code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
# Import conventions we'll be using here. See Part 1
import matplotlib
# matplotlib.use('nbagg')
import matplotlib.pyplot as plt
import numpy as np
```
# Limits, Legends, and Layouts
In this section, we'll focus on what happens around the edges of the axes: Ticks, ticklabels, limits, layouts, and legends.
# Lim... | github_jupyter |
<a href="https://colab.research.google.com/github/piyushjain220/TSAI/blob/main/NLP/Resources/EVA_P2S3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Imports
```
import numpy as np
%matplotlib inline
import numpy as np
import matplotlib.pyplot as ... | github_jupyter |
## QE methods and QE_utils
In this tutorial, we will explore various methods needed to handle Quantum Espresso (QE) calculations - to run them, prepare input, and extract output. All that will be done with the help of the **QE_methods** and **QE_utils** modules, which contains the following functions:
**QE_methods**
... | github_jupyter |
___
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Python3-powered_hello-world.svg/1000px-Python3-powered_hello-world.svg.png" width="300px" height="100px" />
# <font color= #8A0829> Simulación matemática.</font>
#### <font color= #2E9AFE> `Miérco... | github_jupyter |
```
import matplotlib.pyplot as plt
import BondGraphTools
from BondGraphTools.config import config
from BondGraphTools.reaction_builder import Reaction_Network
julia = config.julia
```
# `BondGraphTools`
## Modelling Network Bioenergetics.
https://github.com/peter-cudmore/seminars/ANZIAM-2019
Dr. Peter ... | github_jupyter |
# Naive-Bayes Classifier
```
#Baseline SVM with PCA classifier
import sklearn
import numpy as np
import sklearn.datasets as skd
import ast
from sklearn.feature_extraction import DictVectorizer
from sklearn import linear_model
from sklearn import naive_bayes
from sklearn.metrics import precision_recall_fscore_support
f... | github_jupyter |
# A Whale off the Port(folio)
---
In this assignment, you'll get to use what you've learned this week to evaluate the performance among various algorithmic, hedge, and mutual fund portfolios and compare them against the S&P 500 Index.
```
# Initial imports
import pandas as pd
import numpy as np
import datetime as... | github_jupyter |
# Basics of probability
We'll start by reviewing some basics of probability theory. I will use some simple examples - dice and roullete - to illustrate basic probability concepts. We'll also use these simple examples to build intuition on several properties of probabilities - the law of total probability, independence... | github_jupyter |
```
# A simple example of an animated plot
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
# Initial plot
x = np.arange(0., 10., 0.2)
y = np.arange(0., 10., 0.2)
line, = ax.plot(x, y)
plt.rcParams["figure.figsize"] = (10,8)
plt.ylabel("Price")
plt.... | github_jupyter |
##### Copyright 2020 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 |
# 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 |
```
import numpy as np
import pandas as pd
import tensorflow as tf
import os
import warnings
warnings.filterwarnings('ignore')
from tensorflow import keras
from sklearn.preprocessing import RobustScaler, MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import Ran... | github_jupyter |
# ACA-Py & ACC-Py Basic Template
## Copy this template into the root folder of your notebook workspace to get started
### Imports
```
from aries_cloudcontroller import AriesAgentController
import os
from termcolor import colored
```
### Initialise the Agent Controller
```
api_key = os.getenv("ACAPY_ADMIN_API_KEY")... | github_jupyter |
<a href="https://colab.research.google.com/github/100rab-S/Tensorflow-Developer-Certificate/blob/main/S%2BP_Week_4_Lesson_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
#... | github_jupyter |
Program: 08_kmeans_test.R
Date: September, 2019
Programmer: Hillary Mulder
Purpose: Show K means doesnt work well with harmonized trials data
```
library(cluster)
library(caret)
library(purrr)
library(dplyr)
library(boot)
#library(table1)
library(Hmisc)
data=read.csv("Data/analysis_ds.csv")
data$allhat=ifelse(d... | github_jupyter |
# 作业３：设计并训练KNN算法对图片进行分类。
## example1:
```
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
k=7
test_num=int(input('请输入需要测试的数据数量：'))
#加载TFRecord训练集的数据
reader = tf.TFRecordReader()
filename_queue = tf.train.string_input_producer(["/home/srhyme/ML project/DS/train.t... | github_jupyter |
# Continuous Delivery Explained
> "An introduction to the devops practice of CI/CD."
