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## The question
**Prj05**
Consider the Vasicek model
$$d r_t = \alpha (b - r_t) dt + \sigma dW_t$$
with the following parameters:
$$r_0 = .005, \alpha = 2.11, b = 0.02, \sigma = 0.033.$$
**Todo**
1. Implement Euler simulation and draw a plot of $\mathbb E[ r_t ]$ on $t\in [0, 10]$.
2. Find explicit form of $\math... | github_jupyter |
```
from __future__ import division
import os
import numpy as np
from collections import OrderedDict
import logging
import pandas
from astropy.io import fits
import astropy.wcs
from astropy import table
import sep
import warnings
from astropy.utils.exceptions import AstropyWarning
warnings.simplefilter('ignore', cate... | github_jupyter |
# Append Columns and Rows
Copyright (c) Microsoft Corporation. All rights reserved.<br>
Licensed under the MIT License.<br>
... | github_jupyter |
```
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '%.3f' % x)
pd.options.mode.chained_assignment = None
%matplotlib inline
import matplotlib
#matplotlib.use('agg')
matplotlib.style.use('ggplot')
from matplotlib import pyplot as plt
from functools import reduce
import pickle as p... | github_jupyter |
```
import numpy as np
from collections import Counter
from graphviz import Digraph
class Node:
def __init__(self, frequency, letter=None):
self.left=None
self.right=None
self.parent=None
self.frequency = frequency
self.letter = letter if letter is not None else None
... | 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 |
# QGS model: MAOSOAM
## Coupled ocean-atmosphere channel model version
This model version is a 2-layer channel QG atmosphere truncated at wavenumber 2 coupled, both by friction and heat exchange, to a shallow water **channel** ocean also truncated at wavenumber 2.
More details can be found in the articles:
* Vanni... | github_jupyter |
```
## Here I am removing all the protected features to see the difference from normal
import pandas as pd
import random,time
import numpy as np
import math,copy
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from ... | github_jupyter |
<a href="https://githubtocolab.com/giswqs/geemap/blob/master/examples/notebooks/48_folium_legend.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a>
Uncomment the following line to install [geemap](https://geemap.org) if needed.
```
# !pip install geem... | github_jupyter |
## Logistic Regression in plain Python
In logistic regression, we are trying to model the outcome of a **binary variable** given a **linear combination of input features**. For example, we could try to predict the outcome of an election (win/lose) using information about how much money a candidate spent campaigning, h... | github_jupyter |
```
%pylab inline
rc("image", cmap="gray", interpolation="nearest")
figsize(7, 7)
```
# PyTorch
"Tensors and Dynamic neural networks in Python with strong GPU acceleration"
- like Matlab or Numpy, but with GPU support
- automatic, dynamic differentiation and gradient descent
- some frameworks for neural networks
# ... | github_jupyter |
# Introduction to JumpStart - Image Classification
---
Welcome to Amazon [SageMaker JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html)! You can use JumpStart to solve many Machine Learning tasks through one-click in SageMaker Studio, or through [SageMaker JumpStart API](https://sagemaker... | github_jupyter |
# Object Oriented Programming
Object Oriented Programming (OOP) tends to be one of the major obstacles for beginners when they are first starting to learn Python.
There are many, many tutorials and lessons covering OOP so feel free to Google search other lessons, and I have also put some links to other useful tutoria... | github_jupyter |
<img src="https://ucfai.org/groups/supplementary/sp20/02-06-stats-intro/stats-intro/banner.png">
<div class="col-12">
<span class="btn btn-success btn-block">
Meeting in-person? Have you signed in?
