text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
# Launch buttons for interactivity
Because Jupyter Books are built with Jupyter Notebooks, you can allow users to launch
live Jupyter sessions in the cloud directly from your book. This lets readers quickly interact
with your content in a traditional coding interface using either JupyterHub or BinderHub.
This page des... | github_jupyter |
---
layout: page
title: Método Científico (Incompleto)
nav_order: 12
---
[<img src="./colab_favicon_small.png" style="float: right;">](https://colab.research.google.com/github/icd-ufmg/icd-ufmg.github.io/blob/master/_lessons/12-causalidade.ipynb)
# Método Científico (Incompleto)
{: .no_toc .mb-2 }
Juntando o método... | github_jupyter |
```
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoTokenizer, AutoModelWithLMHead, BertTokenizer, LongformerTokenizer, LongformerModel
import torch, json, random
import numpy as np
percentage = '20'
path = '../data/multiwiz/agent/'+percentage+'p/'
db_path = '../createData/multiwoz21/'
artificial_data_path... | github_jupyter |
# Prompt Tuning
```
import torch
colab = 'google.colab' in str(get_ipython())
# You need a T4. A K80 will not work.
if colab:
!nvidia-smi
gpu_type = torch.cuda.get_device_name(0)
if gpu_type != 'Tesla T4':
raise ValueError("I don't know about this, chief")
# Setup for Colab only
if colab:
!pip... | github_jupyter |
# Clickstream Analysis using Apache Spark and Apache Kafka(or Message hub).
[Message Hub: Apache Kafka as a Service](https://developer.ibm.com/messaging/2016/03/14/message-hub-apache-kafka-as-a-service/), is well integrated into the IBM Data Science Experience.
Before running the notebook, you will need to setup a [... | github_jupyter |
# Welcome to hent-AI colab!
This colab can utilize Googles vast resources for super fast decensoring using this project. All you need is a Google Drive and a good amount of free space on it.
hent-AI git project page: https://github.com/natethegreate/hentAI
# Prereqs
In your Google Drive, make a folder called hent-AI... | github_jupyter |
# About this kernel
The `cost_function` in this kernel is roughly 300x faster compared to the original kernel. Each function call takes roughly 37 µs.
## Reference
* (Excellent) Original Kernel: https://www.kaggle.com/inversion/santa-s-2019-starter-notebook
* First kernel that had the idea to use Numba: https://www.... | github_jupyter |
# Part 3: Advanced Remote Execution Tools
In the last section we trained a toy model using Federated Learning. We did this by calling .send() and .get() on our model, sending it to the location of training data, updating it, and then bringing it back. However, at the end of the example we realized that we needed to go... | github_jupyter |
# Visual GPU Log Analytics Part I: CPU Baseline in Python Pandas
Graphistry is great -- Graphistry and RAPIDS/BlazingDB is better!
This tutorial series visually analyzes Zeek/Bro network connection logs using different compute engines:
* Part I: [CPU Baseline in Python Pandas](./part_i_cpu_pandas.ipynb)
* Part II: [... | github_jupyter |
     
     
     
     
     
  
[Home Page](Start_Here.ipynb)
[Previous Notebook](Introduction_to_Performance_analysis.ipynb)
     
  &emsp... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
# Standard
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import signal
from PIL import Image
import scipy
import os
import cv2
# # Tensorflow and Keras
# from keras.datasets import mnist
# from keras.models... | github_jupyter |
```
%matplotlib notebook
import control as c
import ipywidgets as w
import numpy as np
from IPython.display import display, HTML
import matplotlib.pyplot as plt
import matplotlib.animation as animation
#display(HTML('<script> $(document).ready(function() { $(\"div.input\").hide(); }); </script>'))
# Toggle cell visi... | github_jupyter |
## Instructions
0. If you haven't already, follow [the setup instructions here](https://jennselby.github.io/MachineLearningCourseNotes/#setting-up-python3) to get all necessary software installed.
