code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
import qiskit
import numpy as np, matplotlib.pyplot as plt
import sys
sys.path.insert(1, '../')
import qtm.base, qtm.constant, qtm.nqubit, qtm.onequbit, qtm.fubini_study
num_qubits = 3
num_layers = 2
psi = 2*np.random.rand(2**num_qubits)-1
psi = psi / np.linalg.norm(psi)
qc_origin = qiskit.QuantumCircuit(num_qubits... | github_jupyter |
# Predictive Maintenance using Machine Learning on Sagemaker
*Part 3 - Timeseries data preparation*
## Initialization
---
Directory structure to run this notebook:
```
nasa-turbofan-rul-lstm
|
+--- data
| |
| +--- interim: intermediate data we can manipulate and process
| |
| \--- raw: *immutable* data downloa... | github_jupyter |
# О вероятности попасть под удар фигуры, поставленной случайным образом на шахматную доску
На шахматную доску случайным образом поставлены две фигуры. С какой вероятностью первая фигура бьёт вторую? В данном ноутбуке представлен расчёт этой вероятности для каждой шахматной фигуры как функции от размера доски. Рассмотр... | github_jupyter |
# Collaborative Filtering on Google Analytics Data
### Learning objectives
1. Prepare the user-item matrix and use it with WALS.
2. Train a `WALSMatrixFactorization` within TensorFlow locally and on AI Platform.
3. Visualize the embedding vectors with principal components analysis.
## Overview
This notebook demonstra... | github_jupyter |
```
import numpy as np
from resonance.nonlinear_systems import SingleDoFNonLinearSystem
```
To apply arbitrary forcing to a single degree of freedom linear or nonlinear system, you can do so with `SingleDoFNonLinearSystem` (`SingleDoFLinearSystem` does not support arbitrary forcing...yet).
Add constants, a generalize... | github_jupyter |
# Fit $k_{ij}$ and $r_c^{ABij}$ interactions parameter of Ethanol and CPME
This notebook has te purpose of showing how to optimize the $k_{ij}$ and $r_c^{ABij}$ for a mixture with induced association.
First it's needed to import the necessary modules
```
import numpy as np
from sgtpy import component, mixture, saft... | github_jupyter |
# BAYES CLASSIFIERS
For any classifier $f:{X \to Y}$, it's prediction error is:
$P(f(x) \ne Y) = \mathbb{E}[ \mathbb{1}(f(X) \ne Y)] = \mathbb{E}[\mathbb{E}[ \mathbb{1}(f(X) \ne Y)|X]]$
For each $x \in X$,
$$\mathbb{E}[ \mathbb{1}(f(X) \ne Y)|X = x] = \sum\limits_{y \in Y} P(Y = y|X = x) \cdot \mathbb{1}(f(x) \ne... | github_jupyter |
# Mini Project: Temporal-Difference Methods
In this notebook, you will write your own implementations of many Temporal-Difference (TD) methods.
While we have provided some starter code, you are welcome to erase these hints and write your code from scratch.
### Part 0: Explore CliffWalkingEnv
Use the code cell below... | github_jupyter |
<a href="https://colab.research.google.com/github/elizabethts/DS-Unit-1-Sprint-1-Dealing-With-Data/blob/master/LS_DS_114_Making_Data_backed_Assertions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Lambda School Data Science - Making Data-backed ... | github_jupyter |
```
import numpy as np
import pandas_datareader as pdr
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
start = dt.datetime(2012, 6, 1)
end = dt.datetime(2022, 3, 9)
stock = ['fb']
stock_data = pdr.get_data_yahoo(stock, start, end)
pair = ['snap']
pair_data = pdr.get_data_yahoo(pair, start, e... | github_jupyter |
# Convolutional Neural Networks
---
In this notebook, we train a **CNN** to classify images from the CIFAR-10 database.
The images in this database are small color images that fall into one of ten classes; some example images are pictured below.
