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<a href="https://colab.research.google.com/github/towardsai/tutorials/blob/master/random-number-generator/random_number_generator_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Random Number Generator Tutorial with Python
* Tutorial: ... | github_jupyter |
<a href="https://colab.research.google.com/github/daveshap/QuestionDetector/blob/main/QuestionDetector.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Compile Training Data
Note: Generate the raw data with [this notebook](https://github.com/davesh... | github_jupyter |
# RadiusNeighborsClassifier with MinMaxScaler
This Code template is for the Classification task using a simple Radius Neighbor Classifier, with data being scaled by MinMaxScaler. It implements learning based on the number of neighbors within a fixed radius r of each training point, where r is a floating-point value sp... | github_jupyter |
# 03 - Stats Review: The Most Dangerous Equation
In his famous article of 2007, Howard Wainer writes about very dangerous equations:
"Some equations are dangerous if you know them, and others are dangerous if you do not. The first category may pose danger because the secrets within its bounds open doors beh... | github_jupyter |
# Gender Prediction, using Pre-trained Keras Model
Deep Neural Networks can be used to extract features in the input and derive higher level abstractions. This technique is used regularly in vision, speech and text analysis. In this exercise, we use a pre-trained model deep learning model that would identify low level... | github_jupyter |
# Carving Unit Tests
So far, we have always generated _system input_, i.e. data that the program as a whole obtains via its input channels. If we are interested in testing only a small set of functions, having to go through the system can be very inefficient. This chapter introduces a technique known as _carving_, w... | github_jupyter |
```
# Import Module
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import h5py
# Read data, which has a size of N * 784 and N * 1
MNIST = h5py.File("..\MNISTdata.hdf5",'r')
x_train = np.float32(MNIST['x_train'][:])
x_test = np.float32(MNIST['x_test'][:])
y_train = np.int32(MNIST['y_train'][:,0])... | github_jupyter |
# Attention Basics
In this notebook, we look at how attention is implemented. We will focus on implementing attention in isolation from a larger model. That's because when implementing attention in a real-world model, a lot of the focus goes into piping the data and juggling the various vectors rather than the concepts... | github_jupyter |
# Seasonal Accuracy Assessment of Water Observations from Space (WOfS) Product in Africa<img align="right" src="../Supplementary_data/DE_Africa_Logo_Stacked_RGB_small.jpg">
## Description
Now that we have run WOfS classification for each AEZs in Africa, its time to conduct seasonal accuracy assessment for each AEZ in ... | github_jupyter |
# Residual Networks
Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](h... | github_jupyter |
# Siamese Neural Network with Triplet Loss trained on MNIST
## Cameron Trotter
### c.trotter2@ncl.ac.uk
This notebook builds an SNN to determine similarity scores between MNIST digits using a triplet loss function. The use of class prototypes at inference time is also explored.
This notebook is based heavily on the ... | github_jupyter |
# Multi-class Classification and Neural Networks
## 1. Multi-class Classification
In this exercise, we will use logistic regression and neural networks to recognize handwritten digits (from 0 to 9).
### 1.1 Dataset
The dataset ex3data1.mat contains 5000 training examples of handwritten digits. Each training example ... | github_jupyter |
```
import construction as cs
import matplotlib.pyplot as plt
### read font
from matplotlib import font_manager
font_dirs = ['Barlow/']
font_files = font_manager.findSystemFonts(fontpaths=font_dirs)
for font_file in font_files:
font_manager.fontManager.addfont(font_file)
# set font
plt.rcParams['font.family'] =... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import *
from sklearn.linear_model import *
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict
### UTILITY FUNCTION FOR DATA GENERATION ###
def gen_sinusoidal(timesteps, amp, freq, noise... | github_jupyter |
# Plotting Categorical Data
In this section, we will:
- Plot distributions of data across categorical variables
- Plot aggregate/summary statistics across categorical variables
## Plotting Distributions Across Categories
We have seen how to plot distributions of data. Often, the distributions reveal new information... | github_jupyter |
# AWS Elastic Kubernetes Service (EKS) Deep MNIST
In this example we will deploy a tensorflow MNIST model in Amazon Web Services' Elastic Kubernetes Service (EKS).
