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# Grover's Algorithm
In this section, we introduce Grover's algorithm and how it can be used to solve unstructured search problems. We then implement the quantum algorithm using Qiskit, and run on a simulator and device.
## Contents
1. [Introduction](#introduction)
2. [Example: 2 Qubits](#2qubits)
2.1 [Simul... | github_jupyter |
# Controle de versão
## Objetivos
- Entender o que um Sistema de Controle Versões (SCV) e o versionamento de código
- Compreender benefícios de SCVs para arquivos diversos (reprodutibilidade, auditabilidade, compartilhamento, entre outros)
- Ter uma visão geral sobre os SCVs mais comuns
- Entender o funcionamento do ... | github_jupyter |
# Introduction
According to the Efficient Market Hypothesis, markets take into account all relevant information in efficiently pricing securities [@Fama1970]. While many studies have explored this supposition under its strong, semi-strong and weak form, the growing volume, velocity and variety of market data has forced... | github_jupyter |
# PAMAP 2 Model 1: Artificial Neural Network
#### Dataset Source: https://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring
This is the same model as our ANN, but tested on the dataset PAMAP2 to validate the architecture of our model. This does utilize the sliding window function.
INPUT: pamap2_clea... | github_jupyter |
**[Python Home Page](https://www.kaggle.com/learn/python)**
---
# Try It Yourself
Functions are powerful. Try writing some yourself.
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 learn... | github_jupyter |
```
import os
import sys
import matplotlib.pyplot as plt
import IPython.display as ipd
import pandas as pd
import re
import subprocess
import numpy as np
import math
%load_ext autoreload
%autoreload 2
%matplotlib inline
sys.path.append('../src')
pred_path = './fusion/'
subm2 = {
'name': 'subm2',
'folds': 4,
}... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Trimodal%20People%20Segmentation%20Dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Table of contents... | github_jupyter |
# Evaluation of Seq2Seq Models
```
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, LogitsProcessorList, MinLengthLogitsProcessor, TopKLogitsWarper, TemperatureLogitsWarper, BeamSearchScorer
import torch
import datasets
import pickle
import seaborn as sns
import matplotlib.pyplot a... | github_jupyter |
# Training Neural Networks with Keras
### Goals:
- Intro: train a neural network with `tensorflow` and the Keras layers
### Dataset:
- Digits: 10 class handwritten digits
- http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits
```
%matplotlib inline
# displ... | github_jupyter |
Let us import some Python libraries that will help us load, manipulate, analyse and perform machine learning algorithms on the data.
```
import pandas as pd #Data Manipulation
import numpy as np
import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler #... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
... | github_jupyter |
URL: http://matplotlib.org/examples/showcase/bachelors_degrees_by_gender.html
Most examples work across multiple plotting backends equivalent, this example is also available for:
* [Bokeh - bachelors_degress_by_gender](../bokeh/bachelors_degress_by_gender.ipynb)
```
import holoviews as hv
from holoviews import opts
... | github_jupyter |
# Bias
### Goals
In this notebook, you're going to explore a way to identify some biases of a GAN using a classifier, in a way that's well-suited for attempting to make a model independent of an input. Note that not all biases are as obvious as the ones you will see here.
### Learning Objectives
1. Be able to distin... | github_jupyter |
# FINAL PROJECT for CS 634
## Name: Veena Chaudhari
## Topic: Predicting whether an individual is obese or not based on their eating habits and physical condition
Github link: https://github.com/vac38/Classification_of_obesity.git
Link to dataset: https://archive.ics.uci.edu/ml/datasets/Estimation+of+obesity+leve... | github_jupyter |
# Feature Extraction and Selection
This basic example shows how to use [tsfresh](https://tsfresh.readthedocs.io/) to extract useful features from multiple timeseries and use them to improve classification performance.
We use the robot execution failure data set as an example.