<img src='notebook_ims/cifar_data.png' width=70% height=70% />
### Test... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp data.core
#export
from fastai.torch_basics import *
from fastai.data.load import *
#hide
from nbdev.showdoc import *
```
# Data core
> Core functionality for gathering data
The classes here provide functionality for ... | github_jupyter |
<img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
# <span style="text-align: right; direction: rtl; float: r... | github_jupyter |
```
import os
import json
import pickle
import random
from collections import defaultdict, Counter
from indra.literature.adeft_tools import universal_extract_text
from indra.databases.hgnc_client import get_hgnc_name, get_hgnc_id
from adeft.discover import AdeftMiner
from adeft.gui import ground_with_gui
from adeft.m... | github_jupyter |
# Identificar Perfil de Consumo de Clientes de uma Instituição Financeira
## Uma breve introdução
Uma Instituição Financeira X tem o interesse em identificar o perfil de gastos dos seus clientes. Identificando os clientes certos, eles podem melhorar a comunicação dos ativos promocionais, utilizar com mais eficiência ... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
## Fundamentals, introduction to machine learning
The purpose of these guides is to go a bit deeper into the details behind common machine learning methods, assuming little math background, and teach you how to use popular machine learning Python packages. In particular, we'll focus on the Numpy and PyTorch libraries... | github_jupyter |
# Check Environment
This notebook checks that you have correctly created the environment and that all packages needed are installed.
## Environment
The next command should return a line like (Mac/Linux):
/<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/bin/python
or like (Windows 10):
C:\\<YOUR-HOME-FOLDER>\\Anacond... | 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("charity_data.csv")
application_df... | github_jupyter |
# The Graph Data Access
In this notebook, we read in the data that was generated and saved as a csv from the [TheGraphDataSetCreation](TheGraphDataSetCreation.ipynb) notebook.
Goals of this notebook are to obtain:
* Signals, states, event and sequences
* Volatility metrics
* ID perceived shocks (correlated with an... | github_jupyter |
```
import pandas as pd
import numpy as np
import pickle
BASEDIR_MIMIC = '/Volumes/MyData/MIMIC_data/mimiciii/1.4'
def get_note_events():
n_rows = 100000
icd9_code = pd.read_csv(f"{BASEDIR_MIMIC}/DIAGNOSES_ICD.csv", index_col = None)
# create the iterator
noteevents_iterator = pd.read_csv(
f"{... | github_jupyter |
# Recurrent Neural Networks
```
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
```
## Time series forecasting
```
df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',
skiprows=6, skipfooter=9,
engine='python')
df.head()
fr... | github_jupyter |
# Handwritten Digits Classifier with Improved Accuracy using Data Augmentation
In previous steps, we trained a model that could recognize handwritten digits using the MNIST dataset. We were able to achieve above 98% accuracy on our validation dataset. However, when you deploy the model in an Android app and test it, y... | github_jupyter |
<a href="https://colab.research.google.com/github/ilexistools/ebralc2021/blob/main/nltk_treinar_etiquetador.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
```
# Treinar um etiquetador morfossintático
Para realizar a etiquetagem morfossintátic... | github_jupyter |
# An example of an optimal pruning based routing algorithm
- based on a simple graph and Dijkstra's algorithm with concave cost function
- Create a simple graph with multiple edge's attributes¶
- weight = w_ij
- concave = c_ij where i,j is nodes
```
import networkx as nx
import matplotlib.pyplot as plt
```
##... | github_jupyter |
# Machine Learning Engineer Nanodegree
## Introduction and Foundations
## Project: Titanic Survival Exploration
In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Ti... | github_jupyter |
# Make Corner Plots of Posterior Distributions
This file allows me to quickly and repeatedly make the cornor plot to examin the results of the MCMC analsys
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
from astropy.table import Table
import corner
# import seaborn
matplo... | github_jupyter |
```
import pandas as pd
import sqlite3
conn = sqlite3.connect('database.sqlite')
query = "SELECT * FROM sqlite_master"
df_schema = pd.read_sql_query(query, conn)
df_schema.tbl_name.unique()
df_schema.head(20)
cur = conn.cursor()
cur.execute('''SELECT * from pragma_table_info("Player_Attributes")''' )
rows = cur.fet... | github_jupyter |
# Importing the libraries
```
%pip install tensorflow
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import seaborn as sns
```
# Importing The Dataset
```
dataset = pd.read_csv("../input/framingham-heart-study-dataset/framingham.csv")
```
# Analysing The Data
```
dat... | github_jupyter |
# Reinforcement Learning
page 441<br>
For details, see
- https://github.com/ageron/handson-ml/blob/master/16_reinforcement_learning.ipynb,
- https://gym.openai.com,
- http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html,
- https://www.jstor.org/stable/24900506?seq=1#metadata_info_tab_contents,
- http://book.python... | github_jupyter |
# Dynamic Recurrent Neural Network.
TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length.