- toc: false
- branch: master
- badges: true
- comments: true
- categories: [devops, continuous-delivery]
- image: images/copied_from_nb/img/devops/feedback-cycle.png
> *I wrote... | github_jupyter |
<center>
<img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# String Operations
Estimated time needed: **15** minutes
## Objectives
After completing this lab you will b... | github_jupyter |
**[Pandas Home Page](https://www.kaggle.com/learn/pandas)**
---
# Introduction
In this set of exercises we will work with the [Wine Reviews dataset](https://www.kaggle.com/zynicide/wine-reviews).
Run the following cell to load your data and some utility functions (including code to check your answers).
```
import ... | github_jupyter |
# Content-based recommender using Deep Structured Semantic Model
An example of how to build a Deep Structured Semantic Model (DSSM) for incorporating complex content-based features into a recommender system. See [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.co... | github_jupyter |
# Plots for logistic regression, consistent vs inconsistent noiseless AT, increasing epsilon
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches
import dotenv
import pandas as pd
import mlflow
import plotly
import plotly.graph_objects as go
import plotly.express as px
import plotly.subplot... | github_jupyter |
# RNN - LSTM - Toxic Comments
A corpus of manually labeled comments - classifying each comment by its type of toxicity is available on Kaggle. We will aim to do a binary classification of whether a comment is toxic or not.
Approach:
- Learning Embedding with the Task
- LSTM
- BiLSTM
```
import numpy as np
import pan... | github_jupyter |
## Mask Adaptivity Detection Using YOLO
Mask became an essential accessory post COVID-19. Most of the countries are making face masks mandatory to avail services like transport, fuel and any sort of outside activity. It is become utmost necessary to keep track of the adaptivity of the crowd. This notebook contains imp... | github_jupyter |
## Precision-Recall Curves in Multiclass
For multiclass classification, we have 2 options:
- determine a PR curve for each class.
- determine the overall PR curve as the micro-average of all classes
Let's see how to do both.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.da... | github_jupyter |
```
"""Flow around a hole in a porous medium."""
from fenics import *
import numpy as np
def make_mesh(Theta, a, b, nr, nt, s):
mesh = RectangleMesh(Point(a, 0), Point(b, 1),
nr, nt, 'crossed')
# Define markers for Dirichket boundaries
tol = 1E-14
# x=a becomes the inner bore... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import StratifiedShuffleSplit, cross_val_score, cross_val_predict, GridSearchCV
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticR... | github_jupyter |
#### Copyright 2017 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 writin... | github_jupyter |
## A motivating example: harmonic oscillator
### created by Yuying Liu, 11/02/2019
```
# imports
import os
import sys
import torch
import numpy as np
import scipy as sp
from scipy import integrate
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.m... | github_jupyter |
<a href="https://colab.research.google.com/github/gmshashank/Deep_Flow_Prediction/blob/main/supervised_airfoils_normalized.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Supervised training for RANS flows around airfoils
## Overview
For this e... | github_jupyter |
Decoding with ANOVA + SVM: face vs house in the Haxby dataset
===============================================================
This example does a simple but efficient decoding on the Haxby dataset:
using a feature selection, followed by an SVM.
```
import warnings
warnings.filterwarnings('ignore')
import matplotlib... | github_jupyter |
# Python Flair Basics
**(C) 2018-2020 by [Damir Cavar](http://damir.cavar.me/)**
**Version:** 0.3, February 2020
**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-for-ipython).
**License:** [Creative Commons Attribution-ShareAlike 4.... | github_jupyter |
```
import datetime
import lightgbm as lgb
import numpy as np
import os
import pandas as pd
import random
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import haversine
import catboost as cb
random_seed = 174
random.seed(random_seed)
np.random.seed(random_seed)
# Load data
train = pd.read_... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.pn... | github_jupyter |
# Assignment 3: Question Answering
Welcome to this week's assignment of course 4. In this you will explore question answering. You will implement the "Text to Text Transfer from Transformers" (better known as T5). Since you implemented transformers from scratch last week you will now be able to use them.
