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# Cache Dataset Tutorial and Speed Test
This tutorial shows how to accelerate PyTorch medical DL program based on MONAI CacheDataset.
It's modified from the Spleen 3D segmentation tutorial notebook.
```
# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not... | github_jupyter |
# Random 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).*
## Auto Power Spectral Density
The (auto-) [power spectral dens... | github_jupyter |
# Politeness strategies in MT-mediated communication
In this notebook, we demo how to extract politeness strategies using ConvoKit's `PolitenessStrategies` module both in English and in Chinese. We will make use of this functionality to assess the degree to which politeness strategies are preserved in machine-translat... | github_jupyter |
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="https://cocl.us/topNotebooksPython101Coursera">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/TopAd.png" width="750" align="center">
</a>
</div>
<a href="https://cognit... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import pandas as pd
import os
import datetime as dt
from alpha_vantage.timeseries import TimeSeries
def getStoredData(srtdt, enddt, ticker):
#currently assumes that csv data is organised in format: Date,Open,High,Low,Close,Adj Close,Volume
#also assumes that the name of the csv is the same as that as the ti... | github_jupyter |
---
_You are currently looking at **version 1.5** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
---
# Assignment 3 - More... | github_jupyter |
Notebook to plot the histogram of the power criterion values of Rel-UME test.
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import freqopttest.tst as tst
import kmod
import kgof
import kgof.goftest as gof
# submodul... | github_jupyter |
# Integrated gradients for text classification on the IMDB dataset
In this example, we apply the integrated gradients method to a sentiment analysis model trained on the IMDB dataset. In text classification models, integrated gradients define an attribution value for each word in the input sentence. The attributions a... | github_jupyter |
```
#@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 agreed to in writing, software
# distributed u... | github_jupyter |
# analysis_cdo_nco_draft2017b.ipynb
## Purpose
Use CDO and NCO to analyse CESM simulation output from project [p17c-marc-comparison](https://github.com/grandey/p17c-marc-comparison).
## Requirements
- Climate Data Operators (CDO)
- NetCDF Operators (NCO)
- CESM output data, post-processed to time-series format, as de... | github_jupyter |
# A practical introduction to Reinforcement Learning
Most of you have probably heard of AI learning to play computer games on their own, a very popular example being Deepmind. Deepmind hit the news when their AlphaGo program defeated the South Korean Go world champion in 2016. There had been many successful attempts i... | github_jupyter |
```
#install.packages("caTools", repo="http://cran.itam.mx")
#R.version
path<- "C:/Users/Martin/Documents/Tareas UNISON/Termodinamica/Laboratorio/Informe 5/Datos"
setwd(path)
library("ggplot2")
library("reshape2")
library("dplyr")
library("plotly")
D1 <- read.csv("Datos1.csv" ,header=TRUE, sep="," , stringsAsFactors=FA... | github_jupyter |
## Convolutional Neural Networks
## Project: Write an Algorithm for a Dog Identification App
---
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/MyDrive/Research/AAAI/complexity/50_200/'
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
impor... | github_jupyter |
<a href="https://colab.research.google.com/github/MonitSharma/Learn-Quantum-Computing/blob/main/Circuit_Basics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install qiskit
```
# Qiskit Basics
```
import numpy as np
from qiskit import Qu... | github_jupyter |
This script performs analyses to check how many mice pass the currenty set criterion for ephys.
