code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# How random is `r/random`?
There's a limit of 0.5 req/s (1 request every 2 seconds)
## What a good response looks like (status code 302)
```
$ curl https://www.reddit.com/r/random
<html>
<head>
<title>302 Found</title>
</head>
<body>
<h1>302 Found</h1>
The resource was found at <a href="https://www.reddit... | github_jupyter |
# A 🤗 tour of transformer applications
In this notebook we take a tour around transformers applications. The transformer architecture is very versatile and allows us to perform many NLP tasks with only minor modifications. For this reason they have been applied to a wide range of NLP tasks such as classification, nam... | github_jupyter |
# Titania = CLERK MOTEL
On Bumble, the Queen of Fairies and the Queen of Bees got together to find some other queens.
* Given
* Queen of Fairies
* Queen of Bees
* Solutions
* C [Ellery Queen](https://en.wikipedia.org/wiki/Ellery_Queen) = TDDTNW M UPZTDO
* L Queen of Hearts = THE L OF HEARTS
* E Queen Elizabe... | github_jupyter |
# Contrasts Overview
```
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
```
This document is based heavily on this excellent resource from UCLA http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm
A categorical variable of K categories, or levels, usually enters a regress... | github_jupyter |
# Gym environment with scikit-decide tutorial: Continuous Mountain Car
In this notebook we tackle the continuous mountain car problem taken from [OpenAI Gym](https://gym.openai.com/), a toolkit for developing environments, usually to be solved by Reinforcement Learning (RL) algorithms.
Continuous Mountain Car, a sta... | github_jupyter |
Timing
------
Quickly time a single line.
```
import math
import ubelt as ub
timer = ub.Timer('Timer demo!', verbose=1)
with timer:
math.factorial(100000)
```
Robust Timing and Benchmarking
------------------------------
Easily do robust timings on existing blocks of code by simply indenting
them. The quick and... | github_jupyter |
```
%run ../Python_files/util_data_storage_and_load.py
%run ../Python_files/load_dicts.py
%run ../Python_files/util.py
import numpy as np
from numpy.linalg import inv
# load link flow data
import json
with open('../temp_files/link_day_minute_Jul_dict_JSON_adjusted.json', 'r') as json_file:
link_day_minute_Jul_dic... | github_jupyter |
<a href="https://colab.research.google.com/github/Granero0011/AB-Demo/blob/master/Monte_Carlo_Simulation_Example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import numpy as np
import seaborn as sns
sns.set_style('whitegr... | github_jupyter |
```
import random
import os
import sys
from time import sleep
from datetime import datetime
import requests as rt
import numpy as np
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
from selenium.common.e... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import tsp_env
def attention(W_ref, W_q, v, enc_outputs, query):
with tf.variable_scope("attention_mask"):
u_i0s = tf.einsum('kl,itl->itk', W_ref, enc_outputs)
u_i1s = tf.expand_dims(tf.einsum('kl,il->ik', W_q, query), 1)
u_is = tf.einsum('k,itk... | github_jupyter |
<div align="right"><i>COM418 - Computers and Music</i></div>
<div align="right"><a href="https://people.epfl.ch/paolo.prandoni">Lucie Perrotta</a>, <a href="https://www.epfl.ch/labs/lcav/">LCAV, EPFL</a></div>
<p style="font-size: 30pt; font-weight: bold; color: #B51F1F;">Channel Vocoder</p>
```
%matplotlib inline
im... | github_jupyter |
Authored by: Avani Gupta <br>
Roll: 2019121004
**Note: dataset shape is version dependent hence final answer too will be dependent of sklearn version installed on machine**
# Excercise: Eigen Face
Here, we will look into ability of PCA to perform dimensionality reduction on a set of Labeled Faces in the Wild dat... | github_jupyter |
<div align="center">
<h1><img width="30" src="https://madewithml.com/static/images/rounded_logo.png"> <a href="https://madewithml.com/">Made With ML</a></h1>
Applied ML · MLOps · Production
<br>
Join 30K+ developers in learning how to responsibly <a href="https://madewithml.com/about/">deliver value</a> with ML.