0. Install the Gensim word2vec Python implementation: `python3 -m pip install --upgrade gensim`
0. Get the trained model (1... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
Training and Testing Data
=====================================
To evaluate how well our supervised models generalize, we can split our data into a training and a test set:
<img src="figures/train_test_split_matrix.svg" width="100%">
```
... | github_jupyter |
# Mutivariate Regression Analysis
***
**Videos can be found at: https://www.youtube.com/channel/UCBsTB02yO0QGwtlfiv5m25Q**
In our previous tutorial, we explored the topic of Linear Regression Analysis which attempts to model the relationship between two variables by fitting a linear equation to the observed data. In ... | github_jupyter |
# PINN: Heat equation with variable diffusion
Solving the heat equation in 2D for variable diffusion D using the PINN-concept.
```
import torch
import torchphysics as tp
import math
```
First, we create the spaces for our problem. These define the variable names which will be used in the remaining part of this code.
... | github_jupyter |
```
import numpy as np
import torch
import pandas as pd
from transformers import PreTrainedTokenizerFast
import re
import spacy
nlp = spacy.load("en_core_web_sm")
tokenizer_bert = PreTrainedTokenizerFast.from_pretrained('bert-base-uncased', do_lower_case=True,return_offsets_mapping = True, max_length=512,truncate=True,... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Two Formulations of Maxwell's equations in Cartesian Coor... | github_jupyter |
```
import csv
import pandas as pd
import os
import scipy.stats
import numpy as np
from datetime import date,timedelta,datetime
def read_data(file):
df = pd.read_csv(file)
df = pd.DataFrame(df)
return df
def mofunc(row):
if row['Severity'] > 0.8 or row['Hazard_Score'] > 80:
return 'Warning'
... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/gdrive')
!pip install -q efficientnet
import math, re, os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow.keras.layers as L
import tensorflow.keras.backend ... | github_jupyter |
```
%cd ..
import pandas as pd
import pickle
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append(".")
from src.factory import *
from src.utils import *
from sklearn.metrics import log_loss
DATADIR = Path("../input/rsna-str-pulmonary-embolism-detection/")
train = pd.read_csv(DATADIR / "train.... | github_jupyter |
```
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
```
# Coordinate Ascent - AUC only
```
coats_df_auc_only = pd.read_csv("../output_data/coordinate_ascent_run_AUConly.csv")
coats_df_auc_only[coats_df_auc_only["loss"] == coats_df_auc_only["loss"]... | github_jupyter |
```
import math
import torch
import matplotlib.pyplot as plt
fpath = "./"
range_ = 10.0
n_pts = 25
fname = "high_loss_" + str(range_) + "_" + str(n_pts)
fname = fname.replace(".", "_")
high_loss = torch.load(fpath + fname, map_location=("cpu"))
fname = "low_loss_" + str(range_) + "_" + str(n_pts)
fname = fname.replac... | github_jupyter |
# Lightweight On-line Detector of Anomalies with MinMaxScaler
This code template is for Anomaly detection/outlier analysis using the LODA Algorithm implemented using pyod library and feature scaling using MinMaxScaler.
### Required Packages
```
!pip install plotly
!pip install pyod
import time
import warnings
imp... | github_jupyter |
```
import cv2
import PIL
import kornia
import glob
import torch
import numpy as np
import imgaug as ia
import imgaug.augmenters as iaa
import matplotlib.pyplot as plt
from torchvision import transforms as T
from networks.ResnetFaceSTN import ResnetFaceSTN
class RowImage:
def __init__(self, resize_dim=None):
... | github_jupyter |
[Table of Contents](./table_of_contents.ipynb)
# Multivariate Gaussians
Modeling Uncertainty in Multiple Dimensions
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
## Introduction
The techniques in the last chapter are very... | github_jupyter |
$\newcommand{\vct}[1]{\boldsymbol{#1}}
\newcommand{\mtx}[1]{\mathbf{#1}}
\newcommand{\tr}{^\mathrm{T}}
\newcommand{\reals}{\mathbb{R}}
\newcommand{\lpa}{\left(}
\newcommand{\rpa}{\right)}
\newcommand{\lsb}{\left[}
\newcommand{\rsb}{\right]}
\newcommand{\lbr}{\left\lbrace}
\newcommand{\rbr}{\right\rbrace}
\newcommand{\f... | github_jupyter |
```
# default_exp data.acquisition
```
# Data Acquisition
> This is a script which invokes `pybaseball`'s [`statcast()`](https://github.com/jldbc/pybaseball#statcast-pull-advanced-metrics-from-major-league-baseballs-statcast-system) function to retrieve pitch-level data from statcast.