This tutorial will break down in the following sections:
1) Train a tensorflow model to predict mnist locally
2) Containerise the tensorflow model with o... | github_jupyter |
```
import sys
sys.path.append('../transformers/')
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pickle
from tqdm import tqdm
from path_explain import utils
from plot.text import text_plot, matrix_interaction_plot, bar_inte... | github_jupyter |
# explore_data_gov_sg_api
## Purpose:
Explore the weather-related APIs at https://developers.data.gov.sg.
## History:
- 2017-05 - Benjamin S. Grandey
- 2017-05-29 - Moving from atmos-scripts repository to access-data-gov-sg repository, and renaming from data_gov_sg_explore.ipynb to explore_data_gov_sg_api.ipynb.
```... | github_jupyter |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | github_jupyter |
This model will cluster a set of data, first with KMeans and then with MiniBatchKMeans, and plot the results. It will also plot the points that are labelled differently between the two algorithms.
```
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans, KMeans
f... | 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>
# `GiRaFFE_NRPy`: Source Terms
## Author: Patrick Nelson
... | github_jupyter |
# Find Descriptors (Matching)
Similar to classification, VDMS supports feature vector search based on similariy matching as part of its API.
In this example, where we have a pre-load set of feature vectors and labels associated,
we can search for similar feature vectors, and query information related to it.
We will... | github_jupyter |
```
!sudo nvidia-persistenced
!sudo nvidia-smi -ac 877,1530
from IPython.core.display import display, HTML
display(HTML("<style>.container {width:95% !important;}</style>"))
from core import *
from torch_backend import *
colors = ColorMap()
draw = lambda graph: display(DotGraph({p: ({'fillcolor': colors[type(v)], 'to... | github_jupyter |
# Radius and mean slip of rock patches failing in micro-seismic events
When stresses in a rock surpass its shear strength, the affected rock volume will fail to shearing.
Assume that we observe a circular patch with radius $r$ on, e.g. a fault, and that this patch is affected by a slip with an average slip distance ... | github_jupyter |
# Color extraction from images with Lithops4Ray
In this tutorial we explain how to use Lithops4Ray to extract colors and [HSV](https://en.wikipedia.org/wiki/HSL_and_HSV) color range from the images persisted in the IBM Cloud Oject Storage. To experiment with this tutorial, you can use any public image dataset and uplo... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
```
import nltk
import re
import operator
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
```
The idea is generate more common sentences according to their word tagging. So the sentences will have the real structure written by lovecraft and composed by a list of most common word... | github_jupyter |
# Tutorial - Evaluate DNBs additional Rules
This notebook contains a tutorial for the evaluation of DNBs additional Rules for the following Solvency II reports:
- Annual Reporting Solo (ARS); and
- Quarterly Reporting Solo (QRS)
Besides the necessary preparation, the tutorial consists of 6 steps:
1. Read possible dat... | github_jupyter |
# SST-2
# Simple Baselines using ``mean`` and ``last`` pooling
## Librairies
```
# !pip install transformers==4.8.2
# !pip install datasets==1.7.0
# !pip install ax-platform==0.1.20
import os
import sys
sys.path.insert(0, os.path.abspath("../..")) # comment this if library is pip installed
import io
import re
import ... | github_jupyter |
```
%run ./dlt
%run ./dlt_workflow_refactored
from pyspark.sql import Row
import unittest
from pyspark.sql.functions import lit
import datetime
timestamp = datetime.datetime.fromisoformat("2000-01-01T00:00:00")
def timestamp_provider():
return lit(timestamp)
from pyspark.sql.functions import when, col
from pyspar... | github_jupyter |
# T1056.004 - Input Capture: Credential API Hooking
Adversaries may hook into Windows application programming interface (API) functions to collect user credentials. Malicious hooking mechanisms may capture API calls that include parameters that reveal user authentication credentials.(Citation: Microsoft TrojanSpy:Win32... | github_jupyter |
