | github_jupyter |
<img src="imgs/dh_logo.png" align="right" width="50%">
# Aula 3.5.2 - Clustering
Fala galera! Tudo bem? Hoje continuaremos a aula de clustering/unsupervised learning. Na aula passada, vimos os conceitos básicos de clustering, bem como o algoritmo mais simples para a tarefa (simples, porém muito eficiente em vários c... | github_jupyter |
# Data Processing - Overview
## Pre-requisites and Module Introduction
Let us understand prerequisites before getting into the module.
* Good understanding of Data Processing using Python.
* Data Processing Life Cycle
* Reading Data from files
* Processing Data using APIs
* Writing Processed Data back to files
* W... | github_jupyter |
# Python (EPAM, 2020), lecture 11
# Section 0. Metaclasses one more time
```python
class DisallowPublicClassAttributes(type): # is a metaclass
def __new__(cls, name, bases, dct):
cls_instance = super().__new__(cls, name, bases, dct)
if any([not key.startswith("_") for key in dct.keys()]):
... | github_jupyter |
# Example Data
```
import os
def mkfile(filename, body=None):
with open(filename, 'w') as f:
f.write(body or filename)
return
def make_example_dir(top):
if not os.path.exists(top):
os.mkdir(top)
curdir = os.getcwd()
os.chdir(top)
os.mkdir('dir1')
os.mkdir('dir2')
m... | github_jupyter |
# Plots
> Plotting is everything!
Here we provide the code to process the results as they come from the examples in the benchmarking and transfer learning notebooks. The plots have the same format as the ones in the paper.
```
from bounce.hamiltonian import XXHamiltonian
from bounce.utils import save_benchmark, load_... | github_jupyter |
```
from math import *
seed = (0,0)
length = 100
minlength = 10
ratio = sqrt(2)
vertical = True
queue = [(seed,length,vertical)]
def makelines(lines,queue,ratio):
pt,length, vertical = queue.pop(0)
print("length:",length)
if length>minlength:
if vertical:
A = (pt[0],pt[1]+length/2.0)
... | github_jupyter |
```
// #r ".\binaries\bossspad.dll"
// #r ".\binaries\XNSEC.dll"
#r "C:\BoSSS\experimental\public\src\L4-application\BoSSSpad\bin\Release\net5.0\bossspad.dll"
#r "C:\BoSSS\experimental\public\src\L4-application\BoSSSpad\bin\Release\net5.0\XNSEC.dll"
// #r "C:\BoSSS\experimental\public\src\L4-application\BoSSSpad\bin\R... | github_jupyter |
<a href="https://colab.research.google.com/github/simecek/dspracticum2020/blob/master/lecture_02/01_one_neuron_and_MPG_dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pyth... | github_jupyter |
📍 **Project Title** : Digit Recognizer Project
📍 **Aim of the Project** : This project will classify different digits and predict accordingly.
📍 **Dataset** : https://www.kaggle.com/c/digit-recognizer/data
📍 **Libraries used :** ```Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, Keras```
***********************... | github_jupyter |
# Experiment: Presidential Campaigns Ads Dataset - Feature Extraction -
This notebook shows how to use cloud services using REST API to convert audio to text, to analyze the extracted text and frames contents. Using the files previously collected (see Experiment: Presidential Campaigns Ads Dataset - Data Collection -... | github_jupyter |
```
import time
from collections import OrderedDict, namedtuple
import numpy as np
from numpy import linspace
from pandas import DataFrame
from scipy.integrate import odeint, ode
import ggplot as gg
%autosave 600
HAS_SOLVEIVP = False
try:
from scipy.integrate import solve_ivp
HAS_SOLVEIVP = True
except:
pas... | github_jupyter |
```
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import random
import os
import copy
import json
import scipy
# Detectron colors
_COLORS = np.array([
0.000, 0.447, 0.741,
0.850, 0.325, 0.098,
0.929, 0.694, 0.125,
0.494, 0.184, 0.556,
0.466, 0.674, 0.188
]... | github_jupyter |
# "Statistical Thinking in Python (Part 1)"
> "Building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you."