```
import datajoint as dj
dj.config['database.host'] = 'datajoint.internationalbrainlab.org'
from ibl_pipeline import subject, acquisition, action, behavior, reference, data
from ibl_pipeline.analyses.behavior import Psyc... | github_jupyter |
```
# %load ../../templates/load_libs.py
import sys
from pyspark.ml.classification import LogisticRegression, NaiveBayes, DecisionTreeClassifier, GBTClassifier, \
RandomForestClassifier
# set project directory for shared library
PROJECT_DIR='/home/jovyan/work/amazon-review-validator'
if PROJECT_DIR not in sys.path:... | github_jupyter |
Link for exercises
https://python-textbok.readthedocs.io/en/1.0/Classes.html
Classes and types are themselves objects, and they are of type type. You can find out the type of any object using the type function:
type(any_object)
The data values which we store inside an object are called attributes, and the functions ... | github_jupyter |
# Datasets processing
## Import and preprocessing
```
import pandas as pd
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings("ignore")
#INPS
ht_inps=pd.read_csv('../data/raw/Enti/INPS/Hashtags.csv')
ht_inps['type'] = 'hashtag'
mn_inps=pd.read_csv('../data/raw/Enti/INPS/Mentions.csv')... | github_jupyter |
# Pipeline Analysis for CSM Model
- Plot Heatmaps of the model results using Z-normalization
- CEZ/OEZ Pooled Patient Analysis
- CEZ/OEZ IRR Metric
```
import os
import sys
import collections
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
import scipy.stats
from sklearn.metri... | github_jupyter |
# Simple RNN
In this notebook, we're going to train a simple RNN to do **time-series prediction**. Given some set of input data, it should be able to generate a prediction for the next time step!
<img src='assets/time_prediction.png' width=40% />
> * First, we'll create our data
* Then, define an RNN in PyTorch
* Fin... | github_jupyter |
Welcome to day 5 of the Python Challenge! If you missed any of the previous days, here are the links:
- [Day 1 (syntax, variable assignment, numbers)](https://www.kaggle.com/colinmorris/learn-python-challenge-day-1)
- [Day 2 (functions and getting help)](https://www.kaggle.com/colinmorris/learn-python-challenge-day-2)... | github_jupyter |
# Basic Examples with Different Protocols
## Prerequisites
* A kubernetes cluster with kubectl configured
* curl
* grpcurl
* pygmentize
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](seldon_core_setup.ipynb) to setup Seldon Core with an ingress - either Ambassador or Istio.
Then port-forward ... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from scipy.spatial import distance
import scipy
import math
import scipy.spatial
from collections import Counter
treino = pd.read_csv("dados/3.fit", sep=" ")
treino.head()
teste = pd.read_csv("dados/3.test", sep=" ")
teste.hea... | github_jupyter |
Configurations:
* install tensorflow 2.1
* install matplotlib
* install pandas
* install scjkit-learn
* install nltk
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import re
from tensorflow import keras
from keras.models import Sequential
from keras.layers import De... | github_jupyter |
# Tutorial 4 - Setting parameter values
In [Tutorial 1](./Tutorial%201%20-%20How%20to%20run%20a%20model.ipynb) and [Tutorial 2](./Tutorial%202%20-%20Compare%20models.ipynb), we saw how to run a PyBaMM model with all the default settings. However, PyBaMM also allows you to tweak these settings for your application. In ... | github_jupyter |
<table align="center">
<td align="center"><a target="_blank" href="http://introtodeeplearning.com">
<img src="http://introtodeeplearning.com/images/colab/mit.png" style="padding-bottom:5px;" />
Visit MIT Deep Learning</a></td>
<td align="center"><a target="_blank" href="https://colab.research.google.c... | github_jupyter |
# Training the extended rough Bergomi model part 3
In this notebook we train a neural network for the extended rough Bergomi model for expiries in the range (0.03,0.12].
Be aware that the datasets are rather large.
### Load, split and scale the datasets
```
import os, pandas as pd, numpy as np
wd = os.getcwd()
# L... | github_jupyter |
# Lista 06 - Gradiente Descendente e Regressão Multivariada
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy.testing import *
plt.ion()
```
Hoje vamos fazer um gradiente descendente para uma regressão linear com múltiplas variáveis.