```
%matplotlib inline
import matplotli... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.cm import get_cmap
import pickle
import os
font = {'family' : 'sans-serif',
'size' : 12}
rc('font', **font)
import sys
sys.path.insert(0,'..')
from mavd.callbacks import *
l... | github_jupyter |
# Activation functions
- toc:true
- badges: true
- comments: true
- author: Pushkar G. Ghanekar
- categories: [python, machine-learning, pytorch]
Function that activates the particular neuron or node if the value across a particular threshold. These functions add the necessary non-linearity in the ANNs. Each percept... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, neighbors
from matplotlib.colors import ListedColormap
def knn_comparison(data, n_neighbors = 15):
'''
This function finds k-NN and plots the data.
'''
X = data[:, :2]
y = data[:,2]
# grid cell siz... | github_jupyter |
```
% load_ext autoreload
% autoreload 2
% matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
from copy import deepcopy
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.model_selection impor... | github_jupyter |
```
import sys
sys.path.append('..')
from msidata.dataset_msi_features_with_patients import PreProcessedMSIFeatureDataset
from testing.logistic_regression import get_precomputed_dataloader
import matplotlib.pyplot as plt
from modules.deepmil import Attention
import torch
import pandas as pd
import os
# We need a model... | github_jupyter |
# Tutorial: Compressing Natural Language With an Autoregressive Machine Learning Model and `constriction`
- **Author:** Robert Bamler, University of Tuebingen
- **Initial Publication Date:** Jan 7, 2022
This is an interactive jupyter notebook.
You can read this notebook [online](https://github.com/bamler-lab/constric... | github_jupyter |
# Train a basic TensorFlow Lite for Microcontrollers model
This notebook demonstrates the process of training a 2.5 kB model using TensorFlow and converting it for use with TensorFlow Lite for Microcontrollers.
Deep learning networks learn to model patterns in underlying data. Here, we're going to train a network to... | github_jupyter |
```
import os
import io
import detectron2
# import some common detectron2 utilities
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
# import some common libraries
import numpy as np
imp... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
#plotting
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
#getting data
from pydob.exploratory import (
get_issuance_rates_df,
get_issuance_num_df,
get_issuance_rates_ty... | github_jupyter |
# Python sqlite3
```
Commit : DB에 영구적으로 쓰는 것
Rollback : Data가 수정되기 전으로 되돌리는 것
Sqlite3 Data Type : TEXT, NUMERIC, INTEGER, REAL, BLOB
```
1. DB 생성
2. 데이터 삽입
3. 데이터 삭제
4. 데이터 조회
5. 데이터 수정
## [1. DB 생성]
```
import sqlite3
# sqlite3 버전 확인
print('sqlite3.version : ', sqlite3.version)
import datetime
# 현재 날짜 생성
now = ... | github_jupyter |
#[How to run Object Detection and Segmentation on a Video Fast for Free](https://www.dlology.com/blog/how-to-run-object-detection-and-segmentation-on-video-fast-for-free/)
## Confirm TensorFlow can see the GPU
Simply select "GPU" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the c... | github_jupyter |
# Train without labels
_This notebook is part of a tutorial series on [txtai](https://github.com/neuml/txtai), an AI-powered semantic search platform._
Almost all data available is unlabeled. Labeled data takes effort to manually review and/or takes time to collect. Zero-shot classification takes existing large langu... | github_jupyter |
## [Troisi06](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.96.086601)
Charge-Transport Regime of Crystalline Organic Semiconductors: Diffusion Limited by Thermal Off-Diagonal Electronic Disorder. A. Troisi and G. Orlandi. *Phys. Rev. Lett.* **2006**, *96*, 086601
```
import os
for env in ["MKL_NUM_THREAD... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn import metrics
from ast import literal_eval
from mlxtend.plotting import plot_decision_regions
from sklearn i... | github_jupyter |
```
!pip install gdown
!gdown https://drive.google.com/uc?id=1nI47j3kVW-ZFcUAUSYJVp17wwIs-EpHC
!unzip Scrapping.zip
!rm -rf Scrapping.zip
import os
BASE_PATH = os.getcwd()
if not os.path.exists('/model'):
!mkdir model
if not os.path.exists('/logs'):
!mkdir logs
import os
train_dir = os.path.join('/content/Tr... | github_jupyter |
<a href="https://colab.research.google.com/github/wtsyang/dl-reproducibility-project/blob/master/new_cnnTrain.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Mount Google Drive
from google.colab import drive # import drive from google colab
R... | github_jupyter |
# Incremental modeling with decision optimization
This tutorial includes everything you need to set up decision optimization engines, build a mathematical programming model, then incrementally modify it.