## RNN Overview
<img src="http://co... | github_jupyter |
```
import sys, os
sys.path.append(os.path.abspath('../..'))
from tqdm.notebook import tqdm
import math
import gym
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from collections import deque
from networks.dqn_atari import MC_DQN
from utils.memory import RankedReplayMemory,... | github_jupyter |
# **Exploratory Analysis**
First of all, let's import some useful libraries that will be used in the analysis.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
```
Now, the dataset stored in drive needs to be retieved. I am using google colab for this exploration with TPU hardware accelerat... | github_jupyter |
# Laboratorio 7
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import altair as alt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
alt.themes.enable('opaque')
%matplotlib inline
```
En este laboratorio utilizaremos los mismos datos de ... | github_jupyter |
```
import radical.analytics as ra
import radical.pilot as rp
import radical.utils as ru
import radical.entk as re
import os
from glob import glob
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
import csv
import pandas as pd
import json
import matplotlib as mpl
mpl.rcParams['text.uset... | github_jupyter |
# Running, Debugging, Testing & Packaging
```
!code ./1-helloconnectedworld
```
Let's look at the key parts of our app:
**package.json**
This defines all contributions: commands, context menus, UI, everything!
```json
"activationEvents": [
// Use "*" to start on application start. If contributing comm... | github_jupyter |
# Convolutional Autoencoder
Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.
```
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import i... | github_jupyter |
Practice geospatial aggregations in geopandas before writing them to .py files
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../utils')
import wd_management
wd_management.set_wd_root()
import geopandas as gp
import pandas as pd
import requests
res = requests.get('https://services5.arcgis.com/GfwWN... | github_jupyter |
<a href="https://colab.research.google.com/github/prateekjoshi565/Fine-Tuning-BERT/blob/master/Fine_Tuning_BERT_for_Spam_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Install Transformers Library
```
!pip install transformers
imp... | github_jupyter |
# WeatherPy
----
#### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
import rando... | github_jupyter |
<a href="https://colab.research.google.com/github/jorge23amury/daa_2021_1/blob/master/4_diciembre1358.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
"""
Array2D
"""
class Array2D:
def __init__(self,rows, cols, value):
self.__cols ... | github_jupyter |
# Run hacked AlphaFold2 on the designed bound states
### Imports
```
%load_ext lab_black
# Python standard library
from glob import glob
import os
import socket
import sys
# 3rd party library imports
import dask
import matplotlib.pyplot as plt
import pandas as pd
import pyrosetta
import numpy as np
import scipy
impo... | github_jupyter |
<a href="https://colab.research.google.com/github/parshwa1999/Map-Segmentation/blob/master/ResNet_RoadTest.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Segmentation of Road from Satellite imagery
## Importing Libraries
```
import warnings
war... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplo... | github_jupyter |
## Homework 4
Today we'll start by reproducing the DQN and then try improving it with the tricks we learned on the lecture:
* Target networks
* Double q-learning
* Prioritized experience replay
* Dueling DQN
* Bootstrap DQN
```
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# If you are runni... | github_jupyter |
```
# installing keras
!pip install keras
# installing opencv
!pip install opencv-python
# installing opencv full package
!pip install opencv-contrib-python
import cv2
from keras.models import load_model
import numpy as np
face_detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def face_dete... | github_jupyter |
```
# (c) amantay from https://github.com/AmantayAbdurakhmanov/misc/blob/master/Geabox-Yearn.ipynb
import json
import os
from dotenv import load_dotenv
from web3 import Web3
from multicall import Call, Multicall
load_dotenv() # add this line
RPC_Endpoint = os.getenv('RPC_NODE')
GearboxAddressProvider = Web3.toChecks... | github_jupyter |
```
#!/usr/bin/env python
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.tri as tri
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from matplotlib import ticker, cm
import numpy as np
from numpy import ma
import csv
degree_sign= u'\N{DEGREE SIGN}'
CSV_FILE_PA... | github_jupyter |
__mlmachine - GroupbyImputer, KFoldEncoder, and Skew Correction__
<br><br>
Welcome to Example Notebook 2. If you're new to mlmachine, check out [Example Notebook 1](https://github.com/petersontylerd/mlmachine/blob/master/notebooks/mlmachine_part_1.ipynb).
