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# Processing IoT data
## Summary
This notebook explains how to process telemetry data coming from IoT devices that arrives trough a gateway enabled edgeHub.
## Description
The purpose of this notebook is to explain and guide the reader onto how to process telemetry data generated from IoT devices whitin the DSVM ... | github_jupyter |
# DALI expressions and arithmetic operators
In this example, we will show simple examples how to use binary arithmetic operators in DALI Pipeline that allow for element-wise operations on tensors inside a pipeline. We will show available operators and examples of using constant and scalar inputs.
## Supported operato... | github_jupyter |
## Face and Facial Keypoint detection
After you've trained a neural network to detect facial keypoints, you can then apply this network to *any* image that includes faces. The neural network expects a Tensor of a certain size as input and, so, to detect any face, you'll first have to do some pre-processing.
1. Detect... | github_jupyter |
# Detailed execution time for cadCAD models
*Danilo Lessa Bernardineli*
---
This notebook shows how you can use metadata on PSUBs in order to do pre-processing on the simulations. We use two keys for flagging them: the `ignore` which indicates which PSUBs we want to skip, and the `debug`, which informs us what are t... | github_jupyter |
### Ch6 Figure1
```
# Think about your running shoe website. A data analyst should have little trouble finding websites that referred customers to the store. Let's say that most of your customers came from Twitter, Google and Facebook. There were also quite a few customers that came from running shoe websites. A good ... | github_jupyter |
# Exercise: FPGA and the DevCloud
Now that we've walked through the process of requesting an edge node with a CPU and Intel® Arria 10 FPGA on Intel's DevCloud and loading a model on the Intel® Arria 10 FPGA, you will have the opportunity to do this yourself with the addition of running inference on an image.
In this ... | github_jupyter |
```
import csv
import argparse
import json
from collections import defaultdict, Counter
import re
from annotation_tool_1 import MAX_WORDS
def process_repeat_dict(d):
if d["loop"] == "ntimes":
repeat_dict = {"repeat_key": "FOR"}
processed_d = process_dict(with_prefix(d, "loop.ntimes."))
if '... | github_jupyter |
```
%matplotlib inline
```
# `scikit-learn` - Machine Learning in Python
[scikit-learn](http://scikit-learn.org) is a simple and efficient tool for data mining and data analysis. It is built on [NumPy](www.numpy.org), [SciPy](https://www.scipy.org/), and [matplotlib](https://matplotlib.org/). The following examples s... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
# 神经网络
## 全连接层
### 张量方式实现
```
import tensorflow as tf
from matplotlib import pyplot as plt
plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = ['STKaiti']
plt.rcParams['axes.unicode_minus'] = False
# 创建 W,b 张量
x = tf.random.normal([2,784])
w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
b... | github_jupyter |
```
%matplotlib inline
```
# OTDA unsupervised vs semi-supervised setting
This example introduces a semi supervised domain adaptation in a 2D setting.
It explicits the problem of semi supervised domain adaptation and introduces
some optimal transport approaches to solve it.
Quantities such as optimal couplings, gre... | github_jupyter |
```
import numpy as np
import pandas as pd
import pickle
import math
import pandas as pd
from pandas import HDFStore
import argparse
###################################################################################
#location
node_ids_filename = 'data/node_locate.txt'
with open(node_ids_filename) as f:
_node_ids... | github_jupyter |
# Tutorial 10:
## Extreme Gradient Boosting Classification
Extreme Gradient Boosting, most popularly known as XGBoost is a gradient boosting algorithm that is used for both classification and regression problems. XGBoost is a star among hackathons as a winning algorithm. XGBoost provides a parallel tree boosting that ... | github_jupyter |
## Imports
```
import pandas as pd
import sys
sys.path.insert(0,'../satori')
from postprocess import *
```
## Interaction data processing
```
# For SATORI based interactions
df = pd.read_csv('../results/Arabidopsis_GenomeWide_Analysis_euclidean_v8_fixed/Interactions_SATORI/interactions_summary_attnLimit-0.12.txt',... | github_jupyter |
# "Namentliche Abstimmungen" in the Bundestag
> Parse and inspect "Namentliche Abstimmungen" (roll call votes) in the Bundestag (the federal German parliament)
[](https://mybinder.org/v2/gh/eschmidt42/bundestag/HEAD)
## Context
The German Parliament is so friendly to p... | github_jupyter |
# Getter example
The example of simple Getter class usage and even simpler analysis on recieved data.