... | github_jupyter |
# Expressions and Arithmetic
**CS1302 Introduction to Computer Programming**
___
## Operators
The followings are common operators you can use to form an expression in Python:
| Operator | Operation | Example |
| --------: | :------------- | :-----: |
| unary `-` | Negation | `-y` |
| `+` | Addi... | github_jupyter |
```
%matplotlib inline
from IPython import display
import matplotlib.pyplot as plt
import torch
from torch import nn
import torchvision
import torchvision.transforms as transforms
import time
import sys
sys.path.append("../")
import d2lzh1981 as d2l
from tqdm import tqdm
print(torch.__version__)
print(torchvision._... | github_jupyter |
```
import os
import numpy as np
np.random.seed(0)
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import set_config
set_config(display="diagram")
DATA_PATH = os.path.abspath(
r"C:\Users\jan\Dropbox\_Coding\UdemyML\Chapter13_CaseStudies\CaseStudyIncome\adult.xlsx"
)
```
### Dataset
```
df = pd.re... | github_jupyter |
# UK research networks with HoloViews+Bokeh+Datashader
[Datashader](http://datashader.readthedocs.org) makes it possible to plot very large datasets in a web browser, while [Bokeh](http://bokeh.pydata.org) makes those plots interactive, and [HoloViews](http://holoviews.org) provides a convenient interface for building... | github_jupyter |
# Типы данных в Python
## 1. Числовые
### int
```
x = 5
print (x)
print(type(x))
a = 4 + 5
b = 4 * 5
c = 5 // 4
print(a, b, c)
print -5 / 4
print -(5 / 4)
```
### long
```
x = 5 * 1000000 * 1000000 * 1000000 * 1000000 + 1
print x
print type(x)
y = 5
print type(y)
y = x
print type(y)
```
### float
```
y = 5.7
pr... | github_jupyter |
<img src="images/utfsm.png" alt="" width="100px" align="right"/>
# USM Numérica
## Licencia y configuración del laboratorio
Ejecutar la siguiente celda mediante *`Ctr-S`*.
```
"""
IPython Notebook v4.0 para python 3.0
Librerías adicionales:
Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT.
(c) Sebastian ... | github_jupyter |
# Prudential Life Insurance Assessment
An example of the structured data lessons from Lesson 4 on another dataset.
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
from pathlib import Path
import pandas as pd
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
f... | github_jupyter |
# **Quality Control (QC) and filtering**
This notebooks serves for filtering of the second human testis sample. It is analogous to the filtering of the other sample, so feel free to go through it faster and just skimming through the text.
---------------------
**Motivation:**
Quality control and filtering is the mo... | github_jupyter |
Carbon Insight: Carbon Emissions Visualization
==============================================
This tutorial aims to showcase how to visualize anthropogenic CO2 emissions with a near-global coverage and track correlations between global carbon emissions and socioeconomic factors such as COVID-19 and GDP.
```
# Require... | github_jupyter |
```
from sklearn.model_selection import train_test_split
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
from datetime import datetime
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization
from tensorflow import keras
import os
import re
# Set t... | github_jupyter |
<a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png" width = 400> </a>
<h1 align=center><font size = 5>From Requirements to Collection</font></h1>
## Introduction
In this lab, we will continue learning about the data science methodology, and focus on... | github_jupyter |
# # Lists
### == > it is same as array in c++ , but it can also store multiple data types at the same time
```
# creating lists
a = [1,2,3]
print(type(a))
a1 = list()
print(a1)
a2 = list(a)
print(a2)
a4 = [ i for i in range(10)] ## for range from 0 to 10 set i
print(a4)
a5 = [ i*i for i in range(10)]
print(a5)
a... | github_jupyter |
```
# default_exp data.unwindowed
```
# Unwindowed datasets
> This functionality will allow you to create a dataset that applies sliding windows to the input data on the fly. This heavily reduces the size of the input data files, as only the original, unwindowed data needs to be stored.