<br><br>
Check out the [GitHub repository](https://github.com/pe... | github_jupyter |
```
# default_exp analysis
```
# Tools to analyze the results of Gate simulations
```
#hide
from nbdev.showdoc import *
```
## Dependencies
```
#export
import pandas as pd
import uproot as rt
import awkward as ak
from scipy.stats import moyal
import matplotlib.pyplot as plt
import numpy as np
import math
from scip... | github_jupyter |
# 序列到序列学习（seq2seq）
在`seq2seq`中，
特定的“<eos>”表示序列结束词元。
一旦输出序列生成此词元，模型就会停止预测。
在循环神经网络解码器的初始化时间步，有两个特定的设计决定：
首先，特定的“<bos>”表示序列开始词元，它是解码器的输入序列的第一个词元。
其次，使用循环神经网络编码器最终的隐状态来初始化解码器的隐状态。
这种设计将输入序列的编码信息送入到解码器中来生成输出序列的。
在其他一些设计中 :cite:`Cho.Van-Merrienboer.Gulcehre.ea.2014`，
编码器最终的隐状态在每一个时间步都作为解码器的输入序列的一部分。
类似于 :`lang... | github_jupyter |
# Now You Code 2: Character Frequency
Write a program to input some text (a word or a sentence). The program should create a histogram of each character in the text and it's frequency. For example the text `apple` has a frequency `a:1, p:2, l:1, e:1`
Some advice:
- build a dictionary of each character where the char... | github_jupyter |
## Deep face recognition with Keras, Dlib and OpenCV
Face recognition identifies persons on face images or video frames. In a nutshell, a face recognition system extracts features from an input face image and compares them to the features of labeled faces in a database. Comparison is based on a feature similarity metr... | github_jupyter |
```
dtypes = {
'MachineIdentifier': 'category',
'ProductName': 'category',
'EngineVersion': 'category',
'AppVersion': 'category',
... | github_jupyter |
```
import pytorch_lightning as pl
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader, random_split
from torch.utils.data.distributed import DistributedSampler
import numpy as np
import pandas as pd
import torch as torch
from pathlib import Path
impor... | github_jupyter |
```
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
# helper function to plot 3D NIfTI
def plot_slice (fname):
# Load image
img = nb.load (fname)
data = img.get_data ()
# cut in the middle of brain
cut = int (data.shape[-1]/2) + 10
# plot data
plt.imsh... | github_jupyter |
# Mapping water extent and rainfall using WOfS and CHIRPS
* **Products used:**
[wofs_ls](https://explorer.digitalearth.africa/products/wofs_ls),
[rainfall_chirps_monthly](https://explorer.digitalearth.africa/products/rainfall_chirps_monthly)
## Background
The United Nations have prescribed 17 "Sustainable Developme... | github_jupyter |
# Overview
1. Project Instructions & Prerequisites
2. Learning Objectives
3. Data Preparation
4. Create Categorical Features with TF Feature Columns
5. Create Continuous/Numerical Features with TF Feature Columns
6. Build Deep Learning Regression Model with Sequential API and TF Probability Layers
7. Evaluating Potent... | github_jupyter |
<font size ='3'>*First, let's read in the data and necessary libraries*<font/>
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mypy import print_side_by_side
from mypy import display_side_by_side
#https://stackoverflow.com/a/44923103/8067752
%matplotlib inline
pd.... | github_jupyter |
# Appendix
Hao Lu 04/04/2020
In this notebook, we simulated EEG data with the method described in the paper by Bharadwaj and Shinn-Cunningham (2014) and analyzed the data with the toolbox proposed in the same paper.