```
#hide
# documentation
from... | github_jupyter |
```
#hide
#default_exp vis.gen
```
# Visualisation Generation
<br>
### Imports
```
#exports
import json
import pandas as pd
import typer
import croniter
import importlib
from tqdm import tqdm
import matplotlib.pyplot as plt
from IPython.display import JSON
#exports
def rgb_2_plt_tuple(r, g, b):
"""converts a ... | github_jupyter |
SOP013 - Create secret for azdata login (inside cluster)
========================================================
Description
-----------
Create a secret in the Kubernetes Secret Store, to:
- Run app-deploys (i.e. `azdata app run`)
- Save results in HDFS at /app-deploy
- Enable SOP028 to perform `azdata login`... | github_jupyter |
# Training a Score Estimator (SALLY)
```
import sys
import os
madminer_src_path = "/home/shomiller/madminer"
sys.path.append(madminer_src_path)
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
import math
import matplotlib
from matplotlib import pyp... | github_jupyter |
```
# Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Notebook authors: Kevin P. Murphy (murphyk@gmail.com)
# and Mahmoud Soliman (mjs@aucegypt.edu)
# This notebook reproduces figures for chap... | github_jupyter |
# MLOps with Seldon and Jenkins Classic
This repository shows how you can build a Jenkins Classic pipeline to enable Continuous Integration and Continuous Delivery (CI/CD) on your Machine Learning models leveraging Seldon for deployment.
This CI/CD pipeline will allow you to:
- Run unit tests using Jenkins Classic.
-... | github_jupyter |
El objetivo de este documento es explorar alternativas ofrecidas por la el modulo scipy.interpolate para interpolar datos 2d correspondientes a curvas de $C_L~vs.~\alpha$, $C_M~vs.~\alpha$ y $C_D~vs.~C_L$ para distintos reynolds. Los datos son estan extraidos del Report NACA 824 Gregory P.D. Siemens en 1994 cuando esta... | github_jupyter |
# SETUP AND DEPS
```
! git clone https://github.com/SwapnilDreams100/calling-out-bluff.git
! pip install alibi xhtml2pdf
from google.colab import drive
drive.mount('/content/drive')
! cp ./drive/My\ Drive/glove.6B.300d.txt ./
essay_type = '7'
import keras.layers as klayers
from keras.preprocessing.text import text_t... | github_jupyter |
```
%load_ext sql
%sql sqlite://
# Create tables & insert some random numbers
# Note: in Postgresql, try the generate_series function...
%sql DROP TABLE IF EXISTS R; DROP TABLE IF EXISTS S; DROP TABLE IF EXISTS T;
%sql CREATE TABLE R (A int); CREATE TABLE S (A int); CREATE TABLE T (A int);
for i in range(1,6):
%sql... | github_jupyter |
<a href="https://colab.research.google.com/github/DanIulian/BookStore/blob/master/02_rainbow(1)(1).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Not Quite Rainbow
```
# !apt install xvfb python-opengl ffmpeg -y > /dev/null 2>&1
# !pip install p... | github_jupyter |
## Importing packages
Throughout this tutorial, we will use the following common Python packages:
```
# Use these packages to easily access files on your hard drive
import os, sys, glob
# The Numpy package allows you to manipulate data (mainly numerical)
import numpy as np
# The Pandas package allows more advanced da... | github_jupyter |
```
### First imports and default parameters
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# Overwritting matplotlib default linestyle of negative contours
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
SEED = ... | github_jupyter |
In this notebook, we show how we can train a model with Scikit-learn and save it as a TileDB array on TileDB-Cloud.