```
#from lab2.utils import get_random_number_generator
class BoxWindow:
"""[summary]"""
def __init__(self, args):
"""initialize the box window with the bounding points
Args:
args (np.array([integer])): array of the bounding points of the box
"""
self.bounds = arg... | github_jupyter |
# Introduction to Data Science
## From correlation to supervised segmentation and tree-structured models
Spring 2018 - Profs. Foster Provost and Josh Attenberg
Teaching Assistant: Apostolos Filippas
***
### Some general imports
```
import os
import numpy as np
import pandas as pd
import math
import matplotlib.pyl... | github_jupyter |
This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Find the kth to last element of a linked list.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cases)
* [Algo... | github_jupyter |
## Distinction of solid liquid atoms and clustering
In this example, we will take one snapshot from a molecular dynamics simulation which has a solid cluster in liquid. The task is to identify solid atoms and cluster them. More details about the method can be found [here](https://pyscal.readthedocs.io/en/latest/solidl... | github_jupyter |
## Download the Fashion-MNIST dataset
```
import os
import numpy as np
from tensorflow.keras.datasets import fashion_mnist
(x_train, y_train), (x_val, y_val) = fashion_mnist.load_data()
os.makedirs("./data", exist_ok = True)
np.savez('./data/training', image=x_train, label=y_train)
np.savez('./data/validation', imag... | github_jupyter |
```
import mackinac
import cobra
import pandas as pd
import json
import os
import numpy as np
# load ID's for each organisms genome
id_table = pd.read_table('../data/study_strain_subset_w_patric.tsv',sep='\t',dtype=str)
id_table = id_table.replace(np.nan, '', regex=True)
species_to_id = dict(zip(id_table["designation i... | github_jupyter |
This script loads behavioral mice data (from `biasedChoiceWorld` protocol and, separately, the last three sessions of training) only from mice that pass a given (stricter) training criterion. For the `biasedChoiceWorld` protocol, only sessions achieving the `trained_1b` and `ready4ephysrig` training status are collecte... | github_jupyter |
<h1>CI Midterm<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Q1-Simple-Linear-Regression" data-toc-modified-id="Q1-Simple-Linear-Regression-1">Q1 Simple Linear Regression</a></span></li><li><span><a href="#Q2-Fuzzy-Linear-Regression" data-toc-modified-id="Q2-Fuzzy-Linear-Re... | github_jupyter |
```
# fetching data online
import os
import tarfile
from six.moves import urllib
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
HOUSING_PATH = os.path.join("datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"
def fetch_housing_data(housing_url=HOUSING_URL,... | github_jupyter |
# 2016 Olympics medal count acquisition
In this notebook, we acquire the current medal count from the web.
# 1. List of sports
```
from bs4 import BeautifulSoup
import urllib
r = urllib.urlopen('http://www.bbc.com/sport/olympics/rio-2016/medals/sports').read()
soup = BeautifulSoup(r,"lxml")
sports_span = soup.findA... | github_jupyter |
```
import torch
from torch.nn import functional as F
from torch import nn
from pytorch_lightning.core.lightning import LightningModule
import pytorch_lightning as pl
import torch.optim as optim
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data... | github_jupyter |
**Chapter 7 – Ensemble Learning and Random Forests**
_This notebook contains all the sample code and solutions to the exercises in chapter 7._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb"... | github_jupyter |
# Using [vtreat](https://github.com/WinVector/pyvtreat) with Classification Problems
Nina Zumel and John Mount
November 2019
Note: this is a description of the [`Python` version of `vtreat`](https://github.com/WinVector/pyvtreat), the same example for the [`R` version of `vtreat`](https://github.com/WinVector/vtreat)... | github_jupyter |
```
# Copyright 2019 Google LLC
#
# 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 writi... | github_jupyter |
# Hypothesis Testing
```
set.seed(37)
```
## Student's t-test
The `Student's t-test` compares the means of two samples to see if they are different. Here is a `two-sided` Student's t-test.