```
#export
from tsai.imports... | github_jupyter |
# Ungraded Lab: Build a Multi-output Model
In this lab, we'll show how you can build models with more than one output. The dataset we will be working on is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Energy+efficiency). It is an Energy Efficiency dataset which uses the ... | github_jupyter |
# Fine-tuning a Pretrained Network for Style Recognition
In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.
The advantage of this approach is that, since pre-trained networks are le... | github_jupyter |
# <img style="float: left; padding-right: 10px; width: 45px" src="https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/iacs.png"> CS109A Introduction to Data Science
## Homework 4: Logistic Regression
**Harvard University**<br/>
**Fall 2019**<br/>
**Instructors**: Pavlos Protopapas, Kevin... | github_jupyter |
# Homework 2 - Deep Learning
## Liberatori Benedetta
```
import torch
import numpy as np
# A class defining the model for the Multi Layer Perceptron
class MLP(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer1 = torch.nn.Linear(in_features=6, out_features=2, bias= True)
s... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
# 40 kotlin-dataframe puzzles
inspired by [100 pandas puzzles](https://github.com/ajcr/100-pandas-puzzles)
## Importing kotlin-dataframe
### Getting started
Difficulty: easy
**1.** Import kotlin-dataframe
```
%use dataframe(0.8.0-dev-595-0.11.0.13)
```
## DataFrame Basics
### A few of the fundamental routines for s... | github_jupyter |
# Veg ET validation
```
import pandas as pd
from time import time
import xarray as xr
import numpy as np
def _get_year_month(product, tif):
fn = tif.split('/')[-1]
fn = fn.replace(product,'')
fn = fn.replace('.tif','')
fn = fn.replace('_','')
print(fn)
return fn
def _file_object(bucket_prefix,p... | github_jupyter |
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
sys.path.append('../')
from loglizer.models import SVM
from loglizer import dataloader, preprocessing
import numpy as np
struct_log = '../data/HDFS/HDFS_100k.log_structured.csv' # The structured log file
label_file = '../data/HDFS/anomaly_label.csv' # The a... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
data=pd.read_csv('F:\\bank-additional-full.csv',sep=';')
data.shape
tot=len(set(data.index))
last=data.shape[0]-tot
last
data.isnull().sum()
print(data.y.value_counts())
sns.countplot(x='y', data=data)
plt.show()
cat=data.s... | github_jupyter |
# Requirements Documentation and Notes
# SQL Samples
2. Total monthly commits
```sql
SELECT
date_trunc( 'month', commits.cmt_author_timestamp AT TIME ZONE'America/Chicago' ) AS DATE,
repo_name,
rg_name,
cmt_author_name,
cmt_author_email,
... | github_jupyter |
```
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
%matplotlib inline
# load in the dataset into a pandas dataframe
diamonds = pd.read_csv('./data/diamonds.csv')
# convert cut, color, and clarity into ordered categor... | github_jupyter |
# Revisiting Lambert's problem in Python
```
import numpy as np
import matplotlib.pyplot as plt
from cycler import cycler
from poliastro.core import iod
from poliastro.iod import izzo
plt.ion()
plt.rc('text', usetex=True)
```
## Part 1: Reproducing the original figure
```
x = np.linspace(-1, 2, num=1000)
M_list = ... | github_jupyter |
```
# DATAFRAMES INITIALISATION
import os
os.chdir('C:\\Users\\asus\\OneDrive\\Documenti\\University Docs\\MSc Computing\\Final Project\\RainbowFood(JN)\\Rainbow-Food-Collaborative-Filtering-')
import pandas as pd
# vegetables file
col_list_veg = ["Vegetables", "Serving", "Calories"]
df_veg = pd.read_csv("Vegetables... | github_jupyter |
# Download data for a functional layer of Spatial Signatures
This notebook downloads and prepares data for a functional layer of Spatial Signatures.
