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# 3์ฅ. ์ฌ์ดํท๋ฐ์ ํ๊ณ ๋ ๋๋ ๋จธ์ ๋ฌ๋ ๋ถ๋ฅ ๋ชจ๋ธ ํฌ์ด
**์๋ ๋งํฌ๋ฅผ ํตํด ์ด ๋
ธํธ๋ถ์ ์ฃผํผํฐ ๋
ธํธ๋ถ ๋ทฐ์ด(nbviewer.jupyter.org)๋ก ๋ณด๊ฑฐ๋ ๊ตฌ๊ธ ์ฝ๋ฉ(colab.research.google.com)์์ ์คํํ ์ ์์ต๋๋ค.**
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob... | github_jupyter |
## Importing Necessary Libraries and Functions
The first thing we need to do is import the necessary functions and libraries that we will be working with throughout the topic. We should also go ahead and upload all the of the necessary data sets here instead of loading them as we go. We will be using energy production... | github_jupyter |
```
import numpy as np
import json
import re
from collections import defaultdict
import spacy
import matplotlib.pyplot as plt
%matplotlib inline
annotation_file = '../vqa-dataset/Annotations/mscoco_%s_annotations.json'
annotation_sets = ['train2014', 'val2014']
question_file = '../vqa-dataset/Questions/OpenEnded_mscoco... | github_jupyter |
```
%matplotlib inline
```
# Spectral clustering for image segmentation
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the `spectral_clustering` approach solves the problem
know as 'normalized graph cuts': the image is seen ... | github_jupyter |
<center>
<img src="img/scikit-learn-logo.png" width="40%" />
<br />
<h1>Robust and calibrated estimators with Scikit-Learn</h1>
<br /><br />
Gilles Louppe (<a href="https://twitter.com/glouppe">@glouppe</a>)
<br /><br />
New York University
</center>
```
# Global imports and settings
# Mat... | github_jupyter |
```
import importlib
import pathlib
import os
import sys
from datetime import datetime, timedelta
import pandas as pd
module_path = os.path.abspath(os.path.join('../..'))
if module_path not in sys.path:
sys.path.append(module_path)
datetime.now()
ticker="GME"
report_name=f"{ticker}_{datetime.now().strftime('%Y%m%d_... | github_jupyter |
# Segmented deformable mirrors
We will use segmented deformable mirrors and simulate the PSFs that result from segment pistons and tilts. We will compare this functionality against Poppy, another optical propagation package.
First we'll import all packages.
```
import os
import numpy as np
import matplotlib.pyplot a... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv('austin_weather.csv')
df.head()
df.info()
```
<h2>Visualisasi Scatter Plot Perbandingan Kuantitatif</h2>
Pada tugas kali ini kita akan mengamati nilai DewPointAvg (F) dengan mengamati nilai HumidityAvg (%), TempAvg (F), dan ... | github_jupyter |
# Convolutional Neural Networks: Step by Step
Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.
**Notation**:
- Superscript $[l]$ denotes an object of the $l... | github_jupyter |
# Mapboxgl Python Library for location data visualizaiton
https://github.com/mapbox/mapboxgl-jupyter
### Requirements
These examples require the installation of the following python modules
```
pip install mapboxgl
pip install pandas
```
```
import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.... | github_jupyter |
# ARC Tools
## Coordinates conversions
Below, `xyz` and `zmat` refer to Cartesian and internal coordinates, respectively
```
from arc.species.converter import (zmat_to_xyz,
xyz_to_str,
zmat_from_xyz,
zmat_to_str,
... | github_jupyter |
# Course Outline
* Step 0: ่ผๅ
ฅๅฅไปถไธฆไธ่ผ่ชๆ
* Step 1: ๅฐ่ชๆ่ฎ้ฒไพ
* Step 2: Contingency table ๅ keyness ่จ็ฎๅ
ฌๅผ
* Step 3: ่จ็ฎ่ฉ้ ป
* Step 4: ่จ็ฎ keyness
* Step 5: ๆพๅบ PTT ๅ
ฉๆฟ็ keywords
* Step 6: ่ฆ่ฆบๅ
# Step 0: ่ผๅ
ฅๅฅไปถไธฆไธ่ผ่ชๆ
```
import re # ๅพ
ๆๆไฝฟ็จ regular expression
import math # ็จไพ่จ็ฎ log
import pandas as pd # ็จไพ่ฃฝไฝ่กจๆ ผ
import matplot... | github_jupyter |
The most common analytical task is to take a bunch of numbers in dataset and summarise it with fewer numbers, preferably a single number. Enter the 'average', sum all the numbers and divide by the count of the numbers. In mathematical terms this is known as the 'arithmetic mean', and doesn't always summarise a dataset ... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import model
from datetime import datetime
from datetime import timedelta
sns.set()
df = pd.read_csv('/home/husein/space/Stock-Prediction-Comparison/dat... | github_jupyter |
```
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Point
from sklearn.neighbors import KNeighborsRegressor
import rasterio as rst
from rasterstats import zonal_stats
%matplotlib inline
path = r"[CHANGE THIS PATH]\Wales\\"
data = pd.read_csv(p... | github_jupyter |
```
import json
import os
import tqdm
import pandas as pd
```
## I. convert emails text (both training and testing) into appropriate jsonl file format
### 6088 entries in training set ( 2000+ machine generated, the rest are human-written)
#### 4000+ are from email corpus, 2000+ are from gtp-2 generated and the ENRON ... | github_jupyter |
# Overview
This Jupyter Notebook takes in data from a Google Sheet that contains line change details and their associated high level categories and outputs a JSON file for the MyBus tool.