Para isso, utilizaremos a base de dados carro... | github_jupyter |
```
import pandas
import re
table_5_2018 = pandas.read_excel('Table_5_Offenses_Known_Offenders_Race_and_Ethnicity_by_Bias_Motivation_2018.xls')
new_5_2018 =table_5_2018.rename(columns = {'Table 5' : 'Bias Motivation'}).rename(columns = {'Unnamed: 1' : 'Total Offenses'}).rename(columns = {'Unnamed: 2' : "White"}).renam... | github_jupyter |
# An Introduction To `aima-python`
The [aima-python](https://github.com/aimacode/aima-python) repository implements, in Python code, the algorithms in the textbook *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. A typical module in the repository has the code for a single chapter in th... | github_jupyter |
# A case study in screening for new enzymatic reactions
In this example, we show how to search the KEGG database for a reaction of interest based on user requirements. At specific points we highlight how our code could be used for arbitrary molecules that the user is interested in. This is crucial because the KEGG dat... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as scio
import displayData as dd
import lrCostFunction as lCF
import oneVsAll as ova
import predictOneVsAll as pova
import scipy.optimize as opt
# Setup the parameters you will use for this part of the exercise
input_layer_size = 400 # 20x20 input... | github_jupyter |
# Get the data
```
from google.colab import drive
drive.mount('/content/gdrive')
from sklearn.linear_model import ElasticNet, Lasso, Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler,MinMaxScaler,StandardScaler
from sklearn.model_selection import KFold, cross_val_score, tr... | github_jupyter |
# Задание 1.1 - Метод К-ближайших соседей (K-neariest neighbor classifier)
В первом задании вы реализуете один из простейших алгоритмов машинного обучения - классификатор на основе метода K-ближайших соседей.
Мы применим его к задачам
- бинарной классификации (то есть, только двум классам)
- многоклассовой классификац... | github_jupyter |
This notebook was created to convert the original VQC notebook to follow the routines of the new Qiskit version.
```
import logging
import numpy as np
from sklearn.metrics import f1_score
import matplotlib.pyplot as plt
plt.style.use('dark_background')
import qiskit
from qiskit import IBMQ, Aer, QuantumCircuit
from q... | github_jupyter |
# API demonstration for paper of v1.0
_the LSST-DESC CLMM team_
Here we demonstrate how to use `clmm` to estimate a WL halo mass from observations of a galaxy cluster when source galaxies follow a given distribution (The LSST DESC Science Requirements Document - arXiv:1809.01669, implemented in `clmm`). It uses sev... | github_jupyter |
```
#export
from local.torch_basics import *
from local.test import *
from local.core import *
from local.layers import *
from local.data.all import *
from local.text.core import *
from local.notebook.showdoc import show_doc
#default_exp text.models.awdlstm
#default_cls_lvl 3
```
# AWD-LSTM
> AWD LSTM from [Smerity e... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# CI/CD - Make sure all notebooks respects our format policy
**Tags:** #naas
**Author:** [Maxime Jublou](https://www.linkedin.com/in/maximejublou/)
# Input
### Import libraries
```
import json
import glob
from rich... | github_jupyter |
# Concise Implementation of Softmax Regression
:label:`sec_softmax_concise`
Just as high-level APIs of deep learning frameworks
made it much easier
to implement linear regression in :numref:`sec_linear_concise`,
we will find it similarly (or possibly more)
convenient for implementing classification models. Let us stic... | github_jupyter |
# Bioinfomatic central script
```
# Source the utility functions file, which should be in the scripts folder with this file
source('scripts/meg_utility_functions.R')
source('scripts/load_libraries.R')
```
## USER Controls
First, we'll need to specify the location of important files on your machine.
You'll... | github_jupyter |
<a href="https://colab.research.google.com/github/Fuenfgeld/2022TeamADataManagementBC/blob/main/Tutorial-Metadaten/structureData_task.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Strukturelle Daten und Metadatenschema
#### REFERENCE MODEL FOR ... | github_jupyter |
# Predict H1N1 and Seasonal Flu Vaccines
## Preprocessing
### Import libraries
```
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
```
### Import data
```
features_raw_df = pd.read_csv("data/training_set_features.csv", index_col="respondent_id")
labels_raw_df = pd.read_csv... | github_jupyter |
In this chapter you will:
* Clean and prepare text data
* Build feature vectors from text documents
* Train a machine learning model to classify positive and negative movie reviews
* Work with large text datasets using out-of-core learning
```
## Will be working with movie reviews from IMDB database
## Dataset is 50,... | github_jupyter |
# QUANTUM PHASE ESTIMATION
This tutorial provides a detailed implementation of the Quantum Phase Estimation (QPE) algorithm using the Amazon Braket SDK.