</span>
</div>
<div class="col-12">
<h1> Introduction to Statistics, Featuring Datascience </h1>
... | github_jupyter |
# This notebook will give a first baseline estimation for the matching of entities via a random forest algorithm as multi-class classification
```
import os
import pandas as pd
import gzip
import json
import numpy as np
import nltk
from nltk.corpus import stopwords
import string
from nltk.tokenize import word_tokenize... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
url = '/content/drive/My Drive/Colab Notebooks/Unit 2/223 Data Modeling/london_merged.csv'
df = pd.read_csv(url)
print(df.shape)
df.head()
```
Lambda Schoo... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Traffic Light Detection
## Dependencies
```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import pickle
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mplimg
import glob
%matplotlib inline
```
## First, I tested and debugged a... | github_jupyter |
# Why You Should Hedge Beta and Sector Exposures (Part I)
by Jonathan Larkin and Maxwell Margenot
Part of the Quantopian Lecture Series:
* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)
* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)
---
Whenever we have... | github_jupyter |
```
# Imports
import numpy as np
from skmultiflow.trees import HoeffdingTree
from skmultiflow.data.file_stream import FileStream
val_actual_class_labels=[] #Valence Acutal class labels
val_predicted_class_labels=[] #Valence Predicted Class labels
aro_actual_class_labels =[] #Arousal Acutal class labels
aro_predicte... | github_jupyter |
```
import sklearn
import requests
import json
import spotipy#authentication
import spotipy.util as util#authentication
from spotipy.oauth2 import SpotifyClientCredentials#authentication
# Make sure to fill in your spotify client_secret information
cid = "049ade7215e54c63a2b628f3784dc407"
secret = "171ef0fc408745e88dd5... | github_jupyter |
```
# 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 law or agreed to in writing, software
# distributed und... | github_jupyter |
# Table of Contents
* [1) Large Margin Classification](#1%29-Large-Margin-Classification)
* [1) Optimization Objective](#1%29-Optimization-Objective)
* [2) Large Margin Intuition](#2%29-Large-Margin-Intuition)
* [3) Mathematics Behind Large Margin Classification](#3%29-Mathematics-Behind-Large-Margin-Classific... | github_jupyter |
```
from google.colab import drive
drive.mount("/content/drive")
!unzip '/content/drive/My Drive/Colab_Dataset/Dataset2.zip'
pip install np_utils
import matplotlib.pyplot as plt
import tensorflow as tf
import PIL
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from ... | github_jupyter |
## Basic usage of matplotlib
Matplotlib is the module of choice whenever you want to make a niceplot.
```
# the following two lines are required inside a python script to be run on binder. They are not needed inside the notebook.
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot a... | github_jupyter |
```
from __future__ import division, print_function, absolute_import
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regres... | github_jupyter |
# Deep Learning with Python
## 6.1 Working with text data
> 处理文本数据
要用深度学习的神经网络处理文本数据，和图片类似，也要把数据向量化：文本 -> 数值张量。
要做这种事情可以把每个单词变成向量，也可以把字符变成向量，还可以把多个连续单词或字符(称为 *N-grams*)变成向量。
反正不管如何划分，我们把文本拆分出来的单元叫做 *tokens*（标记），拆分文本的过程叫做 *tokenization*(分词)。
> 注：token 的中文翻译是“标记”😂。这些翻译都怪怪的，虽然 token 确实有标记这个意思，但把这里的 token 翻译成标记就没内味... | github_jupyter |
O objetivo desta lista de exercício é instigar que você resolva problemas simples usando o básico do python, sem necessitar importar pacotes ainda.
Alguns exercícios são aplicados à oceanografia e outros gerais, mas todos com a intenção de que você fortaleça o conhecimento em alguns pontos chaves que servirão de base ... | github_jupyter |
```
import re
import docx2txt
import networkx as nx
import matplotlib.pyplot as plt
%matplotlib inline
```
## Extract programming language from Knowledge Graph
```
file_name_1 = 'Mathew Elliot.docx'
file_name_2 = 'John Guy.docx'
file_name_3 = 'Max Payne.docx'
def extract_programming_languages(file_name):
# read ... | github_jupyter |
# PIG, Beginner’s Version:
* Players take turns rolling a die as many times as they like.
* If a roll is a 2, 3, 4, 5, or 6, the player adds that many points to their score for the turn.
* A player may choose to end their turn at any time and “bank” their points.
* If a player rolls a 1, they lose all their unbank... | github_jupyter |
<a href="https://colab.research.google.com/github/TomFrederik/lucent/blob/dev/notebooks/Lucent_%2B_torchvision.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Licensed under the Apache License, Version 2.0 (the "License");
```
# Licensed unde... | github_jupyter |
# Finch usage
Finch is a WPS server for climate indicators, but also has a few utilities to facilitate data handling. To get started, first instantiate the client. Here, the client will try to connect to a local or remote finch instance, depending on whether the environment variable `WPS_URL` is defined.