Firstly, let's import what we need.
```
import numpy as np
import tiledb.cloud
import os
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from tiledb.ml.models.sklea... | github_jupyter |
# Riskfolio-Lib Tutorial:
<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__
<br>__[Orenji](https://www.orenj-i.net)__
<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__
<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__
<a href='https://ko-fi.com/B0B833SXD' target='... | github_jupyter |
<a href="https://colab.research.google.com/github/neurorishika/PSST/blob/master/Tutorial/Day%203%20Cells%20in%20Silicon/Day%203.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <a href="https://kaggle.com/kernels/welcome?src=https://raw.githubu... | github_jupyter |
<a href="https://colab.research.google.com/github/nnuncert/nnuncert/blob/master/notebooks/DNNC_toy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Git + Repo Installs
```
!git clone https://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@github.com/nnun... | github_jupyter |
# Goals
My goal with this dataset is it use a regression machine learning model to accurately predict the price of a house dependent on features. I will also look through some data analysis and a few other features such as PCA
## Process
I’ll be following a typical data science pipeline, “OSEMN”.
1. Obtaining the da... | github_jupyter |
# Hatch Template!
## Dandelion Voting
Note: What are peoples goal target raise?
1. Percentage of total tokens that have to vote 'yes' to `something` for it to pass.
```
import param
import panel as pn
import pandas as pd
import hvplot.pandas
import holoviews as hv
import numpy as np
pn.extension()
class DandelionVo... | github_jupyter |
# Walkthrough: Multi Device Plugin and the DevCloud
This notebook is a demonstration showing you how to request an edge node with an Intel i5 CPU and load a model on the CPU, GPU, and VPU (Intel® Neural Compute Stick 2) at the same time using the Multi Device Plugin on Udacity's workspace integration with Intel's DevC... | github_jupyter |
```
import tensorflow as tf
import cv2
import functools
import json
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import time
import xml.etree.ElementTree as ET
import yaml
from object_detection.utils import dataset_util
from PIL import Image
from PIL import ImageDraw
from PIL... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D3_BiologicalNeuronModels/student/W2D3_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 1: The Leaky Integrate-and-Fire (L... | github_jupyter |
```
from sympy import symbols, pprint
from sympy import diff
from sympy.solvers import solve
import numpy as np
from scipy import optimize
import string
import random
from autodp.transformer_zoo import Composition
from functools import lru_cache
# data subject
class Entity():
def __init__(self, name="", id=None):
... | github_jupyter |
# Self-orginizing maps
Self-orginizing map (SOM) is a type of neural network which is trained using unsupervised learning algorithms. One of the basic abilities of SOM is to project high-dimensional data to lower dimension (1D, 2D, 3D obviously). SOM can be considered as a general cluster analysis tool.
Scheme of 2D ... | github_jupyter |
<a href="https://colab.research.google.com/github/daveluo/covid19-healthsystemcapacity/blob/master/nbs/usa_beds_capacity_analysis_20200313_v2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!apt-get install python3-rtree
!pip install geopandas
i... | github_jupyter |
```
#Load libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from pandas import read_csv
from pandas import set_option
from matplotlib import pyplot
from pandas import read_csv
from pandas import set_option
from matplotlib import pyplot as plt
import seaborn
HOME_PATH = '... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from urllib.request import Request, urlopen
from IPython.display import Markdown as md
%matplotlib inline
```
# Data explorations
(c) Carlos Contreras, August 2021
## Load data
```
df_comor = pd.read_csv('../../data/AH... | github_jupyter |
<!--
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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
#
# ... | github_jupyter |
An example showing univariate feature selection.
Noisy (non informative) features are added to the iris data and univariate feature selection is applied. For each feature, we plot the p-values for the univariate feature selection and the corresponding weights of an SVM. We can see that univariate feature selection sel... | github_jupyter |
# AWS Glue Notebook for Serverless Data Lake Workshop
This notebook contains the PySpark scripts run in AWS Glue to transform the data in the data lake. Each section refers a section in the lab.