<img src = ... | github_jupyter |
```
#default_exp dataset_torch
```
# dataset_torch
> Module to load the slates dataset into a Pytorch Dataset and Dataloaders with default train/valid test splits.
```
#export
import torch
import recsys_slates_dataset.data_helper as data_helper
from torch.utils.data import Dataset, DataLoader
import torch
import jso... | github_jupyter |
# Collaborative filtering on the MovieLense Dataset
###### This notebook is based on part of Chapter 9 of [BigQuery: The Definitive Guide](https://www.oreilly.com/library/view/google-bigquery-the/9781492044451/ "http://shop.oreilly.com/product/0636920207399.do") by Lakshmanan and Tigani.
### MovieLens dataset
To illus... | github_jupyter |
```
import neuroglancer
# Use this in IPython to allow external viewing
# neuroglancer.set_server_bind_address(bind_address='192.168.158.128',
# bind_port=80)
from nuggt.utils import ngutils
viewer = neuroglancer.Viewer()
viewer
import numpy as np
import zarr
import os
# working_d... | github_jupyter |
```
## plot plasma density
%pylab inline
import numpy as np
from matplotlib import pyplot as plt
from ReadBinary import *
fileSuffix = "-10"
folder = "../data/LargePeriodicLattice-GaussianPlasma/fp=1THz/"
#folder = "../data/2D/"
filename = folder+"Wp2-x{}.data".format(fileSuffix)
arrayInfo = GetArrayInfo(filename)
... | github_jupyter |
```
import pandas as pd
from pylab import rcParams
import seaborn as sb
import matplotlib.pyplot as plt
import sklearn
from sklearn.cluster import DBSCAN
from collections import Counter
import datetime
from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
from functools import reduce
imp... | github_jupyter |
# Quantum tomography for n-qubit
Init state: general GHZ
Target state: 1 layer
Here is the case for n-qubit with $n>1$.
The state that need to reconstruct is GHZ state:
$
|G H Z\rangle=\frac{1}{\sqrt{2}}(|0 \ldots 0\rangle+|1 \ldots 1\rangle)=\frac{1}{\sqrt{2}}\left[\begin{array}{c}
1 \\
0 \\
\ldots \\
1
\end{array... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import warnings
warnings.filterwarnings('ignore')
df1 = pd.read_csv('monday.csv', sep = ";")
df2 = pd.read_csv('tuesday.csv', sep = ";")
df3 = pd.read_csv('wednesday.csv', sep = ";")
df4 = pd.read_csv('thursday.csv', sep = ... | github_jupyter |
# Chapter 2 - Small Worlds vs Large Wolrds
[Recorded Classes 2019 Chap2 by Richard McElreath](https://www.youtube.com/watch?v=XoVtOAN0htU&list=PLDcUM9US4XdNM4Edgs7weiyIguLSToZRI&index=2)
The **Small World** represents the scientific model itself, and the **Large World**
represents the broader context in which one dep... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Deep Learning
## Project: Build a Traffic Sign Recognition Classifier
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included i... | github_jupyter |
# Example 1: How to Generate Synthetic Data (MarginalSynthesizer)
In this notebook we show you how to create a simple synthetic dataset.
# Environment
## Library Imports
```
import numpy as np
import pandas as pd
from pathlib import Path
import os
import sys
```
## Jupyter-specific Imports and Settings
```
# set p... | github_jupyter |
# Mapping QTL in BXD mice using R/qtl2
[Karl Broman](https://kbroman.org)
[<img style="display:inline-block;" src="https://orcid.org/sites/default/files/images/orcid_16x16(1).gif">](https://orcid.org/0000-0002-4914-6671),
[Department of Biostatistics & Medical Informatics](https://www.biostat.wisc.edu),
[University o... | github_jupyter |
# Simplifying Codebases
Param's just a Python library, and so anything you can do with Param you can do "manually". So, why use Param?