```
import o... | github_jupyter |
```
# Change directory to the root so that relative path loads work correctly
import os
try:
os.chdir(os.path.join(os.getcwd(), ".."))
print(os.getcwd())
except:
pass
import glob
import sys
import matplotlib.pyplot as plt
import numpy as np
import torch
from experiments.A_constrained_training.main import... | github_jupyter |
## Notebook for preparing final dataset
```
import pandas as pd
import numpy as np
import re
```
## Dataset 1
```
file2 = pd.read_csv("./dataset/traindata2.csv")
file2.tail() #2 for normal 0,1 for toxic
file2.iloc[24767][-1]
tweet = file2["tweet"]
def clean_text(text):
text = text.lower()
text = re.sub(... | github_jupyter |
## Installs & Imports
```
# Select Tensorflow 2.x version in Colab
%tensorflow_version 2.x
# Import TensorFlow and tf.keras
import tensorflow as tf
keras = tf.keras
# Import helper libraries
import numpy as np
import matplotlib.pyplot as plt
# Print TensorFlow version
version = tf.__version__
print(version)
```
##... | github_jupyter |
## _*H2 ground state energy computation using Iterative QPE*_
This notebook demonstrates computing and graphing the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances using `IQPE` (Iterative Quantum Phase Estimation) algorithm. It is compared to the ground-truth energies as comput... | github_jupyter |
# Import data and preprocess it
```
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
use_all_features = True
use_full_data = True
test_sml_size = 3000
#file paths
train_sig_path_sml = "data/train_sml_sig.csv"
train_bg_path_sml = "data/train_sml_bg.csv"
train_sig_path = ... | github_jupyter |
# MXNet - Gluon Code Snippets
#### Index:
## 1. Import Libraries
```
from mxnet import autograd, nd
# #Gluon data module to read data
from mxnet.gluon import data as gdata
# #Neural Network Layers
from mxnet.gluon import nn
# #Model Parameter Initalizer
from mxnet import init
# #Gluon module to define loss funct... | github_jupyter |
# Generate volcanic ERF time series
Theme Song: Mt. Pinatubo<br>
Artist: The Low Frequency In Stereo<br>
Album: Futuro<br>
Released: 2009
```
from netCDF4 import Dataset, num2date
import numpy as np
import matplotlib.pyplot as pl
import pandas as pd
from ar6.utils import check_and_download
import scipy.stats
%matplot... | github_jupyter |
# Optional Coding Exercise
## -- Implementing a "CART" Decision Tree From Scratch
```
%load_ext watermark
%watermark -d -u -a 'Sebastian Raschka' -v -p numpy,scipy,matplotlib
import numpy as np
```
<br>
<br>
<br>
<br>
<br>
<br>
## 1) Implementing a "CART" Decision Tree from Scratch
In this exercise, you are goin... | github_jupyter |
# Apprentice Challenge
This challenge is a diagnostic of your current python pandas, matplotlib/seaborn, and numpy skills. These diagnostics will help inform your selection into the Machine Learning Guild's Apprentice program.
## Challenge Background: A Magic Eight Ball & Randomness
![Shaking Magic Eight Ball](http... | github_jupyter |
<hr style="height:3px;border:none;color:#333;background-color:#333;" />
<img style=" float:right; display:inline" src="http://opencloud.utsa.edu/wp-content/themes/utsa-oci/images/logo.png"/>
### **University of Texas at San Antonio**
<br/>
<br/>
<span style="color:#000; font-family: 'Bebas Neue'; font-size: 2.5em;"... | github_jupyter |
# Augmenter l'accuracy du Computer Vision grâce aux réseaux à convolution
Dans le workshop précédent, vous avez vu comment reconnaître des vêtements à travers un réseau de neurones constitué de 3 couches. Vous avez pu experimenter l'impact des différents paramètres du modèle comme le nombre de neurones dans la couche ... | github_jupyter |
# Spark Train Logistic Regression
Train Logistic Regression classifier with Apache SparkML
```
%%bash
export version=`python --version |awk '{print $2}' |awk -F"." '{print $1$2}'`
echo $version
if [ $version == '36' ] || [ $version == '37' ]; then
echo 'Starting installation...'