The QPE algorithm is designed to estimate the eigenvalues of a unitary operator $U$ [1, 2];
it is a very important subroutine to many quantum algorithms, most famous... | github_jupyter |
# Módulo 4: APIs
## Spotify
<img src="https://developer.spotify.com/assets/branding-guidelines/logo@2x.png" width=400></img>
En este módulo utilizaremos APIs para obtener información sobre artistas, discos y tracks disponibles en Spotify. Pero primero.. ¿Qué es una **API**?<br>
Por sus siglas en inglés, una API es una... | github_jupyter |
# Data Inputs and Display Libraries
```
import pandas as pd
import numpy as np
import pickle
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
```
# Modeling Libraries
```
from sklearn.tree import De... | github_jupyter |
# Notes
This project requires the creation of an **assets** and **outputs** folder in the same directory as the notebook. The assets folder should contain the WikiLarge_Train.csv file available from [Kaggle](https://www.kaggle.com/c/umich-siads-695-predicting-text-difficulty).
Several files here are writting to the ... | github_jupyter |
## Computer Vision Learner
[`vision.learner`](/vision.learner.html#vision.learner) is the module that defines the [`cnn_learner`](/vision.learner.html#cnn_learner) method, to easily get a model suitable for transfer learning.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
```
## Transfer learning... | github_jupyter |
```
import numpy as np
import pandas as pd
import pyspark
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql.types import StringType
sqlContext = SQLContext(sc)
conf = SparkConf().setAppName("My App").setMaster("local[*]")
sc.stop()
sc = SparkContext(conf = conf)
```
## R... | github_jupyter |
# The Atoms of Computation
Programming a quantum computer is now something that anyone can do in the comfort of their own home.
But what to create? What is a quantum program anyway? In fact, what is a quantum computer?
These questions can be answered by making comparisons to standard digital computers. Unfortuna... | github_jupyter |
```
%cd -q data/actr_reco
import matplotlib.pyplot as plt
import tqdm
import numpy as np
with open("users.txt", "r") as f:
users = f.readlines()
hist = []
for user in tqdm.tqdm(users):
user = user.strip()
ret = !wc -l user_split/listening_events_2019_{user}.tsv
lc, _ = ret[0].split(" ")
hist.append(... | github_jupyter |
```
import pandas as pd
```
## Load in the "rosetta stone" file
I made this file using QGIS, the open-source mapping software. I loaded in the US Census 2010 block-level shapefile for Hennipin County. I then used the block centroids, provided by the census, to colect them within each zone. Since the centroids, by nat... | github_jupyter |
# Sample for KFServing SDK
This is a sample for KFServing SDK.
The notebook shows how to use KFServing SDK to create, get, rollout_canary, promote and delete InferenceService.
```
from kubernetes import client
from kfserving import KFServingClient
from kfserving import constants
from kfserving import utils
from kf... | github_jupyter |
# Assignment Submission for FMUP
## Kishlaya Jaiswal
### Chennai Mathematical Institute - MCS201909
---
# Solution 1
I have choosen the following stocks from Nifty50:
- Kotak Mahindra Bank Ltd (KOTAKBANK)
- Hindustan Unilever Ltd (HINDUNILVR)
- Nestle India Limited (NESTLEIND)
Note:
- I am doing these computations ... | github_jupyter |
# Sequana_coverage versus CNVnator (viral genome)
This notebook compares CNVnator, CNOGpro and sequana_coverage behaviour on a viral genome instance (same as in the virus notebook).