The function was modifed so the values of thee variables within the function can be extracted and stu... | github_jupyter |
# Statistics
## Introduction
In this chapter, you'll learn about how to do statistics with code. We already saw some statistics in the chapter on probability and random processes: here we'll focus on computing basic statistics and using statistical tests. We'll make use of the excellent [*pingouin*](https://pingouin-... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
import cv2
np.set_printoptions(threshold=np.inf)
num_images = 3670
dataset = []
for i in range(1, num_images+1):
img = cv2.imread("color_images/color_" +str(i) +".jpg" )
dataset.append(np.array... | github_jupyter |
# CarND Object Detection Lab
Let's get started!
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from PIL import ImageDraw
from PIL import ImageColor
import time
from scipy.stats import norm
%matplotlib inline
plt.style.use('ggplot')
```
## MobileNets
[*MobileNet... | github_jupyter |
```
import sys
import json
sys.path.insert(0, "../")
print(sys.path)
import pymongo
#31470/5/1
import sys
import json
import cobrakbase
import cobrakbase.core.model
import cobra
import logging
#from cobra.core import Gene, Metabolite, Model, Reaction
#from pyeda import *
#from pyeda.inter import *
#from pyeda.boolalg i... | github_jupyter |
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import classification_report, confusion_matrix, f1_score
from sklearn.metrics import make_scorer, f1_score, accuracy_score, recall_score, precis... | github_jupyter |
```
# importing
import tensorflow as tf
import matplotlib.pyplot as plt
import os
# loading images
path_dir = "/content/drive/MyDrive/Dataset/malariya_cell_data_set/cell_images/"
loaded = 0
path = path_dir+"Uninfected/"
uninfected_list = os.listdir(path)
path = path_dir + "Parasitized"
infected_list = os.listdir(path... | github_jupyter |
# Using Neural Network Formulations in OMLT
In this notebook we show how OMLT can be used to build different optimization formulations of neural networks within Pyomo. It specifically demonstrates the following examples:<br>
1.) A neural network with smooth sigmoid activation functions represented using full-space and... | github_jupyter |
# transmissibility-based TPA: FRF based
In this example a numerical example is used to demonstrate a FRF based TPA example.
```
import pyFBS
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LogNorm
%matplotlib inline
```
## Example datase... | github_jupyter |
```
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
import sklearn.metrics
pd.reset_option('all')
# 84575189_0_6365692015941409487 -> no matches at all
data = pd.read_csv('/Users/summ7t/dev/novartis/table-linker/SemTab2019/embedding_evaluation_file... | github_jupyter |
# Homework 5: Problems
## Due Wednesday 28 October, before class
### PHYS 440/540, Fall 2020
https://github.com/gtrichards/PHYS_440_540/
## Problems 1&2
Complete Chapters 1 and 2 in the *unsupervised learning* course in Data Camp. The last video (and the two following code examples) in Chapter 2 are off topic, but... | github_jupyter |
### Dataset Source:
About this file
Boston House Price dataset
### columns:
* CRIM per capita crime rate by town
* ZN proportion of residential land zoned for lots over 25,000 sq.ft.
* INDUS proportion of non-retail business acres per town
* CHAS Charles River dummy variable (= 1 if tract bounds rive... | github_jupyter |
# 第５章 計算機を作る
## 5.1.2 スタックマシン
```
def calc(expression: str):
# 空白で分割して字句にする
tokens = expression.split()
stack = []
for token in tokens:
if token.isdigit():
# 数値はスタックに push する
stack.append(int(token))
continue
# 数値でないなら，演算子として処理する
x = sta... | github_jupyter |
# Week 3 - Ungraded Lab: Data Labeling
Welcome to the ungraded lab for week 3 of Machine Learning Engineering for Production. In this lab, you will see how the data labeling process affects the performance of a classification model. Labeling data is usually a very labor intensive and costly task but it is of great im... | github_jupyter |
# A Whirlwind Tour of Python
*Jake VanderPlas, Summer 2016*
These are the Jupyter Notebooks behind my O'Reilly report,
[*A Whirlwind Tour of Python*](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp).