- toc: true
- comments: true
- author: Victor Omondi
- categories: [statistical-thinking, eda, data-science]
- image: images/statistic... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../scripts')
import numpy as np
import os, h5py
import pandas as pd
import variant_effect
# read df and add strand
all_dfs = []
cagi_data = '../data/CAGI/'
combined_filename = '../data/combined_cagi.bed'
for filename in os.listdir(cagi_data):
prefix... | github_jupyter |
# TorchDyn Quickstart
**TorchDyn is the toolkit for continuous models in PyTorch. Play with state-of-the-art architectures or use its powerful libraries to create your own.**
Central to the `torchdyn` approach are continuous neural networks, where *width*, *depth* (or both) are taken to their infinite limit. On the ... | github_jupyter |
```
import os
from enum import Enum
import gzip
import time
import numpy as np
from scipy.sparse import dok_matrix, csr_matrix
import tensorflow as tf
# Attalos Imports
import attalos.util.log.log as l
from attalos.dataset.dataset import Dataset
from attalos.evaluation.evaluation import Evaluation
# Local models
fro... | github_jupyter |
# Setup
```
# Python 3 compatability
from __future__ import division, print_function
# system functions that are always useful to have
import time, sys, os
# basic numeric setup
import numpy as np
import math
from numpy import linalg
import scipy
from scipy import stats
# plotting
import matplotlib
from matplotlib ... | github_jupyter |
<h1> Polynomial Regression
This cell is regarding polynomial regression, first we will grab the dataset and clean it a little bit.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_m... | github_jupyter |
# Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with Numpy, we'll be using [TFLearn](http://tflearn.org/), a high-level library built on top of TensorFlow. TFLearn makes it simpler... | github_jupyter |
### Cleaning data associated with bills: utterances, summaries; so they are ready for input to pointer-gen model - this is the new cleaning method implementation
There are 6541 BIDs which overlap between the utterances and summaries datasets (using all the summary data). There are 359 instances in which the summaries ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Configuration
_**Setting up your Azure Machine Learning services workspace and configuring your n... | github_jupyter |
# End-to-End Incremental Training Image Classification Example
1. [Introduction](#Introduction)
2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)
1. [Permissions and environment variables](#Permissions-and-environment-variables)
2. [Prepare the data](#Prepare-the-data)
3. [Training the model](#Tr... | github_jupyter |
# Variable transformers : YeoJohnsonTransformer
The YeoJohnsonTransformer() applies the Yeo-Johnson transformation to the
numerical variables.
**For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:**
Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing
Data as... | github_jupyter |
## Preprocessing Tabular Data
The purpose of this notebook is to demonstrate how to preprocess tabular data for training a machine learning model via Amazon SageMaker. In this notebook we focus on preprocessing our tabular data and in a sequel notebook, [training_model_on_tabular_data.ipynb](training_model_on_tabular_... | github_jupyter |
```
from keras.datasets import fashion_mnist
(train_X,train_Y), (test_X,test_Y) = fashion_mnist.load_data()
import numpy as np
from keras.utils import to_categorical
import matplotlib.pyplot as plt
%matplotlib inline
print('Training data shape: ', train_X.shape, train_Y.shape)
print('Testing data shape: ', test_X.shap... | github_jupyter |
# All
## Set Up
```
print("Installing dependencies...")
%tensorflow_version 2.x
!pip install -q t5
import functools
import os
import time
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
import t5
```
## Set UP TPU ... | github_jupyter |
# Loops and Conditionals
---
## Loops
- for
- while
**looping through list**
*lets create a list*
```
lst = [1, 3, 4]
for item in lst:
print(item)
print('='*3)
```
**Note: blocks**
```
print('Length of the list: ', len(lst))
for item in lst:
print(item)
print('='*3)
print('Finished......')
```
... | github_jupyter |
```
from datascience import *
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
from utils import *
plt.style.use('seaborn-muted')
from matplotlib import patches
import csaps
import warnings
warnings.filterwarnings("ignore")
```
# An Empirical Example from EEP 147
Let's take a ... | github_jupyter |
# Clustering of Social Groups Using Census Demographic Variables
#### Purpose of this notebook
- 1) Use scikit-learn K-Means to create social groups across Toronto, Vancouver, Montreal
#### Data Sources
- Census Variables: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/details/download-telecharger/com... | github_jupyter |
Intro To Python
=====
In this notebook, we will explore basic Python:
- data types, including dictionaries
- functions
- loops
Please note that we are using Python 3.