Versions used:
- sequana 0.7.0
```
%pylab inline
matplotlib.rcParams['figure.figsize'] = [10,7]
```
Here below, we provide the result... | github_jupyter |
<a href="https://colab.research.google.com/github/gcfer/reinforcement-learning/blob/main/RL_A2C_2N_TF2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Reinforcement Learning: A2C (Actor-Critic Method) — Two Networks
## Overview
In this notebook,... | github_jupyter |
# QCoDeS Example with Lakeshore 325
Here provided is an example session with model 325 of the Lakeshore temperature controller
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from qcodes.instrument_drivers.Lakeshore.Model_325 import Model_325
lake = Model_325("lake", "GPIB0::12::INSTR")
`... | github_jupyter |
Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad.
Create a convolutional neural network that trains to 100% accuracy on these images, which cancels training upon hitting training accuracy of >.999
Hint -- it will work best with 3 convolutional layers.
```
import tens... | github_jupyter |
## Single image processing [resize, crope]
```
import numpy as np
#from PIL import Image
import os, glob
import cv2
pic = cv2.imread('../../../data/data/1_d.jpg')
#img = cv2.cvtColor(pic, cv2.COLOR_GRAY2RGB)
# cv2.imshow('image', pic)
# cv2.waitKey(0)
iw, ih = pic.shape[0:2]
w = h = 256
ul_img = pic[:h, :w, :]
ur_im... | github_jupyter |
```
import glob
import os.path as osp
import random
import numpy as np
import json
from PIL import Image
from tqdm import tqdm
import matplotlib.pyplot as plt
%matplotlib inline
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision
from torchvision import mod... | github_jupyter |
# Capstone Part 2a - Classical ML Models (MFCCs with Offset)
___
## Setup
```
# Basic packages
import numpy as np
import pandas as pd
# For splitting the data into training and test sets
from sklearn.model_selection import train_test_split
# For scaling the data as necessary
from sklearn.preprocessing import Standar... | github_jupyter |
# Minimal tutorial on packing and unpacking sequences in PyTorch, aka how to use `pack_padded_sequence` and `pad_packed_sequence`
This is a jupyter version of [@Tushar-N 's gist](https://gist.github.com/Tushar-N/dfca335e370a2bc3bc79876e6270099e) with comments from [@Harsh Trivedi repo](https://github.com/HarshTrivedi... | github_jupyter |
```
! pip install opencv-python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cv2
#tensorflow packages
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
# Face Emotion Recognition
#Here i am using my trained model, that is trained and saved a... | github_jupyter |
```
import pandas as pd
try:
import pickle5 as pickle
except:
!pip install pickle5
import pickle5 as pickle
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.l... | github_jupyter |
```
from mplsoccer import Pitch, VerticalPitch
from mplsoccer.dimensions import valid, size_varies
import matplotlib.pyplot as plt
import numpy as np
import random
np.random.seed(42)
```
# Test five points are same in both orientations
```
for pitch_type in valid:
if pitch_type in size_varies:
kwargs = {'... | github_jupyter |
# Pipelines for classifiers using Balanced Accuracy
For each dataset, classifier and folds:
- Robust scaling
- 2, 3, 5, 10-fold outer CV
- balanced accurary as score
We will use folders *datasets2* and *results2*.