The most immediate benefit to using Param is that it allows you to greatly simplify your codebases, making them much more clear, readable, and maintainable, while simultaneously provi... | github_jupyter |
Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Author: Sebastian Raschka
- GitHub Repository: https://github.com/rasbt/deeplearning-models
```
!pip install -q IPython
!pip install -q ipykernel
!pip install -q watermark
!p... | github_jupyter |
# Changing the input current when solving PyBaMM models
This notebook shows you how to change the input current when solving PyBaMM models. It also explains how to load in current data from a file, and how to add a user-defined current function. For more examples of different drive cycles see [here](https://github.com... | github_jupyter |
## Discretisation
Discretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of the variable's values. Discretisation is also called **binning**, where bin is an alternative name for interval.
### Discretisation helps handl... | github_jupyter |
This baseline has reached Top %11 with rank of #457/4540 Teams at Private Leader Board (missed Bronze with only 2 places)
```
import numpy as np
import pandas as pd
import sys
import gc
from scipy.signal import hilbert
from scipy.signal import hann
from scipy.signal import convolve
pd.options.display.precision = 15... | github_jupyter |
# Exercise 4 - Optimizing Model Training
In [the previous exercise](./03%20-%20Compute%20Contexts.ipynb), you created cloud-based compute and used it when running a model training experiment. The benefit of cloud compute is that it offers a cost-effective way to scale out your experiment workflow and try different alg... | github_jupyter |
# Numpy实现浅层神经网络
实践部分将搭建神经网络，包含一个隐藏层，实验将会展现出与Logistic回归的不同之处。
实验将使用两层神经网络实现对“花”型图案的分类，如图所示，图中的点包含红点（y=0）和蓝点（y=1）还有点的坐标信息，实验将通过以下步骤完成对两种点的分类，使用Numpy实现。
- 输入样本；
- 搭建神经网络；
- 初始化参数；
- 训练，包括前向传播与后向传播（即BP算法）；
- 得出训练后的参数；
- 根据训练所得参数，绘制两类点边界曲线。
<img src="image/data.png" style="width:400px;height:300px;">
该实验将使用Python... | github_jupyter |
# What's this TensorFlow business?
You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.
For t... | github_jupyter |
# Trump Tweets at the Internet Archive
So Trump's Twitter account is gone. At least at twitter.com. But (fortunately for history) there has probably never been a more heavily archived social media account at the Internet Archive and elsewhere on the web. There are also a plethora of online "archives" like [The Trump A... | github_jupyter |
## Profiling sequential code
I profiled the sequential code `count_spacers_with_ED.py` using the `cProfile` Python package. I ran `count_spacers_with_ED.py` with a control file of 100 sequences (*Genome-Pos-3T3-Unsorted_100_seqs.txt*) and an experimental file of 100 sequences (*Genome-Pos-3T3-Bot10_100_seqs.txt*). Eac... | github_jupyter |
## Bayesian Optimization with Scikit-Optimize
In this notebook, we will perform **Bayesian Optimization** with Gaussian Processes in Parallel, utilizing various CPUs, to speed up the search.
This is useful to reduce search times.
https://scikit-optimize.github.io/stable/auto_examples/parallel-optimization.html#exam... | github_jupyter |
## Control Flow
Generally, a program is executed sequentially and once executed it is not repeated again. There may be a situation when you need to execute a piece of code n number of times, or maybe even execute certain piece of code based on a particular condition.. this is where the control flow statements come in.
... | github_jupyter |
# Smart Queue Monitoring System - Retail Scenario
## Overview
Now that you have your Python script and job submission script, you're ready to request an **IEI Tank-870** edge node and run inference on the different hardware types (CPU, GPU, VPU, FPGA).
After the inference is completed, the output video and stats file... | github_jupyter |
## MatrixTable Tutorial
If you've gotten this far, you're probably thinking:
- "Can't I do all of this in `pandas` or `R`?"