## Initialization
The first two sections initialize the Spark environment and only need to be run once. The first block may... | github_jupyter |
```
import os
import sys
import glob
import torch
import numpy as np
import pydicom as dicom
from skimage.draw import polygon
import matplotlib.pyplot as plt
%matplotlib inline
def read_structure(structure):
contours = []
for i, ri in enumerate(structure.ROIContourSequence):
contour = {}
#ret... | github_jupyter |
# Interactive experimentation
```
!pip install --upgrade lightgbm scikit-learn pandas adlfs
```
## Setup cloud tracking
```
import mlflow
from azureml.core import Workspace
ws = Workspace.from_config()
mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
mlflow.set_experiment("untitled")
```
## Load data
You can... | github_jupyter |
# AttnGAN
## Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_AttnGAN_Fine-Grained_Text_CVPR_2018_paper.pdf
https://github.com/taoxugit/AttnGAN
---
## TODO
- run le code en debug dans IntelliJ
- indiquer les shapes dans... | github_jupyter |
# Adversarial Attacks with parametrized DPR on VGGFace2
```
from torch.autograd import Variable
%load_ext autoreload
%autoreload 2
import os.path
import sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath('__file__')), '..'))
from relighters.DPR.model.defineHourglass_512_gray_skip import HourglassNet
... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from rules import normalized_chars
import random
import re
from unidecode import unidecode
laughing = {
'huhu',
'haha',
'gagaga',
'hihi',
'wkawka',
'wkwk',
'kiki',
'keke',
'huehue',
'hshs',... | github_jupyter |
```
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
```
# Tiempo de mezcla (mixing time)
Previamente hemos visto como diseñar una cadena de Markov finita tal que converja a una distribución estacionaria de nuestro interés
Pero ¿Cuánto debemos esperar para que ocurra la conv... | github_jupyter |
```
import pandas as pd
import os
df = pd.read_table('linhas_dr.txt', delim_whitespace = True)
df.head(5)
columns = ["Hbeta", "OIII.4959", "OIII.5007", "NII.6548", "Halpha", "NII.6584", "SII.6716", "SII.6731", "mag_r"]
for column in columns:
if column == "Hbeta":
df_final = df["Hbeta"]
else:
df_fin... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.preprocessing import M... | github_jupyter |
<a href="https://colab.research.google.com/github/michelucci/zhaw-dlcourse-spring2019/blob/master/Week%203%20-%20Computational%20graphs/Week%203%20-%20Exercises%20Solutions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neural Networks and Deep L... | github_jupyter |
# Systematic correction of protein distribution moments
(c) 2020 Manuel Razo. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT)
---
```
import ... | github_jupyter |
# QuakeMigrate - Example - Icequake detection
## Overview:
This notebook shows how to run QuakeMigrate for icequake detection, using a 2 minute window of continuous seismic data from Hudson et al (2019). Please refer to this paper for details and justification of the settings used.
Here, we detail how to:
1. Create ... | github_jupyter |
# Homework 1
*This notebook includes both coding and written questions. Please hand in this notebook file with all the outputs and your answers to the written questions.*
This assignment covers linear filters, convolution and correlation
```
# Setup
import numpy as np
import matplotlib.pyplot as plt
from time import ... | github_jupyter |
## Stereo Vision
```
# import libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
from math import sqrt
# Read sample images
left = cv2.imread('images/l4.png', 0)
right = cv2.imread('images/r4.png', 0)
plt.figure(figsize=(15,10))
ax1 = plt.subplot(121)
ax1.imshow(left, cmap='... | github_jupyter |
# Implementing an RNN in TensorFlow
----------------------------------
This script implements an RNN in TensorFlow to predict spam/ham from texts.
We start by loading the necessary libraries and initializing a computation graph in TensorFlow.