## Preparations
Import instabot from sources
```
import sys
sys.path.append('../../')
from instabot import User, Getter
```
Login users to be used in instabot. I suggest you to add as many users as you have because... | github_jupyter |
# Pushing an image along a Hilbert curve
This started out as a discussion with my son about enumerating $\mathbb{N} \times \mathbb{N}$.
Cantor found a bijection $\mathbb{N} \rightarrow \mathbb{N} \times \mathbb{N}$.
Hilbert found a better bijection $\mathbb{N} \rightarrow \mathbb{N} \times \mathbb{N}$
which can be... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import get_dummies
data = pd.read_csv("A0201.csv", sep=",")
data.head()
indexes = data['sequence'][data['length'] == 9].index
#indexes = data.index
selected_X = data['sequence'][indexes]
selected_y = pd.DataFrame(d... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegressionCV
import sklearn.metrics as metrics
from sklearn.preprocessing import PolynomialFeatures
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.disc... | github_jupyter |
```
# !pip install -q tf-nightly
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import numpy as np
import matplotlib.pyplot as plt
print("Tensorflow Version: {}".format(tf.__version_... | github_jupyter |
# Matrix product state simulation method
## Simulation methods
The `QasmSimulator` has several simulation methods including `statevector`, `stabilizer`, `extended_stabilizer` and `matrix_product_state`. Each of these determines the internal representation of the quantum circuit and the algorithms used to process the q... | github_jupyter |
# Initilization
```
!mkdir -p models
!wget "https://drive.google.com/uc?id=1E95HNEYQI1R-UTuYwJOycrYDJxFxiICC&export=download" -O songs.p
!pip install tensorflow-gpu==1.14
```
# Dataset Preparation
```
%pylab inline
import pickle
import pandas as pd
import keras
from music21 import converter, instrument, note, chord,... | github_jupyter |
# 8. Dashboard
In this notebook we created a dashboard based on a little EDA with the tmdb dataset. As output we decided, the dashboard should contain a figure with the most expensive movies, the most popular ones, the proportion of different movie genres, the production countries and the releases over the year. As co... | github_jupyter |
# Learning Objectives:
1. Reading files
2. Exploring the read dataframe
3. Checking the dataframe info
4. Merging the two dataframes into one
5. Defining questions for the analysis
6. Cleaning Steps
```
import numpy as np
import pandas as pd
```
## Reading the files
```
movies_df = pd.read_csv("../data/movies.csv")... | github_jupyter |
```
import pickle
import os
import numpy as np
base_dirs = ['/localdata/juan/inferno/',
'/localdata/juan/erehwon/',
'/localdata/juan/numenor/']
experiments = ['dcp_mcpilco_dropoutd_mlpp_4',
'dcp_mcpilco_lndropoutd_mlpp_6',
'dcp_mcpilco_dropoutd_dropoutp_7',
... | github_jupyter |
```
# This cell is added by sphinx-gallery
!pip install mrsimulator --quiet
%matplotlib inline
import mrsimulator
print(f'You are using mrsimulator v{mrsimulator.__version__}')
```
# ¹¹⁹Sn MAS NMR of SnO
The following is a spinning sideband manifold fitting example for the 119Sn MAS NMR
of SnO. The dataset was ac... | github_jupyter |
---
```
__authors__ = ["Tricia D Shepherd" , "Ryan C. Fortenberry", "Matthewy Kennedy", "C. David Sherril"]
__credits__ = ["Victor H. Chavez", "Lori Burns"]
__email__ = ["profshep@icloud.com", "r410@olemiss.edu"]
__copyright__ = "(c) 2008-2019, The Psi4Education Developers"
__license__ = "BSD-3-Clause"
__date__ ... | github_jupyter |
Utilities to visualize agent's trade execution and portfolio performance
Chapter 4, TensorFlow 2 Reinforcement Learning Cookbook | Praveen Palanisamy
```
import matplotlib
import matplotlib.pyplot as plt
import mplfinance as mpf
import numpy as np
import pandas as pd
from matplotlib import style
from mplfinance.origin... | github_jupyter |
### Preparing Working Env
```
import matplotlib.pyplot as plt
import numpy as np
from importlib.util import find_spec
if find_spec("core") is None:
import sys
sys.path.append('..')