The full notebook listing is available [on Github](https://github.com/jakevdp/WhirlwindTourOfPy... | github_jupyter |
```
epochs = 5
```
# Example - Simple Vertically Partitioned Split Neural Network
- <b>Alice</b>
- Has model Segment 1
- Has the handwritten Images
- <b>Bob</b>
- Has model Segment 2
- Has the image Labels
Based on [SplitNN - Tutorial 3](https://github.com/OpenMined/PySyft/blob/master/examples/tu... | github_jupyter |
## 1-3. 複数量子ビットの記述
ここまでは１量子ビットの状態とその操作（演算）の記述について学んできた。この章の締めくくりとして、$n$個の量子ビットがある場合の状態の記述について学んでいこう。テンソル積がたくさん出てきてややこしいが、コードをいじりながら身につけていってほしい。
$n$個の**古典**ビットの状態は$n$個の$0,1$の数字によって表現され、そのパターンの総数は$2^n$個ある。
量子力学では、これらすべてのパターンの重ね合わせ状態が許されているので、$n$個の**量子**ビットの状態$|\psi \rangle$はどのビット列がどのような重みで重ね合わせになっているかという$2^n$個の複素確率振幅で記... | github_jupyter |
# Evaluation of a Pipeline and its Components
[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
To be able to make a statement about the quality of results a question-answering pip... | github_jupyter |
```
%matplotlib inline
from __future__ import print_function, unicode_literals
import sys, os
import seaborn as sns
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from pygaarst import raster
sys.path.append('../firedetection/')
import landsat8fire as lfire
sns.set(rc={'image.cmap': 'gist_heat... | github_jupyter |
# Finding fraud patterns with FP-growth
# Data Collection and Investigation
```
import pandas as pd
# Input data files are available in the "../input/" directory
df = pd.read_csv('D:/Python Project/Credit Card Fraud Detection/benchmark dataset/Test FP-Growth.csv')
# printing the first 5 columns for data visualizati... | github_jupyter |
```
import pandas as pd
import sqlite3
import datetime
def main():
data_list = [("ml-latest-small/", "small_output/", "small"), ("ml-20m/", "20M_output/","20M")]
for item in data_list:
start_time = datetime.datetime.now()
process_data(item[0], item[1])
end_tim... | github_jupyter |
```
%cd ..
%ls
import pandas as pd
import datetime
import numpy as np
# xls to csv
xls = pd.read_excel(u'107年 竹苗空品區/107年新竹站_20190315.xls', index_col=0)
xls.to_csv('107年 竹苗空品區/107年新竹站_20190315.csv', encoding='big5')
train = pd.read_csv('107年 竹苗空品區/107年新竹站_20190315.csv', encoding='big5', index_col = False)
train.iloc[26]... | github_jupyter |
```
### MODULE 1
### Basic Modeling in scikit-learn
```
```
### Seen vs. unseen data
# The model is fit using X_train and y_train
model.fit(X_train, y_train)
# Create vectors of predictions
train_predictions = model.predict(X_train)
test_predictions = model.predict(X_test)
# Train/Test Errors
train_error = mae(y_tr... | github_jupyter |
# Goals
### Learn how to change train validation splits
# Table of Contents
## [0. Install](#0)
## [1. Load experiment with defaut transforms](#1)
## [2. Reset Transforms andapply new transforms](#2)
<a id='0'></a>
# Install Monk
- git clone https://github.com/Tessellate-Imaging/monk_v1.git
- cd monk_v... | github_jupyter |
```
import pandas as pd
import numpy as np
import numpy.random as nr
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import sklearn
from sklearn.ensemble import RandomForestClassifier
import catboost as cat
from catboost import CatBoostClassifier
from sklearn import preprocessing
import sklearn.model_se... | github_jupyter |
# Dataproc - Submit Hadoop Job
## Intended Use
A Kubeflow Pipeline component to submit a Apache Hadoop MapReduce job on Apache Hadoop YARN in Google Cloud Dataproc service.