(__NOT__ Python 2! Python 2 has some different functions and syntax)
```
# Let's make sure we are using Python 3
import sys
print(sys.version[0]... | github_jupyter |
<a href="https://colab.research.google.com/gist/adaamko/0161526d638e1877f7b649b3fff8f3de/deep-learning-practical-lesson.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Natural Language Processing and Information Extraction
## Deep learning - pract... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 11: Natural Language Processing and Speech Recognition**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more i... | github_jupyter |
# Managing offline map areas
With ArcGIS you can take your web maps and layers offline in field apps to continue work in places with limited or no connectivity. Using [ArcGIS Runtime SDKs](https://developers.arcgis.com/features/offline/), you can bu... | github_jupyter |
# Using nbconvert as a library
In this notebook, you will be introduced to the programmatic API of nbconvert and how it can be used in various contexts.
A great [blog post](http://jakevdp.github.io/blog/2013/04/15/code-golf-in-python-sudoku/) by [@jakevdp](https://github.com/jakevdp) will be used to demonstrate. Th... | github_jupyter |
## Ireland Covid-19 datasets
* https://data.gov.ie/dataset?q=covid
* https://www.hpsc.ie/a-z/respiratory/coronavirus/novelcoronavirus/casesinireland/epidemiologyofcovid-19inireland/
* https://covid19ireland-geohive.hub.arcgis.com/
```
import pandas as pd
import pylab as plt
import numpy as np
import seaborn as sns
im... | github_jupyter |
# TensorFlow Tutorial
Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Ke... | github_jupyter |
```
import numpy as np
import orqviz
import matplotlib.pyplot as plt
```
Given this "mysterious loss function named *loss_function*, what can we find out about it?
```
def loss_function(pars):
norm_of_pars = np.linalg.norm(pars, ord=2)
freq = 2
return -np.sin(freq*norm_of_pars) / (freq*norm_of_pars) + 1
... | github_jupyter |
# Training a ConvNet PyTorch
In this notebook, you'll learn how to use the powerful PyTorch framework to specify a conv net architecture and train it on the CIFAR-10 dataset.
```
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
... | github_jupyter |
# Label and feature engineering
This lab is *optional*. It demonstrates advanced SQL queries for time-series engineering. For real-world problems, this type of feature engineering code is essential. If you are pursuing a time-series project for open project week, feel free to use this code as a template.
---
Learni... | github_jupyter |
```
# para que funcione para python 2 y 3
from __future__ import division, print_function, unicode_literals
import numpy as np
import os
#salidas repetibles
np.random.seed(42)
# lindas figuras
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labels... | github_jupyter |
```
# default_exp env_wrappers
#hide
from nbdev import *
```
# env_wrappers
> Here we provide a useful set of environment wrappers.