import tensorflow as tf
import tensorflow_datasets as tfds
import random
from core.datasets import RetinaDataset
from core.datas... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/CLASSIFICATION_EN_TREC.ipynb)
# **Classify text accordi... | github_jupyter |
# Downloading and saving CSV data files from the web
```
import urllib.request
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'
csv_cont = urllib.request.urlopen(url)
csv_cont = csv_cont.read() # .decode('utf-8')
# saving the data to local drive
#with open('./datasets/wine_data.csv', '... | github_jupyter |
# FastAI Experiments Using Google Colab CPU
<a href="https://colab.research.google.com/github/rambasnet/DeepLearningMaliciousURLs/blob/master/FastAI-Experiments.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Import Libraries
```
from fastai.ta... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os, sys
currentdir = os.path.dirname(os.path.realpath("__file__"))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
```
# 2. Index assets
In order to build a mosaic, we want to replace each image part with
a matching source picture (a patch). We will ru... | github_jupyter |
# BERT + Keras 对新闻标题分类
日期：2020年4月3日
此方法与 PyTorch 的前半部分基本一致。
```
import os
import re
import time
import numpy as np
import pandas as pd
import transformers
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import tensorflow as tf
import tensorflow.keras.backend as K
... | github_jupyter |
# Final Project Report Group 10
## <u> Insurance Cross Selling <u>
Ashwin Yenigalla. Natwar Koneru, Pratheep Raju, Rahul Narang
### <u> Data Dictionary <u>
The dataset is from Kaggle.com:
https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction
Unique ID Rows: 381109 values Data Features are a... | github_jupyter |
```
import os
import json
from docx import Document
from io import StringIO, BytesIO
import re
import time
import pandas as pd
import json
import spacy
from nltk.corpus import stopwords
from gensim.models import LdaModel
from gensim.models.wrappers import LdaMallet
import gensim.corpora as corpora
from gensim.corpora... | github_jupyter |
# The central limit theorem
## Understanding via visualization
#### Giovanni Pizzi (EPFL), Sep 2018
[Go back to the list of all visualizations](https://github.com/giovannipizzi/educational-scientific-visualizations/)
# Aim of this app
The aim of this app is to:
- visually prove the central limit theorem
- give a feeli... | github_jupyter |
<a href="https://colab.research.google.com/github/gmihaila/machine_learning_things/blob/master/learning_pytorch/pytorch_nn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### SImple NN
1 hiddent layer NN
#### Initialize NN
```
import torch
n_inp... | github_jupyter |
# Gibbs Sampling
[Casella 1992](http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf)
Suppose we are given a joint density $f(x, y_1, \ldots, y_p)$ and are interested in obtaining the characteristics of the marginal density
$$
f(x) = \int\ldots\int f(x, y_1,\ldots, y_p)dy_1\ldots dy_p
$$
such as the mean or va... | 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 |
# A/B test 2 - Loved journeys, control vs content similarity sorted list
This related links A/B test (ab2) was conducted from 26 Feb -5th March 2019.
The data used in this report are 27th Feb 2019 - 5th March because on 26th the split was not 50:50.
The test compared the existing related links (where available) to l... | github_jupyter |
# The Acrobot (v-1) Problem
Acrobot is a 2-link pendulum with only the second joint actuated.