```
x <- rnorm(1000, mean=0, sd=1)
y <- rnorm(1000, mean=1, sd=1)
r <- t.test(x, y, alternative='two.sided')
print(r)
```
Her... | github_jupyter |
```
!pip install confluent-kafka==1.7.0
from confluent_kafka.admin import AdminClient, NewTopic, NewPartitions
from confluent_kafka import KafkaException
import sys
from uuid import uuid4
bootstrap_server = "kafka:9092" # Brokers act as cluster entripoints
conf = {'bootstrap.servers': bootstrap_server}
a = AdminClient(... | github_jupyter |
```
#default_exp dispatch
#export
from fastcore.imports import *
from fastcore.foundation import *
from fastcore.utils import *
from nbdev.showdoc import *
from fastcore.test import *
```
# Type dispatch
> Basic single and dual parameter dispatch
## Helpers
```
#exports
def type_hints(f):
"Same as `typing.get_t... | github_jupyter |
# Scalars
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
```
## Integers
### Binary representation of integers
```
format(16, '032b')
```
### Bit shifting
```
format(16 >> 2, '032b')
16 >> 2
format(16 << 2, '032b')
16 << 2
```
### Overflow
In general, the computer representation of in... | github_jupyter |
#Introduction to the Research Environment
The research environment is powered by IPython notebooks, which allow one to perform a great deal of data analysis and statistical validation. We'll demonstrate a few simple techniques here.
##Code Cells vs. Text Cells
As you can see, each cell can be either code or text. To... | github_jupyter |
## GMLS-Nets: 1D Regression of Linear and Non-linear Operators $L[u]$.
__Ben J. Gross__, __Paul J. Atzberger__ <br>
http://atzberger.org/
Examples showing how GMLS-Nets can be used to perform regression for some basic linear and non-linear differential operators in 1D.
__Parameters:__</span> <br>
The key parameter... | github_jupyter |
```
from moviepy.editor import *
postedByFontSize=25
replyFontSize=35
titleFontSize=100
cortinilla= VideoFileClip('assets for Channel/assets for video/transicion.mp4')
clip = ImageClip('assets for Channel/assets for video/background assets/fondo_preguntas.jpg').on_color((1920, 1080))
final= VideoFileClip('assets for C... | github_jupyter |
# PoissonRegressor with StandardScaler & Power Transformer
This Code template is for the regression analysis using Poisson Regressor, StandardScaler as feature rescaling technique and Power Transformer as transformer in a pipeline. This is a generalized Linear Model with a Poisson distribution.
### Required Packages
... | 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 |
# This notebook helps you to do several things:
1) Find your optimal learning rate
https://docs.fast.ai/callbacks.html#LRFinder
2)
```
%reload_ext autoreload
%autoreload 2
import fastai
from fastai.callbacks import *
from torch.utils.data import Dataset, DataLoader
from models import UNet2d_assembled
import numpy as... | github_jupyter |
## Sampling
You can get a randomly rows of the dataset. It is very usefull in training machine learning models.
We will use the dataset about movie reviewers obtained of [here](http://grouplens.org/datasets/movielens/100k/).
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# read a dataset o... | github_jupyter |
# Plots of the total distance covered by the particles as a function of their initial position
*Author: Miriam Sterl*
We plot the total distances covered by the particles during the simulation, as a function of their initial position. We do this for the FES, the GC and the GC+FES run.
```
from netCDF4 import Dataset... | github_jupyter |
# Data-Sitters Club 8: Just the Code
This notebook contains just the code (and a little bit of text) from the portions of *[DSC 8: Text-Comparison-Algorithm-Crazy-Quinn](https://datasittersclub.github.io/site/dsc8/)* for using Euclidean and cosine distance with word counts and word frequencies, and running TF-IDF for ... | github_jupyter |
# Politician Activity on Facebook by Political Affiliation
The parameters in the cell below can be adjusted to explore other political affiliations and time frames.