pip3 install pyspark==2.4.8 ... | github_jupyter |
# Testing
In the Digital Humanities Lab, we're going to be ensuring that our code is thoroughly documented and tested. This is important because we are collaborating with others and we will also be sharing our code publicly. Once you get used to writing documentation, then tests, then code, you may find that writing t... | github_jupyter |
# Build a stock market brief - S01E06-automate-the-brief
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Yahoo%20Finance/Build%20a%20stock%20market%20brief/S01E06-automate-the-brief.ipynb" target="_parent">
<img src="https://img.shie... | github_jupyter |
# **1D Map Conjugacy for the Kuramoto-Sivashinsky PDE**
```
import numpy as np
from utils import Kuramoto
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# Set plotting parameters
parameters = {'axes.labelsize': 16,
'axes.titlesize': 18,
'legend.fontsize': 13,
... | github_jupyter |
```
import pandas as pd
import numpy as np
import re
from itertools import combinations
import pcalg
import networkx as nx
DATA_FILE = "./data/20200807_user-db_cpu-load_03.json"
TARGET_DATA = {"containers": ["container_cpu_usage_seconds_total", "container_fs_io_current", "container_memory_working_set_bytes", "container... | github_jupyter |
# Summary of Quantum Operations
## Fundamentals
(revised by Amba Datt Pant, originaly created by Diwakar Sigdel)
## Qubit
- Regular or classical computer works on rules of logic - operation based on bits 0 or 1.
- qubit --> quantum bit that can follow quantum mechanics (rules of quantum mechanics). **0, 1 and interme... | github_jupyter |
<a href="https://colab.research.google.com/github/SerafDosSantos/MesBlocNotes/blob/main/exemple_de_PoW_(Proof_of_Work).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Exemple de la preuve de travail (PoW ou Proof-of-Work)
Ce document est un tutor... | github_jupyter |
```
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem import rdMolDescriptors as rdmd
from rdkit.Chem.Scaffolds import MurckoScaffold
import pandas as pd
from tqdm import tqdm
import time
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.cluster import MiniBatchKM... | github_jupyter |
## Training excitatory-inhibitory recurrent network
Here we will train recurrent neural network with excitatory and inhibitory neurons on a simple perceptual decision making task.
[](https://colab.research.google.com/github/gyyang/nn-brain/blob... | github_jupyter |
```
from importlib import reload
import sys
from imp import reload
import warnings
warnings.filterwarnings('ignore')
if sys.version[0] == '2':
reload(sys)
sys.setdefaultencoding("utf-8")
import pandas as pd
df1 = pd.read_csv('labeledTrainData.tsv', delimiter="\t")
df1 = df1.drop(['id'], axis=1)
df1.head()
df1
... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter |
```
!wget http://www.gutenberg.org/files/11/11-0.txt
from keras.models import Sequential
from keras.layers import Dense,Activation
from keras.layers.recurrent import SimpleRNN
import numpy as np
fin=open('11-0.txt',encoding='utf-8-sig')
lines=[]
for line in fin:
line = line.strip().lower()
#line = line.decode("asci... | github_jupyter |
# Python for scientific computing
> Marcos Duarte, Renato Naville Watanabe
> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab)
> Federal University of ABC, Brazil
<p style="text-align: right;">A <a href="https://jupyter.org/">Jupyter Notebook</a></p>
The [Python programming lang... | github_jupyter |
# Mask R-CNN - Train on Shapes Dataset
This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be ... | github_jupyter |
# SciPy - Library of scientific algorithms for Python
Original by J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/
Modified by Clayton Miller (miller.clayton@arch.ethz.ch)
The other notebooks in this lecture series are indexed at [http://jrjohansson.github.com](http://jrjohansson.github.com).
# Introducti... | github_jupyter |
[Original Notebook Downloaded From Kaggle](https://www.kaggle.com/bariskavus/diabetes-prediction-randomforestclassifier)
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's... | github_jupyter |
# Portfolio Optimization
This notebook can be run online without installing any packages. Just click the logo:
[](https://mybinder.org/v2/gh/mcdeoliveira/pyoptimum-examples/master?filepath=examples%2Fportfolio.ipynb)
to run it on [binder](https://mybinder.org).