```
%nbdev_export
import gym
import numpy as np
import torch
from typing import Optional, Union
%nbdev_export
class ToTorchWrapper(gym.Wrapper):
"""
Environment wrapper for conv... | github_jupyter |
### 1. Bias-Variance decomposition
Вспомним, что функцию потерь в задачах регрессии или классификации можно разложить на три компоненты: смещение (bias), дисперсию (variance) и шум (noise). Эти компоненты позволяют описать сложность алгоритма, альтернативно сравнению ошибок на тренировочной и тестовой выборках. Хотя т... | github_jupyter |
```
%matplotlib notebook
import tensorflow as tf
import tensorflow.keras as K
from tensorflow.keras.losses import categorical_crossentropy
import numpy as np
import matplotlib.pyplot as plt
import cv2
import pandas as pd
import sys
sys.path
sys.path.append("../../models/classification")
from models import ResNet, Al... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import os
import random
import copy
import keras
from keras.layers import Input, Dense, Conv2D, Dropout, Flatten, Reshape
from keras.optimizers import RMSprop, Adam
from keras.models import Model
from keras.models import Sequential
from keras.callbacks import LambdaCallba... | github_jupyter |
# Facies classification using machine learning techniques
The ideas of
<a href="https://home.deib.polimi.it/bestagini/">Paolo Bestagini's</a> "Try 2", <a href="https://github.com/ar4">Alan Richardson's</a> "Try 2",
<a href="https://github.com/dalide">Dalide's</a> "Try 6", augmented, by Dimitrios Oikonomou and Eirik L... | github_jupyter |
## Setup
We'll be using a Python library that helps us to parse markup languages like HTML and XML called BeautifulSoup. We will be using an additional library called `lxml`, which helps BeautifulSoup (aka BS4) to search and build XML. It is possible that you may need to do an extra step to install `lxml` if you have ... | github_jupyter |
<a href="https://colab.research.google.com/github/ashishpatel26/100-Days-Of-ML-Code/blob/master/Tensorflow_Basic_Chapter_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Basic Perceptron
```
import tensorflow as tf
print(tf.__version__)
W = tf.... | github_jupyter |
```
import azureml.core
from azureml.core import Workspace
ws = Workspace.from_config()
# Get the default datastore
default_ds = ws.get_default_datastore()
default_ds.upload_files(files=['./Data/borrower.csv', './Data/loan.csv'], # Upload the diabetes csv files in /data
target_path='creditrisk... | github_jupyter |
```
import os, sys
from glob import glob
sys.path.append("../")
sys.path.append('/Users/hongwan/GitHub/DarkHistory/')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from scipy.interpolate import interp1d, RegularGridInterpolator
from tqdm import *
import darkhistory.physics as phy... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/gdrive')
import pandas as pd
import glob
import datetime as dt
import multiprocessing as mp
from datetime import datetime
import numpy as np
import plotly
from pandas import Series
import sys
from scipy import stats
from statsmodels.tsa.stattools import adfuller... | github_jupyter |
<img src="../../../images/qiskit_header.png" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" align="middle">
## _*Relaxation and Decoherence*_
* **Last Updated:** Feb 25, 2019
* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.1, qiskit-aer 0.2
This no... | github_jupyter |
## Universal Style Transfer
The models above are trained to work for a single style. Using these methods, in order to create a new style transfer model, you have to train the model with a wide variety of content images.
Recent work by Yijun Li et al. shows that it is possible to create a model that generalizes to unse... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Given two strings, find the longest common substring.
* [Constraints](#Constraints)
* [Test Cases](#Test-... | github_jupyter |
```
!git clone https://github.com/muhwagua/color-bert.git
!pip install transformers
import random
import re
import urllib.request
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DataCollatorForLan... | github_jupyter |
# Installing Cantera
For this notebook you will need [Cantera](http://www.cantera.org/), an open source suite of object-oriented software tools for problems involving chemical kinetics, thermodynamics, and/or transport processes.
Fortunately a helpful chap named Bryan Weber has made Anaconda packages, so to install you... | github_jupyter |
## Image Classification `CNN` + `Tansfare Learning`
> Classifying image from our own dataset with `10` classes.
### Imports
```
import tensorflow as tf
from tensorflow import keras
import numpy as np
import os, random
import matplotlib.pyplot as plt
import shutil
from tensorflow.keras.preprocessing.image import Image... | github_jupyter |
# Using `scipy.integrate`
## Authors
Zach Pace, Lia Corrales, Stephanie T. Douglas
## Learning Goals
* perform numerical integration in the `astropy` and scientific python context
* trapezoidal approximation
* gaussian quadrature
* use `astropy`'s built-in black-body curves
* understand how `astropy`'s units intera... | github_jupyter |
# Prevalencia
Vamos a analizar el influjo de la prevalencia, en el devenir de la enfermedad
<div class="alert alert-block alert-info">
En epidemiología, se denomina <strong>prevalencia</strong> a la proporción de individuos de un grupo o una población (en medicina, persona), que presentan una característica o evento ... | github_jupyter |
# Entity Extraction from old-style SciSpacy NER Models
These models identify the entity span in an input sentence, but don't attempt to separately link to an external taxonomy. The following variations are possible here. Replace the `MODEL_NAME, MODEL_ALIAS` line in the cell below and repeat run to extract named entit... | github_jupyter |
Welcome to a series on programming quantum computers. There's no shortage of hype around quantum computing on the internet, but I am going to still outline the propositions made by quantum computing in general, as well as how this pertains to us and programmers who intend to work with quantum computers, which we will b... | github_jupyter |
# TensorBoard with Fashion MNIST
In this week's exercise you will train a convolutional neural network to classify images of the Fashion MNIST dataset and you will use TensorBoard to explore how it's confusion matrix evolves over time.