```
from download import download
import geopandas as gpd
import pandas as pd
import osmnx as ox
from tqdm import tqdm
from glob import glob
import rioxarray as ra
impor... | github_jupyter |
# GLM: Negative Binomial Regression
```
%matplotlib inline
import numpy as np
import pandas as pd
import pymc3 as pm
from scipy import stats
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
import seaborn as sns
import re
print('Running on PyMC3 v{}'.format(pm.__version__))
```
This notebook demos ne... | github_jupyter |
# Multi-qubit quantum circuit
In this exercise we creates a two qubit circuit, with two qubits in superposition, and then measures the individual qubits, resulting in two coin toss results with the following possible outcomes with equal probability: $|00\rangle$, $|01\rangle$, $|10\rangle$, and $|11\rangle$. This is li... | github_jupyter |
# Twitter Mining Function & Scatter Plots
---------------------------------------------------------------
```
# Import Dependencies
%matplotlib notebook
import os
import csv
import json
import requests
from pprint import pprint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from twython import ... | github_jupyter |
# Simulating Power Spectra
In this notebook we will explore how to simulate the data that we will use to investigate how different spectral parameters can influence band ratios.
Simulated power spectra will be created with varying aperiodic and periodic parameters, and are created using the [FOOOF](https://github.co... | github_jupyter |
# Symbolic System
Create a symbolic three-state system:
```
import markoviandynamics as md
sym_system = md.SymbolicDiscreteSystem(3)
```
Get the symbolic equilibrium distribution:
```
sym_system.equilibrium()
```
Create a symbolic three-state system with potential energy barriers:
```
sym_system = md.SymbolicDisc... | github_jupyter |
# Logistic Regression
Modules
```
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
```
Hyper-parameters
```
input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
```
MNIST dataset (images and labels)
```
train_dataset = torchvision... | github_jupyter |
# Version information
```
from datetime import date
print("Running date:", date.today().strftime("%B %d, %Y"))
import pyleecan
print("Pyleecan version:" + pyleecan.__version__)
import SciDataTool
print("SciDataTool version:" + SciDataTool.__version__)
```
# How to define a machine
This tutorial shows the different ... | github_jupyter |
## 练习 1：写程序，可由键盘读入用户姓名例如Mr. right，让用户输入出生的月份与日期，判断用户星座，假设用户是金牛座，则输出，Mr. right，你是非常有性格的金牛座！。
```
name = input('请输入你的姓名')
print('你好',name)
print('请输入出生的月份与日期')
month = int(input('月份：'))
date = int(input('日期：'))
if month == 4:
if date < 20:
print(name, '你是白羊座')
else:
print(name,'你是非常有性格的金牛座')
... | github_jupyter |
# Buscas supervisionadas
## Imports
```
# imports necessarios
from search import *
from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
im... | github_jupyter |
```
"""The file needed to run this notebook can be accessed from the following folder using a UTS email account:
https://drive.google.com/drive/folders/1y6e1Z2SbLDKkmvK3-tyQ6INO5rrzT3jp
"""
```
# Object Detection Using RFCN
## Tutorial:
1. Image annotation using LabelImg
2. Conversion of annotation & images into tfre... | github_jupyter |
# 1. Enumerate sentence
Create a function that prints words within a sentence along with their index in front of the word itself.
For example if we give the function the argument "This is a sentence" it should print
```
1 This
2 is
3 a
4 sentence
```
```
def enumWords(sentence):
#Complete this method.