Intitially, both links point downwards. The goal is to swing the
end-effector at a height at least the length of one link above the base.
Both links can swing freely and can pass by each other, i.e., they don't
collide when ... | github_jupyter |
```
import numpy as np
from numpy import ndarray
from typing import List
def assert_same_shape(array: ndarray,
array_grad: ndarray):
assert array.shape == array_grad.shape, \
'''
두 ndarray의 모양이 같아야 하는데,
첫 번째 ndarray의 모양은 {0}이고,
두 번째 ndarray의 모양은 {1}이다.
... | github_jupyter |
```
from __future__ import print_function, division
from keras.datasets import fashion_mnist
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
import os
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from keras.layers import BatchNormalization, Activation, Embeddi... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
# path = "/content/drive/MyDrive/Research/cods_comad_plots/sdc_task/mnist/"
m = 100
desired_num = 100
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import to... | github_jupyter |
# py12box model usage
This notebook shows how to set up and run the AGAGE 12-box model.
## Model schematic
The model uses advection and diffusion parameters to mix gases between boxes. Box indices start at the northern-most box and are as shown in the following schematic:
<img src="box_model_schematic.png" alt="Box... | github_jupyter |
# Windows Metadata Structure and Value Issues
This notebook shows a few examples of the varience that occurs and encumbers parsing windows metadata extracted and serialised via `Get-EventMetadata.ps1` into the file `.\Extracted\EventMetadata.json.zip`.
Below is the number of records in my sample metadata extract.
``... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
This notebook is a tentative overview of how we can use my custom library `neurgoo` to train ANNs.
Everything is written from scratch, directly utilizing `numpy`'s arrays and vectorizations.
`neurgoo`'s philosophy is to be as modular as possible, inspired from PyTorch's API... | github_jupyter |
# Sersic Profiles
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Setup" data-toc-modified-id="Setup-1"><span class="toc-item-num">1 </span>Setup</a></span></li><li><span><a href="#Sersic-parameter-fits" data-toc-modified-id="Sersic-parameter... | github_jupyter |
# ORF recognition by LSTM
LSTM and GRU are two variants of recurrent neural network (RNN).
LSTM was incapable of recognizing short ORFs. How about GRU?
```
import time
t = time.time()
time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t))
PC_SEQUENCES=20000 # how many protein-coding sequences
NC_SEQUENCES=20000 ... | github_jupyter |
# Furniture Rearrangement - How to setup a new interaction task in Habitat-Lab
This tutorial demonstrates how to setup a new task in Habitat that utilizes interaction capabilities in Habitat Simulator.
## Task Definition:
The working example... | github_jupyter |
<a href="https://colab.research.google.com/github/gtbook/robotics/blob/main/S36_vacuum_RL.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%pip install -q -U gtbook
import numpy as np
import gtsam
import pandas as pd
import gtbook
import gtbook.... | github_jupyter |
# Figure 3: iModulon Examples
## Setup
```
from os import path
import seaborn as sns
import matplotlib.pyplot as plt
from pymodulon.io import load_json_model
from pymodulon.plotting import *
```
### Set plotting style
```
sns.set_style('ticks')
plt.style.use('custom.mplstyle')
```
### Load data
```
figure_dir = ... | github_jupyter |
##### Copyright 2021 The TF-Agents 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 a... | github_jupyter |
# **Assignment - 2: Basic Data Understanding**
---
This assignment will get you familiarized with Python libraries and functions required for data visualization.