```
import os
import re
import io
import requests
import numpy as np
impor... | github_jupyter |
# Classificador de Raças de Cachorros usando Tensorflow e Keras
Neste notebook iremos implementadar um modelo para classificação de imagens. Classificação é uma das "tarefas" em que podemos utilizar Machine Learning, nesta tarefa o ensino é **supervisionado**, em outras palavras nós vamos ensinar ao modelo através de... | github_jupyter |
<a href="https://colab.research.google.com/github/graviraja/100-Days-of-NLP/blob/applications%2Fgeneration/applications/generation/utterance_generation/Basic%20Utterance%20Generation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
TASK_DATA_DIR ... | github_jupyter |
### BCO-DMO Knowledge Graph Data Exploration Prototype
This is a prototype demonstrating how python can be used to interactively explore oceanographic data within the BCO-DMO Knowledge Graph. This demonstration was developed for SciPy 2020.
**WARNING** This is just a prototype and will likely be updated (or abandoned... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Initial Data for Solving Maxwell's Equations in Flat Spac... | github_jupyter |
```
import pandas as pd
import numpy as np
import pandas as pd
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import math
import seaborn as sns
import matplotlib.colors as mcolors
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.formula.api import ols
from sta... | github_jupyter |
# Convolutional Neural Networks
*by Marvin Bertin*
<img src="../../images/keras-tensorflow-logo.jpg" width="400">
## Convolutional Neural Networks (CNNs)
Convolutional Neural Networks are very similar to ordinary (fully connected) Neural Networks. They are made up of neurons that have learnable weights and biases. E... | github_jupyter |
# Recurrent Neural Networks
When working with sequential data (time-series, sentences, etc.) the order of the inputs is crucial for the task at hand. Recurrent neural networks (RNNs) process sequential data by accounting for the current input and also what has been learned from previous inputs. In this notebook, we'll... | github_jupyter |
# FormantNet Configuration Code
This code is used to parse the configuration file, if one exists, and save the global variables used by FormantNet into one object, referred to as **cfg** in the other scripts and passed around from function to function.
```
import configparser
class configuration(object):
def __... | github_jupyter |
```
##################################################################
#《Python机器学习及实践：从零开始通往Kaggle竞赛之路（2023年度版）》开源代码
#-----------------------------------------------------------------
# @章节号：6.8.2.1（批量标准化的PyTorch实践）
# @作者：范淼 ... | github_jupyter |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, CuDNNLSTM, CuDNNGRU, BatchNormalization, LocallyConnected2D, ... | github_jupyter |
## Setup a classification experiment
```
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
df = pd.read_csv(
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
header=None)
df.columns = [
"Age", "WorkClass", "fnlwgt... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Visualization/ndwi_symbology.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" ... | github_jupyter |
```
import sys, glob, os
SPARK_HOME=os.environ['SPARK_HOME']
sys.path.append(SPARK_HOME + "/python")
sys.path.append(glob.glob(SPARK_HOME + "/python/lib/py4j*.zip")[0])
from pyspark import SparkConf
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.window import Window, Window... | github_jupyter |
# pinkfish-challenge
Buy on the close on the SAME day a new 20 day high is set
```
# use future imports for python 3.x forward compatibility
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
# other imports
import pand... | github_jupyter |
# Tutorial
This is a very basic tutorial of segmentation and reconstruction in SEM. Here, we use a simple 2-d embedding space as it is easy to visualize. For the purpose of this tutorial, we do not consider structured embedding space (the HRR).
```
# ## un-comment out if running locally
# import os
# os.chdir('../'... | github_jupyter |
MNIST contains 70,000 images of handwritten digits: 60,000 for training and 10,000 for testing. The images are grayscale, 28x28 pixels.
```
import matplotlib.pyplot as plt
%matplotlib inline
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
import sys
import tensorflow as tf
impo... | github_jupyter |
# 圖論（Graph Theory）
This work by Jephian Lin is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
_Tested on SageMath version 8.7_
## 圖
一個__圖__ $G$ 由
一些**點**
還有一些__邊... | github_jupyter |
# Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than writing "Congratulations on the promotion! Lets get coffee and talk... | github_jupyter |
# Машинное обучение, ФКН ВШЭ
## Практическое задание 2. KNN. Exploratory Data Analysis и линейная регрессия
### Оценивание и штрафы
Каждая из задач имеет определенную «стоимость» (указана в скобках около задачи). Максимально допустимая оценка за работу — 10 баллов. Проверяющий имеет право снизить оценку за неэффектив... | github_jupyter |
# Variational Auto Encoders using Ignite
This is a tutorial on using Ignite to train neural network models, setup experiments and validate models.