You will learn how to:
- change coefficients in an expression
- add terms in an expression
- modify constraints and... | github_jupyter |
# Mask R-CNN Demo
A quick intro to using the pre-trained model to detect and segment objects.
```
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
# Import Mask RCNN... | github_jupyter |
```
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
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
%matplotlib inline
from google.c... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import keras
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
from sklearn.model_selection import train_test_split... | github_jupyter |
# Read Matrix Data
Read in the gene expression data sets.
```
## Download microarray matrix files from GEO
setwd("~/NLM_Reproducibility_Workshop/tb_and_arthritis/data")
url <- c("ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE54nnn/GSE54992/matrix/GSE54992_series_matrix.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/seri... | github_jupyter |
# Timeseries
Pandas started out in the financial world, so it naturally has strong support for timeseries data.
We'll look at some pandas data types and methods for manipulating timeseries data.
Afterwords, we'll use [statsmodels' state space framework](http://www.statsmodels.org/stable/statespace.html) to model times... | github_jupyter |
# Think Bayes
This notebook presents example code and exercise solutions for Think Bayes.
Copyright 2018 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignmen... | github_jupyter |
<a href="https://colab.research.google.com/github/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/03_PyTorch101/PyTorch101.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
;
# 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 |
# Spark with MLRun example
This example notebook demonstrates how to execute a spark job with MLRun.
Our spark job is a generic ETL job which pulls data from user-defined data sources, applies a SQL query on top of them, and writes the result to a user defined destination.
The definition of the input-sources should ... | github_jupyter |
```
import numpy as np
import functools
def evaluateOrderOne(vector):
'''
Input: vector
'''
return np.sum(vector)
def __evaluate3Dvector(v):
#print(v)
tmp = np.sum(v)
if tmp == 3:
return 30
elif tmp == 2:
return 0
elif tmp == 0:
return 28
else:
if... | github_jupyter |
# ONLOAD cells are called at beginning of the report
## Available global variables:
* es: elasticsearch connection
* replacementHT: a dictionary of replacement tags
* report: the report object
* params: the parameters (interval are plits in two name_start + name_end
```
#@ONLOAD
replacementHT["Author"]="Arnaud Marcha... | github_jupyter |
# Attack Password with Correlation Power Analysis IV.1 (CPA)
```
%run '../util/Metadata.ipynb'
print_metadata()
```
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Improving-the-code" data-toc-modified-id="Improving-the-code-1"><span class="toc-item-nu... | github_jupyter |
# "Wine Quality."
### _"Quality ratings of Portuguese white wines" (Classification task)._
## Table of Contents
## Part 0: Introduction
### Overview
The dataset that's we see here contains 12 columns and 4898 entries of data about Portuguese white wines.
**Метаданные:**
* **fixed acidity**
* **volatile... | github_jupyter |
# Wiener Filter Implementation in TensorFlow
To implement Wiener Filter functions, we are going to mimic its implementation in Scikit Image (see skimage.restoration.wiener) and in Tikhonet (see https://github.com/CosmoStat/ShapeDeconv/blob/master/python/DeepDeconv/utils/deconv_utils_FCS.py).