## Run-Time Parameters:
Name | Description
:--- | :----------
project_id | Required. The ID of the Google Cloud Platform project that the cluste... | github_jupyter |
# Random Variables
:label:`sec_random_variables`
In :numref:`sec_prob` we saw the basics of how to work with discrete random variables, which in our case refer to those random variables which take either a finite set of possible values, or the integers. In this section, we develop the theory of *continuous random var... | github_jupyter |
# Capstone Project - Madiun Cafe Location
## Introduction / business problem
i am looking to open a cafe in Madiun City, **the question is**, where is the best location for open new cafe? **The background of the problem** it is not worth setting up a cafe in the close promixity of existing ones. because the location ... | github_jupyter |
<div>
<img src="https://drive.google.com/uc?export=view&id=1vK33e_EqaHgBHcbRV_m38hx6IkG0blK_" width="350"/>
</div>
#**Artificial Intelligence - MSc**
This notebook is designed specially for the module
ET5003 - MACHINE LEARNING APPLICATIONS
Instructor: Enrique Naredo
###ET5003_BayesianNN
© All rights reserved to t... | github_jupyter |
# 函数
- 函数可以用来定义可重复代码，组织和简化
- 一般来说一个函数在实际开发中为一个小功能
- 一个类为一个大功能
- 同样函数的长度不要超过一屏
Python中的所有函数实际上都是有返回值(return None),
如果你没有设置return,那么Python将不显示None.
如果你设置return,那么将返回出return这个值.
```
def HJN():
print('Hello')
return 1000
b=HJN()
print(b)
HJN
def panduan(number):
if number % 2 == 0:
print('O')
e... | github_jupyter |
```
import tensorflow as tf
import os
import pickle
import numpy as np
CIFAR_DIR = "./../../cifar-10-batches-py"
print(os.listdir(CIFAR_DIR))
def load_data(filename):
"""read data from data file."""
with open(filename, 'rb') as f:
data = pickle.load(f, encoding='bytes')
return data[b'data'], da... | github_jupyter |
```
import pandas as pd
import numpy as np
X_raw = pd.read_csv("/Users/joejohns/data_bootcamp/GitHub/final_project_nhl_prediction/Data/Shaped_Data/data_bet_stats_mp.csv")
##note it is against, for .. then sometimes *by itself* stat% = for/(for+against); stat%_against would be 1-stat%
##basic features to add:
##pp %
##... | github_jupyter |
# Linear Regression
We will implement a linear regression model by using the Keras library.
```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
```
## Data set: Weight and height
Active Drive and read the csv file with the weight and height data
```
df = pd.read_csv('/cont... | github_jupyter |
```
print(__doc__)
from time import time
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
np.random.seed(42)
digit... | github_jupyter |
# Training vs validation loss
[](https://colab.research.google.com/github/parrt/fundamentals-of-deep-learning/blob/main/notebooks/3.train-test-diabetes.ipynb)
By [Terence Parr](https://explained.ai).
This notebook explores how to use a validat... | github_jupyter |
# Simple Attack
In this notebook, we will examine perhaps the simplest possible attack on an individual's private data and what the OpenDP library can do to mitigate it.
## Loading the data
The vetting process is currently underway for the code in the OpenDP Library.
Any constructors that have not been vetted may st... | github_jupyter |
```
import tensorflow as tf
import h5py
import shutil
import numpy as np
from torch.utils.data import DataLoader
import keras
from tqdm.notebook import tqdm
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv3D, Dropout, MaxPooling3D,MaxPooling2D
from keras.utils import to_categorical
from... | github_jupyter |
© 2018 Suzy Beeler and Vahe Galstyan. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT)
This exercise was generated from a Jupyter notebook. You... | github_jupyter |
# 1. Python and notebook basics
In this first chapter, we will cover the very essentials of Python and notebooks such as creating a variable, importing packages, using functions, seeing how variables behave in the notebook etc. We will see more details on some of these topics, but this very short introduction will the... | github_jupyter |
<a href="http://cocl.us/pytorch_link_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " />
</a>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN... | github_jupyter |
# Classifying Fashion-MNIST
Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 9... | github_jupyter |
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