- "What does this have to do with biology?"
The two crucial features that Hail adds are _scalability_ and the _domain-specific primitives_ needed to work easily with biological data. Fear not!... | github_jupyter |
```
# 1. Loading Libraries
# Importing NumPy and Panda
import pandas as pd
import numpy as np
# ---------Import libraries & modules for data visualizaiton
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
# Importing scit-learn module to split the dataset into train/test sub-datasets
from skle... | github_jupyter |
# COCO Reader
Reader operator that reads a COCO dataset (or subset of COCO), which consists of an annotation file and the images directory.
`DALI_EXTRA_PATH` environment variable should point to the place where data from [DALI extra repository](https://github.com/NVIDIA/DALI_extra) is downloaded. Please make sure tha... | github_jupyter |
```
import numpy as np
np.random.seed(123)
import os
from keras.models import Model
from keras.layers import Input, Convolution2D, MaxPooling2D, BatchNormalization
from keras.layers import Flatten, Dense, Dropout, ZeroPadding2D, Reshape, UpSampling2D
from keras.layers.local import LocallyConnected1D
from keras.layers.... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=Mathematics/PatternsAndRelations/patter... | github_jupyter |
# Python code til udregning af data fra ATP
```
#Imports
```
# Udregninger
## Alder for at kunne blive tilbudt tidlig pension
```
#Årgange født i 1955-1960 har adgang til at søge i 2021.
#Man skal være fyldt 61 for at søge.
print(2021-61, "kan anmode om tidlig pension")
#Der indgår personer fra 6 1⁄2 årgange
pr... | github_jupyter |
# Speed comparison between PyPairs and the R verison - PyPairs
Here we ran the sandbag part of the original Pairs method on the oscope dataset for a growing subset of genes. Taking note of the required execution time. Single cored time is taken. For the result please see: [2.3 Differences in code - Python](./2.3%20Dif... | github_jupyter |
# Practice Notebook: Methods and Classes
The code below defines an *Elevator* class. The elevator has a current floor, it also has a top and a bottom floor that are the minimum and maximum floors it can go to. Fill in the blanks to make the elevator go through the floors requested.
```
class Elevator:
def __init_... | github_jupyter |
# PTN Template
This notebook serves as a template for single dataset PTN experiments
It can be run on its own by setting STANDALONE to True (do a find for "STANDALONE" to see where)
But it is intended to be executed as part of a *papermill.py script. See any of the
experimentes with a papermill script to get sta... | github_jupyter |
# Worksheet 0.1.2: Python syntax (`while` loops)
<div class="alert alert-block alert-info">
This worksheet will invite you to tinker with the examples, as they are live code cells. Instead of the normal fill-in-the-blank style of notebook, feel free to mess with the code directly. Remember that -- to test things out -... | github_jupyter |
# "# backtesting with grid search"
> "Easily backtest a grid of parameters in a given trading strategy"
- toc: true
- branch: master
- badges: true
- comments: true
- author: Jerome de Leon
- categories: [grid search, backtest]
<a href="https://colab.research.google.com/github/enzoampil/fastquant/blob/master/examples... | github_jupyter |
## Preprocessing
```
# Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import tensorflow as tf
# Import and read the charity_data.csv.
import pandas as pd
application_df = pd.read_csv("Resources/charity_data.csv")
appl... | github_jupyter |
# Tabular Q-Learning From Scratch
### Custom Environment to train our model on
```
import gym
from gym import spaces
import numpy as np
import random
from copy import deepcopy
class gridworld_custom(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
d... | github_jupyter |
# Abstractive Summarization
### Loading Pre-processed Dataset
The Data is preprocessed in [Data_Pre-Processing.ipynb](https://github.com/JRC1995/Abstractive-Summarization/blob/master/Data_Pre-Processing.ipynb)
Dataset source: https://www.kaggle.com/snap/amazon-fine-food-reviews
```
import json
with open('Processed... | github_jupyter |
```
import pandas as pd
import numpy as np
import lightgbm as lgb
from collections import OrderedDict
from sklearn.metrics import roc_auc_score
from tqdm import tqdm
from copy import deepcopy
from autowoe import ReportDeco, AutoWoE
```
### Чтение и подготовка обучающей выборки
```
train = pd.read_csv("./example_dat... | github_jupyter |
Discussion Analysis
===
Notebook for analysis of discussion done in Evidence and Reconsider tasks via the annotation web client.