## Setup
```
# Load the TensorBoard notebook extension.
%load_ext tensorboard
imp... | github_jupyter |
```
from causaleffect import *
'''Define G (example in section 3.3 of paper "Identifying Causal Effects with the R Package causaleffect")'''
G1 = createGraph(["X<->Y", "Z->Y", "X->Z", "W->X", "W->Z"])
#plotGraph(G1)
'''Define G2 (example Figure 1a of paper "Identification of Joint Interventional
Distributions in Recu... | github_jupyter |
### Mutable Sequences
When dealing with mutable sequences, we have a few more things we can do - essentially adding, removing and replacing elements in the sequence.
This **mutates** the sequence. The sequence's memory address has not changed, but the internal **state** of the sequence has.
#### Replacing Elements
... | github_jupyter |
```
# Based on Huggingface interface
# - https://huggingface.co/transformers/notebooks.html
# - https://github.com/huggingface/notebooks/blob/master/transformers_doc/quicktour.ipynb
# -
# Transformers installation, if needed
#! pip install transformers datasets
```
# Task: Sentiment analysis
```
# Default model used ... | github_jupyter |
# Measuring a Multiport Device with a 2-Port Network Analyzer
## Introduction
In microwave measurements, one commonly needs to measure a n-port deveice with a m-port network analyzer ($m<n$ of course).
<img src="nports_with_2ports.svg"/>
This can be done by terminating each non-measured port with a matched load,... | github_jupyter |
<a href="https://colab.research.google.com/github/kyle-gao/ML_ipynb/blob/master/TF_TPU_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the... | github_jupyter |
<table> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="25%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by <a href="http://abu.lu.... | github_jupyter |
```
from __future__ import print_function ## Force python3-like printing
try:
from importlib import reload
except:
pass
%matplotlib inline
# %matplotlib notebook
from matplotlib import pyplot as plt
import os
import warnings
import numpy as np
from astropy.table import Table
from scipy.integrate import sim... | github_jupyter |
# W-net Model - Train
```
%matplotlib inline
import matplotlib.pylab as plt
import numpy as np
import os
import glob
import sys
from keras.optimizers import Adam
# Importing our w-net model
MY_UTILS_PATH = "../Modules/"
if not MY_UTILS_PATH in sys.path:
sys.path.append(MY_UTILS_PATH)
import frequency_spatial_net... | github_jupyter |
# Bidirectional LSTM Sentiment Classifier
In this notebook, we use a *bidirectional* LSTM to classify IMDB movie reviews by their sentiment.
#### Load dependencies
```
import tensorflow
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.... | github_jupyter |
# Árboles de decisión y bosques
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
```
Ahora vamos a ver una serie de modelos basados en árboles de decisión. Los árboles de decisión son modelos muy intuitivos. Codifican una serie de decisiones del tipo "SI" "ENTONCES", de forma similar a cómo l... | github_jupyter |
```
#remove cell visibility
from IPython.display import HTML
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
Toggle cell visibilit... | github_jupyter |
```
import tensorflow as tf
import random
import gym
import numpy as np
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
import gym_super_mario_bros
from gym_supe... | github_jupyter |
## What is a Variable?
A variable is any characteristics, number, or quantity that can be measured or counted. For example:
- Age (21, 35, 62, ...)
- Gender (male, female)
- Income (GBP 20000, GBP 35000, GBP 45000, ...)
- House price (GBP 350000, GBP 570000, ...)
- Country of birth (China, Russia, Costa Rica, ...)
- ... | github_jupyter |
# Contributing
Contributions are very welcome — please do ask questions and suggest ideas in [Issues](https://github.com/nategadzhi/notoma/issues), and feel free to implement features you want and submit them via Pull Requests.