### How to explore other political affiliation?
The ***affiliation*** parameter can be use to aggregate politicians by their political affiliations. The ... | github_jupyter |
# 目的：了解Python基本語法
1. [資料型別](#01)
2. [for-loop](#02)
3. [while-loop](#03)
4. [清單(list)](#04)
5. [tuple是什麼？](#05)
6. [Python特殊的清單處理方式](#06)
7. [if的用法](#07)
8. [以if控制迴圈的break和continue](#08)
9. [函數：將計算結果直接於函數內印出或回傳(return)出函數外](#09)
10. [匿名函數](#10)
11. [物件導向範例](#11)
12. [NumPy (Python中用於處理numerical array的套件)](#12)
13. [一維... | github_jupyter |
# SageMaker Batch Transform using an XgBoost Bring Your Own Container (BYOC)
In this notebook, we will walk through an end to end data science workflow demonstrating how to build your own custom XGBoost Container using Amazon SageMaker Studio. We will first process the data using SageMaker Processing, push an XGB algo... | github_jupyter |
# Operations on word vectors
Welcome to your first assignment of this week!
Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings.
**After this assignment you will be able to:**
- Load pre-trained word vectors, and measure similarity usi... | github_jupyter |
# 16 - Regression Discontinuity Design
We don't stop to think about it much, but it is impressive how smooth nature is. You can't grow a tree without first getting a bud, you can't teleport from one place to another, a wound takes its time to heal. Even in the social realm, smoothness seems to be the norm. You can't ... | github_jupyter |
```
# Import libraries and modules
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
print(np.__version__)
print(tf.__version__)
np.set_printoptions(threshold=np.inf)
```
# Local Development
## Arguments
```
arguments = {}
# File arguments.
arguments["train_file_pattern"] = "gs://m... | github_jupyter |
# Tidy Data
> Structuring datasets to facilitate analysis [(Wickham 2014)](http://www.jstatsoft.org/v59/i10/paper)
If there's one maxim I can impart it's that your tools shouldn't get in the way of your analysis. Your problem is already difficult enough, don't let the data or your tools make it any harder.
## The Ru... | github_jupyter |
# Module 3 Graded Assessment
```
"""
1.Question 1
Fill in the blanks of this code to print out the numbers 1 through 7.
"""
number = 1
while number <= 7:
print(number, end=" ")
number +=1
"""
2.Question 2
The show_letters function should print out each letter of a word on a separate line.
Fill in the blanks to mak... | github_jupyter |
### Instructions
The lecture uses random forest to predict the state of the loan with data taken from Lending Club (2015). With minimal feature engineering, they were able to get an accuracy of 98% with cross validation. However, the accuracies had a lot of variance, ranging from 98% to 86%, indicating there are lots ... | 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 |
論文<br>
https://arxiv.org/abs/2109.07161<br>
<br>
GitHub<br>
https://github.com/saic-mdal/lama<br>
<br>
<a href="https://colab.research.google.com/github/kaz12tech/ai_demos/blob/master/Lama_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 環境セットア... | github_jupyter |