```
import os
import re
import pandas as pd
import numpy as np
import sklearn
import sklearn.metrics
from collections import Counter
import itertools
import sqlite3
import sys
sys.path.appe... | github_jupyter |
_Lambda School Data Science, Unit 2_
# Regression 2 Sprint Challenge: Predict drugstore sales 🏥
For your Sprint Challenge, you'll use real-world sales data from a German drugstore chain, from Jan 2, 2013 — July 31, 2015.
You are given three dataframes:
- `train`: historical sales data for 100 stores
- `test`: his... | github_jupyter |
<a href="https://colab.research.google.com/github/olgOk/QCircuit/blob/master/tutorials/Deutsch_Algorithm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deutsch Algorithm
by Olga Okrut
Install frameworks, and import libraries
```
!pip install t... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Stop-Reinventing-Pandas" data-toc-modified-id="Stop-Reinventing-Pandas-1"><span class="toc-item-num">1 </span>Stop Reinventing Pandas</a></span></li><li><span><a href="#First-Hacks!" data-toc-mod... | github_jupyter |
# Interacting with ProtoDash
In this notebook we'll combine the ProtoDash and the Partial Effects to obtain feature importances on the digits classifications task.
ProtoDash was proposed in _Gurumoorthy, Karthik & Dhurandhar, Amit & Cecchi, Guillermo & Aggarwal, Charu. (2019). Efficient Data Representation by Selecti... | github_jupyter |
<h2> 6. Bayes Classification </h2>
This notebook has the code for the charts in Chapter 6
### Install BigQuery module
You don't need this on AI Platform, but you need this on plain-old JupyterLab
```
!pip install google-cloud-bigquery
%load_ext google.cloud.bigquery
```
### Setup
```
import os
PROJECT = 'data-sci... | github_jupyter |
```
import pandas as pd
from datetime import timedelta, date
import matplotlib.pyplot as plt
def append_it(date, amount,treasury,Agency,MBS, duration):
append_data = {'Date':[date], 'Amount':[amount], 'Duration':[duration],'Treasury':[treasury],'Agency':[Agency], 'MBS':[MBS]}
append_df = pd.DataFrame(append_da... | github_jupyter |
# Time series analysis (Pandas)
Nikolay Koldunov
koldunovn@gmail.com
================
Here I am going to show just some basic [pandas](http://pandas.pydata.org/) stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. If you find this small tutorial useful, I encourage y... | github_jupyter |
<i>Copyright (c) Microsoft Corporation.</i>
<i>Licensed under the MIT License.</i>
# LightGBM: A Highly Efficient Gradient Boosting Decision Tree
This notebook gives an example of how to perform multiple rounds of training and testing of LightGBM models to generate point forecasts of product sales in retail. We will... | github_jupyter |
# External Validation of SWAST Forecasting Model
## Overall results summary.
This notebook generates the overall results summary for the MASE, and prediction intervals for LAS, YAS and WAST.