---
## Part 1 - Loading data
---
###Import the following libraries:
* ```numpy``` with an alias name ```np```,
* ```pandas``` with an alias name ``... | 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=TechnologyStudies/IntroductionToDataStr... | github_jupyter |
# Minimum inter-class distances of all points of different dataset in different norms in table form
```
import os
os.chdir("../")
import sys
import json
import math
import numpy as np
import pickle
from PIL import Image
from sklearn import metrics
from sklearn.metrics import pairwise_distances as dist
import matplotli... | github_jupyter |
Conditional Generative Adversarial Network
----------------------------------------
*Note: This example implements a GAN from scratch. The same model could be implemented much more easily with the `dc.models.GAN` class. See the MNIST GAN notebook for an example of using that class. It can still be useful to know ho... | github_jupyter |
<a href="https://colab.research.google.com/github/mashyko/Caffe2_Detectron2/blob/master/Caffe2_Quickload.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Tutorials Installation:
https://caffe2.ai/docs/tutorials.html
First download the tutorials sou... | github_jupyter |
# BERTを用いたテキスト分類
このノートブックでは、[BERT](https://arxiv.org/abs/1810.04805)を用いて分類器を構築します。BERTは事前学習済みのNLPのモデルであり、2018年にGoogleによって公開されました。データセットとしては、IMDBレビューデータセットを使います。
なお、学習には時間がかかるので、GPUを使うことを推奨します。
## 準備
### パッケージのインストール
```
!pip install tensorflow-text==2.6.0 tf-models-official==2.6.0
```
### インポート
```
import os
imp... | github_jupyter |
```
from PyQt4 import QtGui
import os, sys
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar... | github_jupyter |
# Demystifying Approximate Bayesian Computation
#### Brett Morris
### In this tutorial
We will write our own rejection sampling algorithm to approximate the posterior distributions for some fitting parameters.
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import anderso... | github_jupyter |
## Contoso ISD solution package
This notebook is for creating a consolidated view over the data from each of the source systems.
```
storage_account = 'steduanalytics__update_this'
use_test_env = True
if use_test_env:
stage1 = 'abfss://test-env@' + storage_account + '.dfs.core.windows.net/stage1'
stage2 = 'abf... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import TensorDataset, Dataset, DataLoader, random_split
from torch.nn.utils.rnn import pack_padded_sequence, pack_sequence, pad_packed_sequence, pad_sequence
impo... | github_jupyter |
## Analysis of UK's Tradings for 2014 Trading Year
Task:
A country's economy depends, sometimes heavily, on its exports and imports. The United Nations Comtrade database provides data on global trade. It will be used to analyse the UK's imports and exports of milk and cream in 2015:
- How much does the UK export and... | github_jupyter |
# Spark Lab
This lab will demonstrate how to perform web server log analysis with Spark. Log data is a very large, common data source and contains a rich set of information. It comes from many sources, such as web, file, and compute servers, application logs, user-generated content, and can be used for monitoring ser... | github_jupyter |
## Dependencies
```
import json, warnings, shutil
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras.models import Model
from tensorflow.keras import optimiz... | github_jupyter |
# Surface
```
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.linspace(-3,3,100)
X, Y = np.meshgrid(x, y)
Z = X**4+Y**4-16*X*Y
ax.plot_surface(X, Y, Z)
ax.set_zlim3d(0,120)
ax.set_xlabel('X Axis')
ax.set... | github_jupyter |
# Arrays, lists and tuples
In python, there are variables which can contain multiple entry of different kinds. In programming, we call them arrays; arrays of values. We already know one kind of arrays, strings. Strings are arrays of characters.
See also
* [Arrays](https://physics.nyu.edu/pine/pymanual/html/chap3/chap3... | github_jupyter |
# Regression with Amazon SageMaker XGBoost (Parquet input)
This notebook exhibits the use of a Parquet dataset for use with the SageMaker XGBoost algorithm. The example here is almost the same as [Regression with Amazon SageMaker XGBoost algorithm](xgboost_abalone.ipynb).
This notebook tackles the exact same problem ... | github_jupyter |
# BLU15 - Model CSI
## Part 1 of 2 - When to train your model
In this notebook we will be covering the following:
- 1. The need for retraining
- 1.1 Data drift
- 1.2 Robustness
- 1.3 When ground truth is not available at the time of model training
- 1.4 Concept drift
- 2. How to measure the decline in model p... | github_jupyter |
# Paper Figure Creation
- Created on a cloudly London Saturday morning, April 3rd 2021
- Revised versions of the figures
```
import climlab
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import xarray as xr
import pandas as pd
import cartopy.crs as ccrs
from... | github_jupyter |
# 2A.data - Matplotlib
Tutoriel sur [matplotlib](https://matplotlib.org/).