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
# remove warnings
import warnings
warnings... | github_jupyter |
|<img style="float:left;" src="http://pierreproulx.espaceweb.usherbrooke.ca/images/usherb_transp.gif" > |Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
### Section 10.6, Conduction de la chaleur dans une sphère
```
#
# Pie... | github_jupyter |
# TTI pure qP-wave equation implementation
The aim of this notebook is to show how to solve the pure qP-wave equation using the finite-difference (FD) scheme. The 2D TTI pure qP-wave equation can be written as ([Mu et al., 2020](https://library.seg.org/doi/10.1190/geo2019-0320.1))
$$\begin{align}
\frac{1}{v_{p}^{2}}\... | github_jupyter |
# 1-1 Intro Python Practice
## Getting started with Python in Jupyter Notebooks
### notebooks, comments, print(), type(), addition, errors and art
<font size="5" color="#00A0B2" face="verdana"> <B>Student will be able to</B></font>
- use Python 3 in Jupyter notebooks
- write working code using `print()` and `#` comm... | github_jupyter |
# An exercise in discretisation and the CFL criterion
*These notebooks have been built from Lorena Barba's Computational Fluid Dynamics module. Here we are going to go from a (simple) equation, to a numerical solution of it. We are then going to look at how changing the resolution impacts the speed and validity of the ... | github_jupyter |
```
import plotly.express as px
import pandas as pd
import plotly.graph_objects as go
import pickle
from plotly.subplots import make_subplots
import numpy as np
import os
import Loader
from scipy.spatial import ConvexHull, distance_matrix
loader = Loader.Loader(r"C:\Users\logiusti\Lorenzo\Data\ups")
def remap(x):
... | github_jupyter |
Week 7 Notebook: Optimizing Other Objectives
===============================================================
This week, we will look at optimizing multiple objectives simultaneously. In particular, we will look at pivoting with adversarial neural networks {cite:p}`Louppe:2016ylz,ganin2014unsupervised,Sirunyan:2019nfw`... | github_jupyter |
## TASK-1: Make a class to calculate the range, time of flight and horizontal range of the projectile fired from the ground.
## TASK-2: Use the list to find the range, time of flight and horizontal range for varying value of angle from 1 degree to 90 dergree.
## TASK-3: Make a plot to show the variation of range, tim... | github_jupyter |
#Improving Computer Vision Accuracy using Convolutions
In the previous lessons you saw how to do fashion recognition using a Deep Neural Network (DNN) containing three layers -- the input layer (in the shape of the data), the output layer (in the shape of the desired output) and a hidden layer. You experimented with t... | github_jupyter |
DeepLarning Couse HSE 2016 fall:
* Arseniy Ashuha, you can text me ```ars.ashuha@gmail.com```,
* ```https://vk.com/ars.ashuha```
* partially reusing https://github.com/ebenolson/pydata2015
<h1 align="center"> Image Captioning </h1>
In this seminar you'll be going through the image captioning pipeline.
To begin wi... | github_jupyter |
<a href="https://colab.research.google.com/github/ZinnurovArtur/Colour-Match/blob/main/Outfit_neural_network.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
i... | github_jupyter |
# Edge Computing using Tensorflow and Neural Compute Stick
## " Generate piano sounds using EEG capturing rhythmic activity of brain"
### Contents
#### 1. Motivation
#### 2. Signal acquisition
#### 3. Signal postprocessing
#### 4. Synthesize music
##### 4.1 Training Data
##### 4.2 Training data preprocess... | github_jupyter |
# Customizing and controlling xclim
xclim's behaviour can be controlled globally or contextually through `xclim.set_options`, which acts the same way as `xarray.set_options`. For the extension of xclim with the addition of indicators, see the [Extending xclim](extendxclim.ipynb) notebook.
```
import xarray as xr
impo... | github_jupyter |
# Predicting Student Admissions with Neural Networks in Keras
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: http://www.ats.ucla.edu/
## Loading the data
To lo... | github_jupyter |
```
# Import plotting modules
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
df = [4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, ... | github_jupyter |
# Introduction
## Research Question
What is the information flow from visual stream to motor processing and how early in processing can we predict behavioural outcomes.