See this... | github_jupyter |
3.11 模型选择、欠拟合和过拟合
在前几节基于Fashion-MNIST数据集的实验中，我们评价了机器学习模型在训练数据集和测试数据集上的表现。如果你改变过实验中的模型结构或者超参数，你也许发现了：当模型在训练数据集上更准确时，它在测试数据集上却不一定更准确。这是为什么呢？
3.11.1 训练误差和泛化误差
在解释上述现象之前，我们需要区分训练误差（training error）和泛化误差（generalization error）。通俗来讲，前者指模型在训练数据集上表现出的误差，后者指模型在任意一个测试数据样本上表现出的误差的期望，并常常通过测试数据集上的误差来近似。计算训练误差和泛化误差可以使用之前介绍过的损失函数，例如线性... | github_jupyter |
# 1. Formulate your questions
Are there party-level differences in House expenditures?
```
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sb
import numpy as np
import pandas as pd
```
# 2. Read in your data
From ProPublica's [House Office Expenditure Data](https://projects.propublica.org/repr... | github_jupyter |
# NumPy for Python in Jupyter Notebook
```
# NumPy (Numerical Python)
import numpy as np
```
### Creating an array
```
a=[1,2,3,4,5]
print("This is a list:",a)
b=np.array(a)
print("\nArray created from list:",b)
print("Class of array:",type(b)) # not a list
print("Datatype of array:",b.dtype) # dtype attribute ret... | github_jupyter |
# Network Analysis
---
## Introduction
Networks are mathematical or graphical representations of patterns of relationships between entities. These relationships are defined by some measure of "closeness" between individuals, and can exist in an abstract or actual space (for example, whether you are related to someo... | github_jupyter |
# Ensemble NMS - Detectron2 [Inference]
### Hi kagglers, This is `Ensemble NMW - Detectron2 [Inference]` notebook.
* [Sartorius Segmentation - Detectron2 [training]](https://www.kaggle.com/ammarnassanalhajali/sartorius-segmentation-detectron2-training)
* [Sartorius Segmentation - Detectron2 [Inference]](https://www.k... | github_jupyter |
<div style='background-image: url("share/baku.jpg") ; padding: 0px ; background-size: cover ; border-radius: 15px ; height: 250px; background-position: 0% 80%'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.9) ; width: 50% ; height: 150px">
<div style="positi... | github_jupyter |
# GPU-accelerated interactive visualization of single cells with RAPIDS, Scanpy and Plotly Dash
Copyright (c) 2020, NVIDIA CORPORATION.
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://ww... | github_jupyter |
```
import numpy as np
import urdf2casadi.urdfparser as u2c
from urdf2casadi.geometry import plucker
from urdf_parser_py.urdf import URDF, Pose
import PyKDL as kdl
import kdl_parser_py.urdf as kdlurdf
from timeit import Timer, timeit, repeat
import rbdl
import pybullet as pb
def median(lst):
n = len(lst)
if n ... | github_jupyter |
Guilherme Andrade, Gabriel Ramos, Daniel Madeira, Rafael Sachetto, Renato Ferreira, Leonardo Rocha, G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering, Procedia Computer Science, Volume 18, 2013, Pages 369-378, ISSN 1877-0509, http://dx.doi.org/10.1016/j.procs.2013.05.200.
(http://www.sciencedirect.com/... | github_jupyter |
# 12.15.1 Getting and Mapping the Tweets
### Get the `API` Object
```
from tweetutilities import get_API
api = get_API()
```
### Collections Required By `LocationListener`
```
tweets = []
counts = {'total_tweets': 0, 'locations': 0}
```
### Creating the `LocationListener`
```
from locationlistener import LocationL... | github_jupyter |
# 사용자 정의 모델 만들기 (Siamese)
> fastai에서는 데이터를 정의하는 방법으로 DataBlock API를 제안합니다. 각 인자가 의미하는 내용과, 실제 Siamese 공식 튜토리얼에 이 내용이 어떻게 적용되는지를 살펴봅니다.