```
from jyquickhelper import add_notebook_menu
add_notebook_menu()
```
*Aparté*
Les librairies de visualisation en python se sont beaucoup développées ([10 plotting librairies](http://www.xavierdupre.fr/app/jupytalk/helpsphinx/2016/pydata20... | github_jupyter |
<a href="https://colab.research.google.com/github/ShreyasJothish/ai-platform/blob/master/tasks/methodology/word-embeddings/Word_Embeddings.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Word Embeddings using Word2Vec.
### Procedure
1) I shall b... | github_jupyter |
<p><font size="6"><b>05 - Pandas: "Group by" operations</b></font></p>
> *© 2016-2018, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*
---
```
%matplotlib inli... | github_jupyter |
```
import tensorflow as tf
hellow_constant = tf.constant('Hello Tensor Constant')
with tf.Session() as sess:
output = sess.run(hellow_constant)
print(output)
x = tf.placeholder(tf.string)
y = tf.placeholder(tf.float32)
z = tf.placeholder(tf.int32)
with tf.Session() as sess:
output = sess.run(x, feed_dict... | github_jupyter |
```
import os
from glob import glob
import pandas as pd
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
from matplotlib.colors import Normalize
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import gridspec
import json
import torch
import gpytorch
import h5py
import... | github_jupyter |
# Scripting `bettermoments`
In this Notebook, we will step through how to integrate the moment map making process (in this case, a zeroth moment map, or integrated intensity map), into your workflow. This should elucidate the steps that are taken automatically when using the [command line interface](https://bettermom... | github_jupyter |
## Classes for callback implementors
```
from fastai.gen_doc.nbdoc import *
from fastai.callback import *
from fastai.basics import *
```
fastai provides a powerful *callback* system, which is documented on the [`callbacks`](/callbacks.html#callbacks) page; look on that page if you're just looking for how to use exi... | github_jupyter |
What is PyTorch?
================
It’s a Python-based scientific computing package targeted at two sets of
audiences:
- A replacement for NumPy to use the power of GPUs
- a deep learning research platform that provides maximum flexibility
and speed
Getting Started
---------------
Tensors
^^^^^^^
Tensors are ... | 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"> CS109B Introduction to Data Science
## Lab 8: Recurrent Neural Networks and Introduction to Natural Language Processing
**Harvard University**<br/>
**Spring 202... | github_jupyter |
```
!wget www.di.ens.fr/~lelarge/MNIST.tar.gz
!tar -zxvf MNIST.tar.gz
import torch
import torchvision
from torchvision.datasets import MNIST
from torch.utils.data import random_split, DataLoader
import torch.nn.functional as F
from collections import namedtuple
import matplotlib.pyplot as plt
Dataset = MNIST(root="./"... | github_jupyter |
# Aim of this notebook
* To construct the singular curve of universal type to finalize the solution of the optimal control problem
# Preamble
```
from sympy import *
init_printing(use_latex='mathjax')
# Plotting
%matplotlib inline
## Make inline plots raster graphics
from IPython.display import set_matplotlib_forma... | github_jupyter |
# Construction
In this section, we construct two classes to implement a basic feed-forward neural network. For simplicity, both are limited to one hidden layer, though the number of neurons in the input, hidden, and output layers is flexible. The two differ in how they combine results across observations. The first lo... | github_jupyter |
# Shor's Algorithm for Factorization of Integers
Given a large number $N$, say with at least 100 digits, how can we find a factor of $N$? There are several famous classical algorithms, and [Wikipedia](https://en.wikipedia.org/wiki/Integer_factorization) contains an exhaustive list of these algorithms. The best known a... | github_jupyter |
<a href="https://colab.research.google.com/github/cyberboysumanjay/RcloneLab/blob/master/RcloneLab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### 📚 For more information please visit our [GitHub](https://github.com/cyberboysumanjay/RcloneLab/)... | github_jupyter |
# fastai and the New DataBlock API
> A quick glance at the new top-level api
- toc: true
- badges: true
- comments: true
- image: images/chart-preview.png
- category: DataBlock
---
This blog is also a Jupyter notebook available to run from the top down. There will be code snippets that you can then run in any envir... | 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 |
<a href="https://colab.research.google.com/github/cohmathonc/biosci670/blob/master/IntroductionComputationalMethods/exercises/07_ODEs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pylab as plt
```
## Solvi... | github_jupyter |
```
#import packages
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow_datasets as tfds
import matplotlib.pylab as plt
import os
import zipfile
from tensorflow.keras.preprocessing.image import ImageDataGenerator
local_zip = '../Dataset/horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip,... | github_jupyter |