- Can decoding models be trained by region
- How accurate are the modeled regions at predicting a behaviour
- Possible behaviours (correct vs. inco... | github_jupyter |
<center>
<img src="../../img/ods_stickers.jpg">
## Открытый курс по машинному обучению
<center>Автор материала: Michael Kazachok (@miklgr500)
# <center>Другая сторона tensorflow:KMeans
## <center>Введение
<p style="text-indent:20px;"> Многие знают <strong>tensorflow</strong>, как одну из лучших библиотек для обучен... | github_jupyter |
# Generating counterfactual explanations with any ML model
The goal of this notebook is to show how to generate CFs for ML models using frameworks other than TensorFlow or PyTorch. This is a work in progress and here we show a method to generate diverse CFs by independent random sampling of features. We use scikit-lea... | github_jupyter |
# NumPy
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulatio... | github_jupyter |
# Analisis de resultados encuesta conocimiento y actitudes ante el uso del achiote
#### Cargamos librerias a utilizar
```
library("dplyr")
library("tidytext")
library("tm")
library("ggplot2")
library("stringr")
library("corrplot")
library("cluster")
```
#### Leemos los archivo Csv
```
achiote1 <- read.csv("first_ch... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/figures/PDSH-cover-small.png?raw=1">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
# Santander Value Prediction Challenge
According to Epsilon research, 80% of customers are more likely to do business with you if you provide **personalized service**. Banking is no exception.
The digitalization of everyday lives means that customers expect services to be delivered in a personalized and timely manner... | 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 |
```
import sys, random, os, json
sys.path.append(sys.path.append(os.path.join(os.getcwd(), '..')))
from datamart.augment import Augment
import pandas as pd
es_index = "datamart"
augment = Augment(es_index=es_index)
```
### Initialize a dataframe
```
old_df = pd.DataFrame(data={
'city': ["los angeles", "New york"... | github_jupyter |
# 2022/01/01/SAT(HappyNewYear)
datail-review 해보자
- feature_names = 높이,가로 길이 이런 것들, data = 각 featuredml 값들, target = 0,1,2...예를 들면 붓꽃의 이름을 대용한 것, target_names = 각 target이 가리키는 이름이 무엇인지?
---
model_selection 모듈은 학습 데이터와 테스트 데이터 세트를 분리하거나 교차 검증 분할 및 평가, 그리고 Estimator의 하이퍼 파라미터를 튜닝하기 위한 다양한 함수와 클래스를 제공, 전체 데이터를 학습 데이터와 ... | github_jupyter |
```
from IPython import display
```
# What to expect from the Python lessons
- Get you started with python through a little project
- Showcase relevant use cases of python for exploratory data analysis
- Provide you with exercises during and after the lessons so that you practice and experience python
- Provide you w... | github_jupyter |
```
import numpy as np
from tqdm import tqdm
from time import time
import torchvision
from torchvision import models, transforms
import torch
from torch import nn
from torch.utils.tensorboard import SummaryWriter
def accuracy(yhat,y):
# si y encode les indexes
if len(y.shape)==1 or y.size(1)==1:
retur... | github_jupyter |
<a href="https://www.bigdatauniversity.com"><img src = "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/Images/CCLog.png" width = 300, align = "center"></a>
<h1 align=center><font size=5>Data Analysis with Python</font></h1>
<h1>Data Wrangling</h1>
<h3>Welcome!</h3>
By the ... | github_jupyter |
## PureFoodNet implementation
```
#libraries
from tensorflow import keras
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D
from tensorflow.keras.layers import MaxPool2D, BatchNormalizat... | github_jupyter |
```
from IPython.display import HTML
css_file = './custom.css'
HTML(open(css_file, "r").read())
```
# Norms and Distances
© 2018 Daniel Voigt Godoy
## 1. Definition
From [Wikipedia](https://en.wikipedia.org/wiki/Norm_(mathematics)):
...a norm is a function that assigns a strictly positive length or size to eac... | github_jupyter |
```
import numpy as np
from collections import defaultdict
from torch.utils import data
import matplotlib.pyplot as plt
%matplotlib inline
# Generate Dataset
np.random.seed(42)
def generate_dataset(num_sequences=2**8):
sequences = []
for _ in range(num_sequences):
token_length = np.random.randint(1, 12... | github_jupyter |
<a href="https://colab.research.google.com/github/macscheffer/DS-Sprint-01-Dealing-With-Data/blob/master/DS_Unit_1_Sprint_Challenge_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Data Science Unit 1 Sprint Challenge 1
## Loading, cleaning, vis... | github_jupyter |
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