```
# 2. ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as sts
import seaborn as sns
sns.set()
%matplotlib inline
```
# 01. Smooth function optimization
Рассмотрим все ту же функцию из задания по линейной алгебре:
$ f(x) = \sin{\frac{x}{5}} * e^{\frac{... | github_jupyter |
# Mount Drive
```
from google.colab import drive
drive.mount('/content/drive')
!pip install -U -q PyDrive
!pip install httplib2==0.15.0
import os
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from pydrive.files import GoogleDriveFileList
from google.colab import auth
from oauth2client.clien... | github_jupyter |
```
import pandas as pd
import numpy as np
data = pd.read_csv('features_30_sec.csv')
data.head()
dataset = data[data['label'].isin(['blues', 'classical', 'jazz', 'metal', 'pop'])].drop(['filename','length'],axis=1)
dataset.iloc[:, :-15].head()
from sklearn.model_selection import train_test_split
from sklearn.preprocess... | github_jupyter |
## Importing Packages
```
import pandas as pd
import numpy as np
import tqdm
import pickle
from pprint import pprint
import os
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
#sklearn
from sklearn.manifold import TSNE
from sklearn.feature_extraction.text import CountVectorizer
from skl... | github_jupyter |
# Analyzing data with Dask, SQL, and Coiled
In this notebook, we look at using [Dask-SQL](https://dask-sql.readthedocs.io/en/latest/), an exciting new open-source library which adds a SQL query layer on top of Dask. This allows you to query and transform Dask DataFrames using common SQL operations.
## Launch a cluste... | github_jupyter |
```
# Copyright 2019 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License")
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras import layers
import tensorflow.keras.backend as keras_backend
tf.keras.backend.set_floatx('float32')
import tensorflow_probability as tfp
f... | github_jupyter |
<a href="https://colab.research.google.com/github/gpdsec/Residual-Neural-Network/blob/main/Custom_Resnet_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
*It's custom ResNet trained demonstration purpose, not for accuracy.
Dataset used is cats_vs_d... | github_jupyter |
```
import geopandas as gpd
import pandas as pd
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import tarfile
from discretize import TensorMesh
from SimPEG.utils import plot2Ddata, surface2ind_topo
from SimPEG.potential_fields import gravity
from SimPEG import (
maps,
d... | github_jupyter |
# Overview
In this project, I will build an item-based collaborative filtering system using [MovieLens Datasets](https://grouplens.org/datasets/movielens/latest/). Specically, I will train a KNN models to cluster similar movies based on user's ratings and make movie recommendation based on similarity score of previous... | github_jupyter |
```
import json
import os
from pathlib import Path
import time
import copy
import numpy as np
import pandas as pd
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from torchvision import models
from fastai.dataset import open_image
import json
from PIL import ImageDraw, ImageFo... | github_jupyter |
## Bibliotecas:
```
#importanto bibliotecas
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model, preprocessing
import statsmodels.api as sm
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
from dat... | 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 |
Quick study to investigate oscillations in reported infections in Germany. Here is the plot of the data in question:
```
import coronavirus
import numpy as np
import matplotlib.pyplot as plt
%config InlineBackend.figure_formats = ['svg']
coronavirus.display_binder_link("2020-05-10-notebook-weekly-fluctuations-in-data... | github_jupyter |
# Predict Happiness Source
- Importing the Packages
```
# importing packages
import pandas as pd
import numpy as np # For mathematical calculations
import seaborn as sns # For data visualization
import matplotlib.pyplot as plt # For plotting graphs
%matplotlib inline
... | github_jupyter |
```
from scipy.special import expit
from rbm import RBM
from sampler import VanillaSampler, PartitionedSampler
from trainer import VanillaTrainier
from performance import Result
import numpy as np
import datasets, performance, plotter, mnist, pickle, rbm, os, logging
logger = logging.getLogger()
# Set the logging leve... | github_jupyter |
```
!pip install matplotlib
import os
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
class Args:
method = 'dopri5' # choices=['dopri5', 'adams']
data_size = 1000
batch_time = 10
batch_size = 20
niters = 2000
test_freq = 20
viz = ... | github_jupyter |
```
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn... | github_jupyter |
---
_You are currently looking at **version 1.0** 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-text-mining/resources/d9pwm) course resource._
---
# Assignment 2 - Introd... | github_jupyter |
# Assignment 4: Word Sense Disambiguation: from start to finish
## Due: Tuesday 6 December 2016 15:00 p.m.
Please name your Jupyter notebook using the following naming convention: ASSIGNMENT_4_FIRSTNAME_LASTNAME.ipynb
Please send your assignment to `m.c.postma@vu.nl`.