## Implementation in skim... | github_jupyter |
<center>
<img src="xeus-python.png" width="50%">
<h1>Python kernel based on xeus</h1>
</center>
# Simple code execution
```
a = 3
a
b = 89
def sq(x):
return x * x
sq(b)
print
```
# Redirected streams
```
import sys
print("Error !!", file=sys.stderr)
```
# Error handling
```
"Hello"
def dummy_funct... | github_jupyter |
# Linear basis function models with PyMC4
```
import logging
import pymc4 as pm
import numpy as np
import arviz as az
import tensorflow as tf
import tensorflow_probability as tfp
print(pm.__version__)
print(tf.__version__)
print(tfp.__version__)
# Mute Tensorflow warnings ...
logging.getLogger('tensorflow').setLeve... | github_jupyter |
# HPC 6.2 Automating the workflow on ARC
We are now going to transfer this workflow over to ARC, automating parts of it as we go.
The first step is to connect to ARC via `ssh`, as we are off campus we need to do this via `remote-access.leeds.ac.uk` first:
`ssh <username>@remote-access.leeds.ac.uk`
`ssh <username... | github_jupyter |
```
import tensorflow as tf
tf.config.experimental.list_physical_devices()
tf.test.is_built_with_cuda()
```
# Importing Libraries
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os.path as op
import pickle
import tensorflow as tf
from tensorflow import keras
from keras.models im... | github_jupyter |
## NLTK code examples
Python code examples to mirror lecture material
**Author List**: Sam Choi
**Original Sources**: http://nltk.org
**License**: Feel free to do whatever you want to with this code
Let's begin by importing NLTK and a couple sets of data. We'll import corp... | github_jupyter |
```
from PIL import Image
import os
import math
import random
import uuid
Image.MAX_IMAGE_PIXELS = None
# 处理的所有图片及结果存放的总目录
dir = "F:/study1/研一/Python-CommonCode/HappyTimes/ValentineDay/"
# 白底图片所在的路径
whiteImagePath = ["F:/study1/研一/Python-CommonCode/HappyTimes/ValentineDay/whitebackground.jpg"]
# 白底图片所在的目录
whiteGoalPath... | github_jupyter |
#### validation or kfold is not added and also data augmentation is not added yet. Mainly the Ensemble is not performed. Due for tomorrow.
```
%%capture
!pip install ../input/segmentation-models-wheels/efficientnet_pytorch-0.6.3-py3-none-any.whl
!pip install ../input/segmentation-models-wheels/pretrainedmodels-0.7.4-p... | github_jupyter |
# Stablecoin Billionaires<br> Descriptive Analysis of the Ethereum-based Stablecoin ecosystem
## by Anton Wahrstätter, 01.07.2020
# Analytics Part of the thesis
```
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from datetime import datetime
from collections import Counter
f... | github_jupyter |
# Selecting by Callable
This is the fourth entry in a series on indexing and selecting in pandas. In summary, this is what we've covered:
* [Basic indexing, selecting by label and location](https://www.wrighters.io/indexing-and-selecting-in-pandas-part-1/)
* [Slicing in pandas](https://www.wrighters.io/indexing-and-... | github_jupyter |
```
#@title Check GPU
#@markdown Run this to connect to a Colab Instance, and see what GPU Google gave you.
gpu = !nvidia-smi --query-gpu=gpu_name --format=csv
print(gpu[1])
print("The Tesla T4 and P100 are fast and support hardware encoding. The K80 and P4 are slower.")
print("Sometimes resetting the instance in the ... | github_jupyter |
```
# Load packages
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd
import os
import pickle
import time
import scipy as scp
import scipy.stats as scps
from scipy.optimize import differential_evolution
from scipy.optimize import minimize
from datetime import datetime
import ma... | github_jupyter |
```
import numpy as np
import pandas as pd
pd.set_option('precision', 1)
```
**Question 1** (25 points)
There is simulated data of 25,000 human heights and weights of 18 years old children at this URL
`http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_Data_Dinov_020108_HeightsWeights.html`
The original data has hei... | github_jupyter |
# Workshop 5 [Student]
This notebook will cover the following topics:
1. polymorphism
2. Object composition
3. list of objects
4. stacks and queues
## 5.1 Polymorphism (Follow):
**Learning Objectives:**
1. Understand how functions can have the same name but do different things
2. Understand how functions... | github_jupyter |
# Combining measurements
When we do a fit, we can have additional knowledge about a parameter from other measurements. This can be taken into account either through a simultaneous fit or by adding a constraint (subsidiary measurement).