# Python для анализа данных
## Что такое SQL. Как писать запросы. Работа с Clickhouse.
Автор: *Ян Пиле, НИУ ВШЭ*
Язык SQL очень прочно влился в жизнь аналитиков и требования к кандидатам благодаря простоте, удобству и распространенности. Часто SQL используется для формирования выгрузок, витрин (с последующим постро... | github_jupyter |
In this project, I implemented three models, which are subtractor, adder-subtractor, adder-subtractor-multiplier, based on `Addition.ipynb` provided in homework3 sameple code.
And I wrote three jupyter notebooks of these models, which named [Subtractor.ipynb](https://nbviewer.jupyter.org/github/rapirent/DSAI-HW3/blob/... | github_jupyter |
# Image segmentation with a U-Net-like architecture
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2019/03/20<br>
**Last modified:** 2020/04/20<br>
**Description:** Image segmentation model trained from scratch on the Oxford Pets dataset.
## Download the data
```
!curl -O http://www.robot... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
import numpy.random as rnd
import scipy.stats as st
import os
plt.style.use(os.path.join(os.getcwd(), 'mystyle.mplstyle') )
nvalues = 10
norm_variates = rnd.randn(nvalues)
norm_variates
for... | github_jupyter |
# Learning Curves and Bias-Variance Tradeoff
In practice, much of the task of machine learning involves selecting algorithms,
parameters, and sets of data to optimize the results of the method. All of these
things can affect the quality of the results, but it’s not always clear which is
best. For example, if your resu... | github_jupyter |
<a href="https://colab.research.google.com/github/yukinaga/minnano_kaggle/blob/main/section_2/01_pandas_basic.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Pandasの基礎
PandasはPythonでデータ分析を行うためのライブラリで、データの読み込みや編集、統計量の表示などを簡単に行うことができます。
主要なコードはCyt... | github_jupyter |
# First and Second order random walks
First and second order random walks are a node-sampling mechanism that can be employed in a large number of algorithms. In this notebook we will shortly show how to use Ensmallen to sample a large number of random walks from big graphs.
To install the GraPE library run:
```
pip i... | github_jupyter |
```
x = 1
if x == 1:
print('That is true')
x = 1
if x != 1:
print('That is true')
else:
print('x = 1')
if x == 1:
print('That is true!')
a = int(input())
b = int(input())
if a < b:
print(a)
el... | github_jupyter |
```
import sys
sys.path.insert(0, '..')
from branca.element import *
```
## Element
This is the base brick of `branca`. You can create an `Element` in providing a template string:
```
e = Element("This is fancy text")
```
Each element has an attribute `_name` and a unique `_id`. You also have a method `get_name` t... | github_jupyter |
<a href="https://practicalai.me"><img src="https://raw.githubusercontent.com/practicalAI/images/master/images/rounded_logo.png" width="100" align="left" hspace="20px" vspace="20px"></a>
<img src="https://raw.githubusercontent.com/practicalAI/images/master/basic_ml/06_Multilayer_Perceptron/nn.png" width="200" vspace="1... | github_jupyter |
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