A well-known NLP task is [Word Sense Disambigu... | github_jupyter |
<a href="https://colab.research.google.com/github/sarahalyahya/SoftwareArt-Text/blob/main/LousyFairytaleGenerator_Assemblage_Project1_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Lousy Fairytale Plot Generator | Sarah Al-Yahya
*scroll to the en... | github_jupyter |
# Imports
```
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import cvxpy as cp
import time
import collections
from typing import Dict
from typing import List
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import sea... | github_jupyter |
```
import seaborn as sb
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
from sklearn.neighbors import NearestNeighbors
link='/Users/afatade/Downloads/anime_cleaned.csv'
data=pd.read_csv(link)
data.head()
len(data)
#We have a lot of data. Lets see wh... | github_jupyter |
# <font color='blue'>Data Science Academy</font>
# <font color='blue'>Big Data Real-Time Analytics com Python e Spark</font>
# <font color='blue'>Capítulo 6</font>
# Machine Learning em Python - Parte 2 - Regressão
```
from IPython.display import Image
Image(url = 'images/processo.png')
import sklearn as sl
import w... | github_jupyter |
## Plotting Results
```
experiment_name = ['l1000_AE','l1000_cond_VAE','l1000_VAE','l1000_env_prior_VAE']
import numpy as np
from scipy.spatial.distance import cosine
from scipy.linalg import svd, inv
import pandas as pd
import matplotlib.pyplot as plt
import dill as pickle
import os
import pdb
import torch
import ai.... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd.variable as Variable
import torch.utils.data as data
import torchvision
from torchvision import transforms
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import sparse
import lightfm... | github_jupyter |
```
#
# This small example shows you how to access JS-based requests via Selenium
# Like this, one can access raw data for scraping,
# for example on many JS-intensive/React-based websites
#
import time
from selenium import webdriver
from selenium.webdriver import DesiredCapabilities
from selenium.webdriver.support.ui... | github_jupyter |
Evaluating performance of FFT2 and IFFT2 and checking for accuracy. <br><br>
Note that the ffts from fft_utils perform the transformation in place to save memory.<br><br>
As a rule of thumb, it's good to increase the number of threads as the size of the transform increases until one hits a limit <br><br>
pyFFTW uses lo... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import warnings
warnings.filterwarnings('ignore')
import math
from time import time
import pickle
import pandas as pd
import numpy as np
from time import time
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.metrics imp... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from sklearn.utils import shuffle
import re
import time
import collections
import os
def build_dataset(words, n_words, atleast=1):
count = [['PAD', 0], ['GO', 1], ['EOS', 2], ['UNK', 3]]
counter = collections.Counter(words).most_common(n_words)
counter = [i for... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_04_atari.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 12: Reinforcement Learn... | github_jupyter |
# Disclaimer
Released under the CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
# Purpose of this notebook
The purpose of this document is to show how I approached the presented problem and to record my learning experience in how to use Tensorflow 2 and CatBoost to perform a classification task on t... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
<a href="https://colab.research.google.com/github/120Davies/DS-Unit-4-Sprint-3-Deep-Learning/blob/master/Ro_Davies_LS_DS_431_RNN_and_LSTM_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img align="left" src="https://lever-client-logos.s3... | github_jupyter |
## Data Description and Analysis
```
import numpy as np
import pandas as pd
pd.set_option('max_columns', 150)
import gc
import os
# matplotlib and seaborn for plotting
import matplotlib
matplotlib.rcParams['figure.dpi'] = 120 #resolution
matplotlib.rcParams['figure.figsize'] = (8,6) #figure size
import matplotlib.p... | github_jupyter |
---
## Data Prep
### Dataset Cleaning
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from time import time
from src.features import build_features as bf
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import GridSearchC... | github_jupyter |
```
import scipy.io, os
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
from fastjmd95 import rho
from matplotlib.colors import ListedColormap
import seaborn as sns; sns.set()
sns.set()
import seawater as sw
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import matp
... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA,TruncatedSVD,NMF
from sklearn.preprocessing import Normalizer
import argparse
import time
import pickle as pkl
def year_binner(year,val=10):
return year - year%val
def dim_reduction(df,rows):
df_svd = TruncatedSVD(n_components=300,... | github_jupyter |
Load libs and utilities.