## Adding a constraint
If we know a parameters value from a different measurement... | github_jupyter |
### Increasing accuracy for LL fault detection
```
from jupyterthemes import get_themes
import jupyterthemes as jt
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('chesterish')
import pandas as pd
data_10=pd.read_csv(r'D:\Acads\BTP\Lightly Loaded\Train10hz2.csv')
data_20=pd.read_csv(r'D:\Acads\BTP\Lightly... | github_jupyter |
```
#### ALL NOTEBOOK SHOULD HAVE SOME VERSION OF THIS #####################################
########################################################################################
%load_ext autoreload
%autoreload 2
import os
import sys
currentdir = os.getcwd()
# go to root directory. change the # of os.path.dirnames... | github_jupyter |
<a href="https://colab.research.google.com/github/chemicoPy/MACD-RSI-STOCHASTIC-strategy/blob/ccxt/MACD_RSI_STOCHASTIC_strategy_(ccxt).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install ccxt
!pip install pandas_ta
!pip install schedule... | github_jupyter |
# What are `TargetPixelFile` objects?
Target Pixel Files (TPFs) are a file common to Kepler/K2 and the TESS mission. They contain movies of the pixel data centered on a single target star.
TPFs can be thought of as stacks of images, with one image for every timestamp the telescope took data. Each timestamp is referre... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_ou... | github_jupyter |
# Milestone Project 2 - Walkthrough Steps Workbook
Below is a set of steps for you to follow to try to create the Blackjack Milestone Project game!
## Game Play
To play a hand of Blackjack the following steps must be followed:
1. Create a deck of 52 cards
2. Shuffle the deck
3. Ask the Player for their bet
4. Make sur... | github_jupyter |
# Income Prediction Explanations
We will use an SKLearn classifier built on the [1996 US Census DataSet](https://archive.ics.uci.edu/ml/datasets/adult) which predicts high (>50K$) or low (<=50K$) income based on the Census demographic data.
The Kf... | github_jupyter |
## 1.2 引领浪潮的LaTeX
### 1.2.1 LaTeX的出现
LaTeX是一款高质量的文档排版系统，LaTeX在读法上一般发作Lay-tek或者Lah-tek的音，而不是大家普遍认为的Lay-teks。LaTeX的历史可以追溯到1984年，在这一年里，兰波特博士作为早期开发者发布了LaTeX的最初版本。事实上，LaTeX完全是兰伯特博士的意外所得，他当年出于排版书籍的需要，在早先的文档排版系统TeX基础上新增了一些特定的宏包，为便于自己日后重复使用这些宏包，他将这些宏包构建成标准宏包。谁曾想，正是这些不经意间开发出来的宏包构成了LaTeX的雏形。
<p align="center">
<img align="mid... | github_jupyter |
# xspec Documentation (v1.3.2)
This ipython Notebook is intended to provide documentation for the linetools GUI named XSpecGUI.
Enjoy and feel free to suggest edits/additions, etc.
Here is a screenshot of the XSpecGUI in action:
```
from IPython.display import Image
Image(filename="images/xspec_example.png")
```
... | github_jupyter |
<a href="https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_multiple_stock_trading.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trad... | github_jupyter |
```
# Built-in libraries
from datetime import datetime, timedelta
# NumPy, SciPy and Pandas
import pandas as pd
import numpy as np
def hourly_dataset(name):
"""
Constants for time period with maximum number of buildings measured simultaneously in the BDG dataset.
For more details, go to old_files/RawFeatur... | github_jupyter |
# Convolutional Autoencoder
Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.