```
# assume you have openmm, pdbfixer and mdtraj installed.
# if not, you can follow the gudie here https://github.com/npschafer/openawsem
# import all using lines below
# from simtk.openmm.app import *
# from simtk.openmm import *
# from simtk.unit import *
from simtk.openmm.app import ForceField
# define atoms and ... | github_jupyter |
# [NTDS'18] tutorial 2: build a graph from an edge list
[ntds'18]: https://github.com/mdeff/ntds_2018
[Benjamin Ricaud](https://people.epfl.ch/benjamin.ricaud), [EPFL LTS2](https://lts2.epfl.ch)
* Dataset: [Open Tree of Life](https://tree.opentreeoflife.org)
* Tools: [pandas](https://pandas.pydata.org), [numpy](http:... | github_jupyter |
<a href="https://colab.research.google.com/github/livjab/DS-Unit-2-Sprint-4-Practicing-Understanding/blob/master/module1-hyperparameter-optimization/LS_DS_241_Hyperparameter_Optimization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
_Lambda School... | github_jupyter |
```
import fitsio as ft
import healpy as hp
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('/users/PHS0336/medirz90/github/LSSutils')
from lssutils.utils import make_hp
from lssutils.lab import get_cl
from lssutils.extrn.galactic.hpmaps import logHI
from sklearn.linear_model import Linea... | github_jupyter |
# Scroll down to get to the interesting tables...
# Construct list of properties of widgets
"Properties" here is one of:
+ `keys`
+ `traits()`
+ `class_own_traits()`
Common (i.e. uninteresting) properties are filtered out.
The dependency on astropy is for their Table. Replace it with pandas if you want...
```
imp... | github_jupyter |
# DJL BERT Inference Demo
## Introduction
In this tutorial, you walk through running inference using DJL on a [BERT](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) QA model trained with MXNet and PyTorch.
You can provide a question and a paragraph containing the a... | github_jupyter |
# Feature Engineering in Keras.
Let's start off with the Python imports that we need.
```
import os, json, math, shutil
import numpy as np
import tensorflow as tf
print(tf.__version__)
# Note that this cell is special. It's got a tag (you can view tags by clicking on the wrench icon on the left menu in Jupyter)
# The... | github_jupyter |
```
import os, numpy, warnings
import pandas as pd
os.environ['R_HOME'] = '/home/gdpoore/anaconda3/envs/tcgaAnalysisPythonR/lib/R'
warnings.filterwarnings('ignore')
%config InlineBackend.figure_format = 'retina'
%reload_ext rpy2.ipython
%%R
require(ggplot2)
require(snm)
require(limma)
require(edgeR)
require(dplyr)
req... | github_jupyter |
# <font color='Blue'> Ciência dos Dados na Prática</font>
# Sistemas de Recomendação
Cada empresa de consumo de Internet precisa um si... | github_jupyter |
# Transporter statistics and taxonomic profiles
## Overview
In this notebook some overview statistics of the datasets are computed and taxonomic profiles investigated. The notebook uses data produced by running the [01.process_data](01.process_data.ipynb) notebook.
```
import numpy as np
import pandas as pd
import s... | github_jupyter |
```
import numpy as np
from keras.models import Sequential
from keras.models import load_model
from keras.models import model_from_json
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications.imagen... | github_jupyter |
## Prepare data
```
# mount google drive & set working directory
# requires auth (click on url & copy token into text box when prompted)
from google.colab import drive
drive.mount("/content/gdrive", force_remount=True)
import os
print(os.getcwd())
os.chdir('/content/gdrive/My Drive/Colab Notebooks/MidcurveNN')
!pwd
... | github_jupyter |
```
import io
import os
import pandas as pd
data_path = 'E:\\BaiduYunDownload\\optiondata3\\'
```
## Definitions
* Underlying The stock, index, or ETF symbol
* Underlying_last The last traded price at the time of the option quote.
* Exchange The exchange of the quote – Asterisk(*) represents a consolidated price of al... | github_jupyter |
# Modeling Transmission Line Properties
## Table of Contents
* [Introduction](#introduction)
* [Propagation constant](#propagation_constant)
* [Interlude on attenuation units](#attenuation_units)
* [Modeling a loaded lossy transmission line using transmission line functions](#tline_functions)
* [Input impedances, re... | github_jupyter |
# Ridge Regression
## Goal
Given a dataset with continuous inputs and corresponding outputs, the objective is to find a function that matches the two as accurately as possible. This function is usually called the target function.