In this experiment, we'll be replicating [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114) by Kingma and Welling. This paper uses an encoder-decoder archi... | github_jupyter |
# Analysis of single-cell transcriptomics
This tutorial demonstrates how to analyze single-cell transcriptomics data using LANTSA including
* Clustering & visualization
* Cell type marker genes
```
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import lantsa
```
## Read t... | github_jupyter |
```
import numpy as np
from mlp.layers import BatchNormalizationLayer
test_inputs = np.array([[-1.38066782, -0.94725498, -3.05585424, 2.28644454, 0.85520889,
0.10575624, 0.23618609, 0.84723205, 1.06569909, -2.21704034],
[ 0.11060968, -0.0747448 , 0.56809029, 2.45926149, -2.28677816,
-0.99... | github_jupyter |
# Задание 2.2 - Введение в PyTorch
Для этого задания потребуется установить версию PyTorch 1.0
https://pytorch.org/get-started/locally/
В этом задании мы познакомимся с основными компонентами PyTorch и натренируем несколько небольших моделей.<br>
GPU нам пока не понадобится.
Основные ссылки:
https://pytorch.org/t... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
from pathlib import Path
sys.path.append(str(Path('.').resolve().parents[0]))
from pprint import pprint
from collections import Counter
import numpy as np
import pandas as pd
import sklearn
from imblearn.under_sampling import RandomUnderSampler, NearMiss, EditedNearest... | github_jupyter |
# Test notebook Meteorites
```
from pathlib import Path
import numpy as np
import pandas as pd
import requests
from IPython.display import display
from IPython.utils.capture import capture_output
import pandas_profiling
from pandas_profiling.utils.cache import cache_file
file_name = cache_file(
"meteorites.csv",... | github_jupyter |
#### MicroSoft MSVC cl
```
//---------------------------
//%runinterm
//%term:c:\Windows\System32\cmd.exe /c start
//%execfile:src\test.exe
//---------------------------
//%ccompiler:cl
//%cflags: /Fe:src\test.exe /source-charset:utf-8
//%ldflags:/execution-charset:utf-8
//---------------------------
//%overwritefile
... | github_jupyter |
# Section 4: Case Study I - U-Net for Building Mapping
Now let's move in to a little advance model call U-Net. U-Net is popular in satellite image analysis (remote sensing) community. It’s very elegant and simple model that can be used to perform semantic segmentation task (labelling each pixel) well.
In this section... | github_jupyter |
# バッチ推論サービスを作成する
健康クリニックは一日中患者の測定を取り、各患者の詳細を別々のファイルに保存すると想像してください。その後、一晩で糖尿病予測モデルを使用して、その日のすべての患者データをバッチとして処理し、翌朝待つ予測を生成し、糖尿病のリスクがあると予測される患者をフォローアップできるようにします。Azure Machine Learning では、*バッチ推論パイプライン*を作成することでこれを実現できます。そして、この演習ではそれを実施します。
## ワークスペースに接続する
作業を開始するには、ワークスペースに接続します。
> **注**: Azure サブスクリプションでまだ認証済みのセッ... | github_jupyter |
#$EXERCISE_PREAMBLE$
As before, don't forget to run the setup code below before jumping into question 1.
```
# SETUP. You don't need to worry for now about what this code does or how it works.
from learntools.core import binder; binder.bind(globals())
from learntools.python.ex2 import *
print('Setup complete.')
```
... | github_jupyter |
Sascha Spors,
Professorship Signal Theory and Digital Signal Processing,
Institute of Communications Engineering (INT),
Faculty of Computer Science and Electrical Engineering (IEF),
University of Rostock,
Germany
# Data Driven Audio Signal Processing - A Tutorial with Computational Examples
Winter Semester 2021/22 (M... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.