```
print('******************Summary of External validation results*****************\n\n')
import numpy as np
import pandas as ... | github_jupyter |
# Bayesian Regression - Inference Algorithms (Part 2)
In [Part I](bayesian_regression.ipynb), we looked at how to perform inference on a simple Bayesian linear regression model using SVI. In this tutorial, we'll explore more expressive guides as well as exact inference techniques. We'll use the same dataset as befor... | github_jupyter |
## 1. Google Play Store apps and reviews
<p>Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play a... | github_jupyter |
<a href="https://colab.research.google.com/github/ksetdekov/HSE_DS/blob/master/07%20NLP/kaggle%20hw/solution.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# !pip3 install kaggle
from google.colab import files
files.upload()
!mkdir ~/.kaggle
!c... | github_jupyter |
# Satellite sea surface temperatures along the West Coast of the United States
# during the 2014–2016 northeast Pacific marine heat wave
In 2016 we published a [paper](https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016GL071039) on the heat wave in the ocean off the California coast
This analysis was the last t... | github_jupyter |
# Minimal end-to-end causal analysis with ```cause2e```
This notebook shows a minimal example of how ```cause2e``` can be used as a standalone package for end-to-end causal analysis. It illustrates how we can proceed in stringing together many causal techniques that have previously required fitting together various alg... | github_jupyter |
The data and the description:
https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks
Abstract: The datasets' positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS.
```
import num... | github_jupyter |
## stripplot() and swarmplot()
Many datasets have categorical data and Seaborn supports several useful plot types for this data. In this example, we will continue to look at the 2010 School Improvement data and segment the data by the types of school improvement models used.
As a refresher, here is the KDE distributi... | github_jupyter |
<a href="https://colab.research.google.com/github/elcoreano/DS-Unit-1-Sprint-1-Dealing-With-Data/blob/master/module1-afirstlookatdata/LS_DSPT3_111_A_First_Look_at_Data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Lambda School Data Science - A ... | github_jupyter |
# Training Keyword Spotting
This notebook builds on the Colab in which we used the pre-trained [micro_speech](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/micro_speech) example as well as the HarvardX [3_5_18_TrainingKeywordSpotting.ipynb](https://github.com/tinyMLx/colabs) and [4... | github_jupyter |
```
!pip install scikit-optimize
```
Based on this:
* https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html#sphx-glr-auto-examples-bayesian-optimization-py
```
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.plots import plot_gaussian_process
from skop... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
%matplotlib inline
cc = pd.read_csv('./posts_ccompare_raw.csv', index_col=0, encoding='utf-8')
cc['Timestamp'] = pd.to_datetime(cc['Timestamp'])
```
# Reaction features
```
features_reactions = pd.DataFrame(index=cc.index)
features_reactions['n_up'] = c... | github_jupyter |
```
import os
import json
import pandas as pd
from tqdm import tqdm_notebook
df_larval = pd.read_csv(os.path.join('..', 'data', 'breeding-sites', 'larval-survey-en.csv'))
df_larval.head()
```
## Shapefile
```
with open(os.path.join('..', 'data','shapefiles','Nakhon-Si-Thammarat.geojson')) as f:
data = json.load(... | github_jupyter |
```
# Les imports pour l'exercice
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import string
import random
from collections import deque
```
## Partie 1 : Code de César
### Implementation
Le code suivant contient deux fonctions principales : `encryptMessage` et `decryptMessage`.
Ces fonct... | github_jupyter |
```
# Run in python console
import nltk; nltk.download('stopwords')
```
Import Packages
```
import re
import numpy as np
import pandas as pd
from pprint import pprint
# Gensim
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
from gensim.models import CoherenceModel
# spacy ... | github_jupyter |
# Evaluate AminoAcids Prediction
```
%matplotlib inline
import pylab
pylab.rcParams['figure.figsize'] = (15.0, 12.0)
import os
import sys
import numpy as np
from shutil import copyfile
from src.python.aa_predict import *
import src.python.aa_predict as AA
checkpoint_path = "../../data/trained/aapred_cnn_lates... | github_jupyter |
```
import os
import numpy as np
import torch
torch.manual_seed(29)
from torch import nn
import torch.backends.cudnn as cudnn
import torch.nn.parallel
cudnn.benchmark = True
import torch.nn.functional as F
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from glob import glob
from PIL.PngImage... | github_jupyter |
# Create a Learner for inference
```
from fastai import *
from fastai.gen_doc.nbdoc import *
```
In this tutorial, we'll see how the same API allows you to create an empty [`DataBunch`](/basic_data.html#DataBunch) for a [`Learner`](/basic_train.html#Learner) at inference time (once you have trained your model) and ho... | github_jupyter |
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