In the case of a ridge regression, the idea is to modellize the target function as a li... | github_jupyter |
```
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
train = pd.read_csv("/kaggle/input/30-days-of-ml/train.csv")
test = pd.read_csv("/kaggle/input/30-days-of-ml/test.csv")
sample_submi... | github_jupyter |
```
%matplotlib inline
```
GroupLasso for linear regression with dummy variables
=====================================================
A sample script for group lasso with dummy variables
Setup
-----
```
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.metrics ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
import shutil
sys.path.append('../code/')
sys.path.append('../python/')
from pprint import pprint
from os import path
import scipy
import os
from matplotlib import pyplot as plt
from tqdm import tqdm
from argparse import Namespace
import pickle
impor... | github_jupyter |
# Pair-wise Correlations
The purpose is to identify predictor variables strongly correlated with the sales price and with each other to get an idea of what variables could be good predictors and potential issues with collinearity.
Furthermore, Box-Cox transformations and linear combinations of variables are added whe... | github_jupyter |
# This notebook shows an example where a set of electrodes are selected from a dataset and then LFP is extracted from those electrodes and then written to a new NWB file
```
import pynwb
import os
#DataJoint and DataJoint schema
import datajoint as dj
## We also import a bunch of tables so that we can call them easi... | 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 |
# Introduction to TensorFlow v2 : Basics
### Importing and printing the versions
```
import tensorflow as tf
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution is: {}".format(tf.executing_eagerly()))
print("Keras version: {}".format(tf.keras.__version__))
```
### TensorFlow Variables
[Te... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
import sys
import os
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname("__file__"), os.path.pardir))
sys.path.append(BASE_DIR)
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import imgaug as ia
import imgaug.augmenters as iaa
import tensorflow as... | github_jupyter |
# Statistics & Data Analysis
## Req
#### Import Requirements
##### HTML formatting
```
from IPython.display import HTML
HTML("""<style type="text/css">
table.dataframe td, table.dataframe th {
max-width: none;
</style>
""")
HTML("""<style type="text/css">
table.dataframe td, table.dataframe th {
m... | github_jupyter |
```
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.datasets import mnist
from tensorflow.contrib.eager.python import tfe
# enable eager mode
tf.enable_eager_execution()
tf.set_random_seed(0)
np.random.seed(0)
if not os.path.exists('weights/'):
os.makedirs('weights/')
# constants... | github_jupyter |
# ANCOM: WGS
```
library(tidyverse)
library(magrittr)
source("/Users/Cayla/ANCOM/scripts/ancom_v2.1.R")
```
## T2
```
t2 <- read_csv('https://github.com/bryansho/PCOS_WGS_16S_metabolome/raw/master/DESEQ2/WGS/T2/T2_filtered_greater_00001.csv')
head(t2,n=1)
t2.meta <- read_csv('https://github.com/bryansho/PCOS_WGS_16S... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"></ul></div>
```
!pip install tensorflow-addons
!pip install lifelines
!pip install scikit-plot
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow import keras
from sklearn.model_selection import train_te... | github_jupyter |
# TensorFlow BYOM: Train with Custom Training Script, Compile with Neo, and Deploy on SageMaker
In this notebook you will compile a trained model using Amazon SageMaker Neo. This notebook is similar to the [TensorFlow MNIST training and serving notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sag... | github_jupyter |
# $$User\ Defined\ Metrics\ Tutorial$$
[](https://colab.research.google.com/github/catboost/tutorials/blob/master/custom_loss/custom_loss_and_metric_tutorial.ipynb)
# Contents
* [1. Introduction](#1.\-Introduction)
* [2. Classification](#2.\-Cl... | github_jupyter |
[Table of Contents](./table_of_contents.ipynb)
# The Extended Kalman Filter
```
from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
```
We have developed the theory for the linear Kalman filter. Then, in the last two chapters we broached the ... | github_jupyter |
```
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.getenv("MRCNN_HOME", "/Mask_RCNN")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library... | github_jupyter |
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