```
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import i... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
params = {
"legend.fontsize": "x-large",
"axes.labelsize": "x-large",
"axes.titlesize": "x-large",
"xtick.labelsize": "x-large",
"ytick.labelsize": "x-large",
"figure.facecolo... | github_jupyter |
```
import numpy as np
import higra as hg
from functools import partial
from scipy.cluster.hierarchy import fcluster
from ultrametric.optimization import UltrametricFitting
from ultrametric.data import load_datasets, show_datasets
from ultrametric.graph import build_graph, show_graphs
from ultrametric.utils import Exp... | github_jupyter |
# Building, Training and Evaluating Models with TensorFlow Decision Forests
## Overview
In this lab, you use TensorFlow Decision Forests (TF-DF) library for the training, evaluation, interpretation and inference of Decision Forest models.
## Learning Objective
In this notebook, you learn how to:
1. Train a binary ... | github_jupyter |
# 객담도말 결핵진단 딥러닝 모델
CNN 기반의 객담도말 결핵진단 딥러닝 모델 소스코드입니다.
```
import os
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers... | github_jupyter |
# Imports
Numpy import for array processing, python doesn’t have built in array support. The feature of working with native arrays can be used in python with the help of numpy library.
Pandas is a library of python used for working with tables, on importing the data, mostly data will be of table format, for ease manip... | github_jupyter |
# GPU random forest with data from Snowflake
<table>
<tr>
<td>
<img src="https://saturn-public-assets.s3.us-east-2.amazonaws.com/example-resources/rapids.png" width="300">
</td>
<td>
<img src="https://saturn-public-assets.s3.us-east-2.amazonaws.com/example-resources/... | github_jupyter |
# The purpose of this notebook
**UPDATE 1:** *In version 5 of this notebook, I demonstrated that the model is capable of reaching the LB score of 0.896. Now, I would like to see if the augmentation idea from [this kernel](https://www.kaggle.com/jiweiliu/lgb-2-leaves-augment) would help us to reach an even better score... | github_jupyter |
# Estimating the carbon content of marine bacteria and archaea
In order to estimate the characteristic carbon content of marine bacteria and archaea, we rely on two main methodologies - volume based estimates and amino acid based estimates.
## Volume-based estimates
We collected measurements of the characeteristic vo... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_header.png"></a>
# Using the ChiFinder Component
The `ChiFinder` component creates a map of the $\chi$ drainage network index from a digital elevation model. The $\chi$ index, described by Perron and Royden (2013), is a function of drai... | github_jupyter |
```
#|hide
#|skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
```
some dependencies to get the dataset
```
! pip install rarfile av
! pip install -Uq pyopenssl
```
# Tutorial - Using fastai on sequences of Images
> How to use fastai to train an image sequence to image sequence job.
This... | github_jupyter |
# Assignment 1.1 - Python 101
Python is an easy to learn, powerful programming language with efficient high-level data structures and object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development ... | github_jupyter |
<h1><center> Análise de pellets plásticos como ferramenta para o estudo de permanência de microplásticos em praias arenosas. </center></h1>
Juana Gerevini Bozzetto
# 03. Recortando Área de Interesse (Pellet)
- Recortar área de interesse
- Ver se o programa funciona para maior parte das imagens
```
import skimage
im... | github_jupyter |
# TensorFlow MNIST Tutorial
```
import tensorflow as tf
import numpy as np
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
```
Lets load MNIST data and check random image
```
mnist = input_data.read_data_sets('../../datasets/MNIST', one_hot=True)
batch = mnist.train.next_batch(1)
sample_image = ba... | github_jupyter |
# Treasury Bond Fund Simulator
This is based off of [longinvest's post on bogleheads][1]. He implemented it all in a spreadsheet (which is linked in the thread).
The goal is to calculate returns of a simulated bond fund given a bunch of interest rates. By having returns (instead of rates) we can perform backtesting w... | github_jupyter |
```
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.models import Sequential
model = Sequential()
#inputlayer : apply filters
model.add(Convolution2D(filters=32,
kernel_size=(3,3),
... | github_jupyter |
# Overview
This a notebook that inspects the results of a WarpX simulation.