- author: "Chansung Park"
- toc: true
- image: images/datablock/siamese-model.png
- comments: true
- categories: [model, siamese, fastai]
- permalink: /model-siamese/
- badges: false
-... | github_jupyter |
This notebook I am going to discuss about,
1. deep learning
2. forward propagation
3. gradient decent
4. backword propagation
5. basic deep learning model with keras
### Deep Learning :
----
Deep learning is a machine learning algorithm where artificial neural network solve particular problem. This neural netwo... | github_jupyter |
<a href="https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%206%20-%20Lesson%202%20-%20Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2019 The TensorFlow Authors.
```
#@title Lic... | github_jupyter |
## Show the attention of VGG19
```
from keras.applications.mobilenet import MobileNet
from keras.applications.mobilenet import preprocess_input as MobileNet_preprocess_input
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input as VGG19_preprocess_input
from keras.application... | github_jupyter |
# Transformer
What is a Transformer?
A Transformer is a type of neural network architecture developed by Vaswani et al. in 2017.
Without going into too much detail, this model architecture consists of a multi-head self-attention mechanism combined with an encoder-decoder structure. It can achieve SOTA results that ou... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
```
### Dependencies
```
!unzip -q '/content/drive/My Drive/Colab Notebooks/[Kaggle] Understanding Clouds from Satellite Images/Data/train_images256x384.zip'
!unzip -q '/content/drive/My Drive/Colab Notebooks/[Kaggle] Understanding Clouds from Satellite... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
from IPython.display import HTML, Latex, Markdown, Pretty
from windIO.Plant import WTLayout
from fusedwake.WindFarm import WindFarm
from fusedwake.Plotting import circles
from fusedwake.gcl import GCL
import fusedwake.gcl.fortran as fgcl
import fusedwake.gcl.p... | github_jupyter |
## Smart Signatures with Transaction Groups
#### 06.3.5 Winter School on Smart Contracts
##### Peter Gruber (peter.gruber@usi.ch)
2022-01-22
* Smart Signatures with more than 1 transaction
* Combine conditions across transactions
## Setup
See notebook 04.1, the lines below will always automatically load functions in ... | github_jupyter |
# Self Supervised Learning with Fastai
> Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks.
[](https:/... | github_jupyter |
# Aerospike Spark Connector Tutorial for Scala
## Tested with Spark connector 3.2.0, ASDB EE 5.7.0.7, Java 8, Apache Spark 3.0.2, Python 3.7 and Scala 2.12.11 and [Spylon]( https://pypi.org/project/spylon-kernel/)
#### Please download the appropriate Aeropsike Connect for Spark from the [download page](https://enterp... | github_jupyter |
# bioptim #1 - InitialGuess
This tutorial is a trivial example on how to manage InitialGuess with bioptim. It is designed to show how one can change the InitialGuess of a problem if it's needed.
InitialGuess allow the problem to start the calculation at a certain point, the goal is to make this initialGuess as near as... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import scipy.signal
import time
import cv2
import matplotlib.pyplot as plt
tf.config.list_physical_devices("GPU")
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.c... | github_jupyter |
# COVID-19 Time Series Prediction Using Temporal Fusion Transformers
## Bernhard Kaindl
**DISCLAIMER:** This project is part of Udacity's [Data Scientist Nanodegree](https://classroom.udacity.com/nanodegrees/nd025/dashboard/overview). The model shipped with this version of the project is to be understood as a _proof ... | github_jupyter |
```
class MFInput:
def __init__(self, name, x, y, x0):
self.name = name
# list of tuples
self.points = [(x[i], y[i]) for i in range(len(x))]
self.mi = self.getMi(x0)
def getY(self, x1, y1, x2, y2, x0):
if y1 == y2:
return y1
... | github_jupyter |
```
#importing important libraries
#libraries for reading dataset
import numpy as np
import pandas as pd
#libraries for data visualisation
import matplotlib.pyplot as plt
import seaborn as sns
#libraries for model building and understanding
import sklearn
from sklearn.model_selection import train_test_split
from skl... | github_jupyter |
<img src="https://rhyme.com/assets/img/logo-dark.png" align="center"> <h2 align="center">Logistic Regression: A Sentiment Analysis Case Study</h2>
### Introduction
___
- IMDB movie reviews dataset
- http://ai.stanford.edu/~amaas/data/sentiment
- Contains 25000 positive and 25000 negative reviews
<img src="https:/... | github_jupyter |
```
import sys
if not '..' in sys.path:
sys.path.append('..')