%METADATA%
layout: default
nav_order: 4
title: Contributing
### Reporting issues
Please ... | github_jupyter |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Requantization of a Speech Signal
The following exa... | github_jupyter |
# Import libraries
```
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import matplotlib.p... | github_jupyter |
```
#https://www.powercms.in/blog/how-get-json-data-remote-url-python-script
import urllib.request, json
#save url inside variable as raw string
url = r"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=MSFT&interval=5min&apikey=demo"
#use urllib.request.urlopen()
response = urllib.request.urlop... | github_jupyter |
# **Boston BLUE bikes Analysis**
Team Member: Zhangcheng Guo, Chang-Han Chen, Ziqi Shan, Tsung Yen Wu, Jiahui Xu
### Topic Background and Motivation
>A rapidly growing industry, bike-sharing, replaces traditional bike rentals. BLUE bikes' renting procedures are fully automated from picking up, returning, and making p... | github_jupyter |
# Dog Breed Identification
This example is based on a very popular [Udacity project](https://github.com/udacity/dog-project). The goal is to classify images of dogs according to their breed.
In this notebook, you will take the first steps towards developing an algorithm that could be used as part of a mobile or web... | github_jupyter |
# Visual English
### Eryk Wdowiak
This notebook attempts to illustrate the English text that we're using to develop a neural machine translator.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib inline
import nltk
from nltk.tokenize import word_tokeni... | github_jupyter |
```sql
-- Create a new table
CREATE TABLE people (
first_name VARCHAR(30) NOT NULL,
has_pet BOOLEAN DEFAULT true,
pet_type VARCHAR(10) NOT NULL,
pet_name VARCHAR(30),
pet_age INT
);
```
```sql
-- Creating tables for PH-EmployeeDB
CREATE TABLE departments (
dept_no VARCHAR(4) NOT NULL,
dept_name VARCHAR(... | github_jupyter |
<a href="https://colab.research.google.com/github/queiyanglim/trading_algorithm/blob/master/brent_wti_copula.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!git clone https://github.com/queiyanglim/trading_algorithm.git
import os
os.getcwd()
im... | github_jupyter |
# 02 - RDD: RESILENT DISTRIBUTED DATASETS
Colección inmutable y distribuida de elementos que pueden manipularse en paralelo
Un programa Spark opera sobre RDDs:
Spark automáticamente distribuye los datos y paraleliza las operaciones
```
!pip install pyspark
# Create apache spark ... | github_jupyter |
# SYCL Task Scheduling and Data Dependences
##### Sections
- [Buffers and Accessors](#Buffers-and-Accessors)
- [Memory Management](#Memory-Management)
- [Explicit Data Movement](#Explicit-Data-Movement)
- [Implicit data movement](#Implicit-data-movement)
- [What is USM?](#What-is-Unified-Shared-Memory?)
- [Types of ... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Synchronisation in Complex Networks
```
import numpy as np
import matplotlib.pylab as plt
import networkx as nx
from NetworkFunctions import *
from NetworkClasses import *
N = 100; # number of nodes
m = 2;
G = nx.barabasi_albert_graph(N,m,seed=None); # Barabasi-Albert graph
A = nx.to_numpy_matrix(G); # creates adja... | github_jupyter |
## ALS Implementation
- This notebook |is implementation of ALS algorithm from "collaborative filtering for implicit dataset"
### Initialize parameters
- r_lambda: normalization parameter
- alpha: confidence level
- nf: dimension of latent vector of each user and item
- initilzed values(40, 200, 40) are the best... | github_jupyter |
```
import kwant
import numpy as np
import matplotlib.pyplot as pyplot
import tinyarray
%matplotlib inline
import scipy
from tqdm.notebook import tqdm
```
$$H = v_f(k_y \sigma_x - k_x\sigma_y) + (m_0 - m_1(k_x^2 + k_y^2))\sigma_z\tau_z + M_z\sigma_z$$
$$H = v_f(k_x\sigma_x - k_y\sigma_y) + (m_0 - m_1(k_x^2 + k_y^2))\... | github_jupyter |
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