```
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
from google.colab import drive
drive.mount('/content/drive')
%cd "drive/MyDrive/Projects/Fourier"
!pip inst... | github_jupyter |
# Neural Networks for Regression with TensorFlow
> Notebook demonstrates Neural Networks for Regression Problems with TensorFlow
- toc: true
- badges: true
- comments: true
- categories: [DeepLearning, NeuralNetworks, TensorFlow, Python, LinearRegression]
- image: images/nntensorflow.png
## Neural Network Regression... | github_jupyter |
# Análise de Dados com Python
Neste notebook, utilizaremos dados de automóveis para analisar a influência das características de um carro em seu preço, tentando posteriormente prever qual será o preço de venda de um carro. Utilizaremos como fonte de dados um arquivo .csv com dados já tratados em outro notebook. Caso ... | github_jupyter |
```
import pandas as pd
#This is the Richmond USGS Data gage
river_richmnd = pd.read_csv('JR_Richmond02037500.csv')
river_richmnd.dropna();
#Hurricane data for the basin - Names of Relevant Storms - This will be used for getting the storms from the larger set
JR_stormnames = pd.read_csv('gis_match.csv')
# Bring in the ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/NAIP/ndwi.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="https://nbvi... | github_jupyter |
## Release the Kraken!
```
# The next library we're going to look at is called Kraken, which was developed by Université
# PSL in Paris. It's actually based on a slightly older code base, OCRopus. You can see how the
# flexible open-source licenses allow new ideas to grow by building upon older ideas. And, in
# this ... | github_jupyter |
```
# @title Copyright & License (click to expand)
# Copyright 2021 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 r... | github_jupyter |
RMinimum : Full - Test
```
import math
import random
import queue
```
Testfall : $X = [0, \cdots, n-1]$, $k$
```
# User input
n = 2**10
k = 2**5
# Automatic
X = [i for i in range(n)]
# Show Testcase
print(' Testcase: ')
print('=============================')
print('X = [0, ..., ' + str(n - 1) + '... | github_jupyter |
# Advanced topics
The following material is a deep-dive into Yangson, and is not necessarily representative of how one would perform manipulations in a production environment. Please refer to the other tutorials for a better picture of Rosetta's intended use. Keep in mind that the key feature of Yangson is to be abl... | github_jupyter |
# Planning Search Agent
Notebook version of the project [Implement a Planning Search](https://github.com/udacity/AIND-Planning) from [Udacity's Artificial Intelligence Nanodegree](https://www.udacity.com/course/artificial-intelligence-nanodegree--nd889) <br>
**Goal**: Solve deterministic logistics planning problems f... | github_jupyter |
>>> Work in Progress (Following are the lecture notes of Prof Andrew Ng/Head TA-Raphael Townshend - CS229 - Stanford. This is my interpretation of his excellent teaching and I take full responsibility of any misinterpretation/misinformation provided herein.)
## Lecture Notes
#### Outline
- Decision Trees
- Ensemble M... | github_jupyter |
```
import argparse
import logging
import math
import os
import random
import shutil
import time
from collections import OrderedDict
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader, RandomS... | github_jupyter |
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