# Instruction
Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`
```
# Import statements
import yt ; yt.funcs.mylog.setLevel(50)
import num... | github_jupyter |
```
import math
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Loading the data
def load_data():
from sklearn.model_selection import train_test_split
data = np.genfromtxt('time_temp_2016.tsv', delimiter='\t')
x = data[:, 0]
x = x.reshape((x.shape[0], 1))
y = data[:, ... | github_jupyter |
```
#default_exp data.core
#export
from local.torch_basics import *
from local.test import *
from local.data.load import *
from local.notebook.showdoc import *
```
# Data core
> Core functionality for gathering data
The classes here provide functionality for applying a list of transforms to a set of items (`TfmdList... | github_jupyter |
Tutorials table of content:
- [Tutorial 1: Run a first scenario](./Tutorial-1_Run_your_first_scenario.ipynb)
- Tutorial 2: Add contributivity measurements methods
- [Tutorial 3: Use a custom dataset](./Tutorial-3_Use_homemade_dataset.ipynb)
# Tutorial 2 : Exploring contributivity
With this example, we dive deeper ... | github_jupyter |
```
import numpy as np
import os
from codeStore import support_fun as spf
import importlib
# case 1, ecoli (head Force) model in finite pipe.
ellipse_centerx_list = [0.75, 0.5, 0]
rot_theta_list = np.linspace(0, 2, 11) # true theta=rot_theta_list*np.pi
rs1_list = (0.1, 0.2, 0.3)
PoiseuilleStrength_list = np.linspace(-... | github_jupyter |
# Spacy and SVM
```
import numpy as np
import pandas as pd
import spacy
from spacy.matcher import Matcher
from spacy.tokens import Span
from spacy import displacy
nlp=spacy.load("en_core_web_sm")
train=pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test=pd.read_csv('/kaggle/input/nlp-getting-started/test... | github_jupyter |
# Glyph objects
Logomaker uses the [glyph](https://logomaker.readthedocs.io/en/latest/Glyph.html) class to render individual glyphs. This class also allows the user to customize each glyph according to their needs. We begin by importing useful packages
```
import numpy as np
import pandas as pd
import matplotlib.pypl... | github_jupyter |
# First day!
Congratulations! It's your first day as a data scientist in the company! Your first project is to build a model for predicting if a movie will get a positive or negative review.
You need to start exploring your dataset. In order to create a function that will scan each movie review, you want to know how ma... | github_jupyter |
## Table Tutorial
[Table](https://hail.is/docs/0.2/hail.Table.html) is Hail's distributed analogue of a data frame or SQL table. It will be familiar if you've used R or `pandas`, but `Table` differs in 3 important ways:
- It is distributed. Hail tables can store far more data than can fit on a single computer.
- It... | github_jupyter |
<a href="https://colab.research.google.com/github/unicamp-dl/IA025_2022S1/blob/main/ex03/Guilherme_Pereira/Aula_3_Guilherme_Pereira.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Escreva aqui seu nome:
# PyTorch: Gradientes e Grafo Computacion... | github_jupyter |
# Factorization Machine
----
### Concept
- Factorization Machine is general predictor like SVM. FM calculate all of pair-wise interaction between variables. So, FM can overcome the situation `cold-start` because of pair-wise vector factorization. It works like latent vector, but break the independence of the interacti... | github_jupyter |
# Transfer Learning with CNN - Rodrigo Puerto Pedrera
Este documento corresponde a la entrega práctica final de la asignatura de cuarto curso: Computación bioinspirada. Corresponde a la entrega de dificultad media establecida. Trata sobre la realización de una red neuronal convolucional que se ajuste al dataset *CIFAR... | github_jupyter |
# Diseño de software para cómputo científico
----
## Unidad 1: Modelo de objetos de Python
<small><b>Source:</b> <a href="https://dbader.org/blog/python-dunder-methods">https://dbader.org/blog/python-dunder-methods</a></small>
### Agenda de la Unidad 1
---
- Clase 1:
- Diferencias entre alto y bajo nivel.
... | github_jupyter |
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