from draw_workflow import draw_workflow
```
# Noodles
_Easy_ concurrent programming <s>in</s> using Python
Johan Hidding, Thursday 19-11-2015 @ NLeSC
```
from noodles import schedule, run, run_parallel, gather
```
## But, why?
* save time _us... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#-MIDS---w261-Machine-Learning-At-Scale-" data-toc-modified-id="-MIDS---w261-Machine-Learning-At-Scale--1"><span class="toc-item-num">1 </span> MIDS - w261 Machine Learning At Scale </a></div><div class="lev2 toc-item"><a href="#Assignment---HW11" d... | github_jupyter |
```
from keras.layers import Dense, Activation, Dropout, Reshape, concatenate, ReLU, Input
from keras.models import Model, Sequential
from keras.regularizers import l2, l1_l2
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.layers.normalization import BatchNormalization
from kera... | github_jupyter |
## Python Closures and Generators
## Closures - binding variables from outer function in the inner function
## Technically - function gets stored with its enviroment(bound variables)
### Can also think of preserving certain state
```
# remember this function?
def add_factory(x):
def add(y):
return y + x
... | github_jupyter |
# Lab 01 : Deep Q-Learning (DQN) - demo
```
# For Google Colaboratory
import sys, os
if 'google.colab' in sys.modules:
from google.colab import drive
drive.mount('/content/gdrive')
file_name = 'DQN_demo.ipynb'
import subprocess
path_to_file = subprocess.check_output('find . -type f -name ' + str(fi... | github_jupyter |
```
import torch
import torch.nn as nn
import os
import numpy as np
from string import punctuation
char_to_int = {"'": 1, ',': 2, 'e': 3, 'a': 4, 'r': 5, 'i': 6, 's': 7, 'n': 8, 'o': 9, 't': 10, 'l': 11, 'c': 12, 'd': 13, 'm': 14, 'u': 15, 'h': 16, 'g': 17, 'p': 18, 'b': 19, 'k': 20, 'y': 21, '"': 22, 'f': 23, 'w': 24,... | 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 |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
# Day 2, session 3: Detecting features in Hi-C maps
In this session we will be looking at ways to automatically find regions with features of interest.
This includes both supervised and unsupervised methods depending on the question.
## Unsupervised detection
### Differential contacts
The classic approach, much like... | github_jupyter |
## 모듈 불러오기
```
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras import layers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import json
from tqdm im... | github_jupyter |
<center>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# K-Nearest Neighbors
Estimated time needed: **25** minutes
## Objectives
After completing this lab you will b... | github_jupyter |
# Clean Slate: Estimating offenses eligible for expungement under varying conditions
> Prepared by [Laura Feeney](https://github.com/laurafeeney) for Code for Boston's [Clean Slate project](https://github.com/codeforboston/clean-slate).
## Summary
This notebook takes somewhat processed data from the Middlesex DA and a... | github_jupyter |
TSG035 - Spark History logs
===========================
Description
-----------
Steps
-----
### Parameters
```
import re
tail_lines = 2000
pod = None # All
container='hadoop-livy-sparkhistory'
log_files = [ "/var/log/supervisor/log/sparkhistory*" ]
expressions_to_analyze = [
re.compile(".{23} WARN "),
re... | github_jupyter |
```
import numpy as np
import cv2
import time
import matplotlib.pyplot as plt
img_left_color=cv2.imread('Left/ImageL1.png')
img_right_color=cv2.imread('Right/ImageR1.png')
img_left_bw = cv2.blur(cv2.cvtColor(img_left_color, cv2.COLOR_RGB2GRAY),(5,5))
img_right_bw = cv2.blur(cv2.cvtColor(img_right_color, cv2.COLOR_RGB2... | github_jupyter |
# Functions
Functions are key to creating reusable software, testing, and working in teams.
This lecture motivates the use of functions, discusses how functions are defined in python, and
introduces a workflow that starts with exploratory code and produces a function.
**Topics**
- Creating reusable software components... | github_jupyter |
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