The output file is used by the MyBus tool's results page and contains the Line-level changes that are displayed there.
Run all ce... | github_jupyter |
# Region Based Data Analysis
The following notebook will go through prediction analysis for region based Multiple Particle Tracking (MPT) using OGD severity datasets for non-treated (NT) hippocampus, ganglia, thalamus, cortex, and striatum.
## Table of Contents
[1. Load Data](#1.-load-data)<br />
[2. Analys... | github_jupyter |
# Lab 3: Tables
Welcome to lab 3! This week, we'll learn about *tables*, which let us work with multiple arrays of data about the same things. Tables are described in [Chapter 6](https://www.inferentialthinking.com/chapters/06/Tables) of the text.
First, set up the tests and imports by running the cell below.
```
... | github_jupyter |
```
%matplotlib inline
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import scipy as sp
import seaborn as sns
sns.set(context='notebook', font_scale=1.2, rc={'figure.figsize': (12, 5)})
plt.style.use(['seaborn-colorblind', 'seaborn-darkgrid'])
RANDOM_SEED... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
%matplotlib notebook
import pandas as pd
import numpy as np
from util import *
from sklearn.model_selection import train_test_split
from sklearn import metrics
from skater.core.global_interpretation.interpretable_models.brlc import BRLC
from skater.core.global_inte... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
# ResNet-101 on CIFAR-10
### Imports
```
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
```
#... | github_jupyter |
```
import sys
sys.path.append("..") # Adds higher directory to python modules path.
from pathlib import Path
import glob
import numpy as np
import tensorflow as tf
import pickle
import matplotlib.pyplot as plt
import random
import pickle
import os
import config
import data
import random
from natsort import natsorted
i... | github_jupyter |
# Optimization of CNN - TPE
In this notebook, we will optimize the hyperparameters of a CNN using the define-by-run model from Optuna.
```
# For reproducible results.
# See:
# https://keras.io/getting_started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development
import os
os.environ['PYTHONHASHS... | github_jupyter |
# QCoDeS Example with Tektronix Keithley 7510 Multimeter
In this example we will show how to use a few basic functions of the Keithley 7510 DMM. We attached the 1k Ohm resistor to the front terminals, with no source current or voltage.
For more detail about the 7510 DMM, please see the User's Manual: https://www.tek.... | github_jupyter |
# Applying Customizations
```
import pandas as pd
import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('bokeh', 'matplotlib')
```
As introduced in the [Customization](../getting_started/2-Customization.ipynb) section of the 'Getting Started' guide, HoloViews maintains a strict separation ... | github_jupyter |
# Reading outputs from E+
```
# some initial set up
# if you have not installed epp, and only downloaded it
# you will need the following lines
import sys
# pathnameto_eppy = 'c:/eppy'
pathnameto_eppy = '../'
sys.path.append(pathnameto_eppy)
```
## Using titletable() to get at the tables
So far we have been making c... | github_jupyter |
# Applying the Expected Context Framework to the Switchboard Corpus
### Using `DualContextWrapper`
This notebook demonstrates how our implementation of the Expected Context Framework can be applied to the Switchboard dataset. See [this dissertation](https://tisjune.github.io/research/dissertation) for more details ab... | github_jupyter |
```
#python packages pd
import numpy as np
import matplotlib.pyplot as plt
#machine learning packages
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D, Bidirectional, Dropout
from keras.layers import CuDNNLSTM
from keras.utils.... | github_jupyter |
# Underfitting and Overfitting demo using KNN
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv('data_knn_classification_cleaned_titanic.csv')
data.head()
x = data.drop(['Survived'], axis=1)
y = data['Survived']
#Scaling the data
from sklearn.preprocessin... | github_jupyter |
# [ATM 623: Climate Modeling](../index.ipynb)
[Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany
# Lecture 17: Ice albedo feedback in the EBM
### About these notes:
This document uses the interactive [`IPython notebook`](http://ipython.org/notebook.html) format (now also c... | github_jupyter |
# Neural networks with PyTorch
Next I'll show you how to build a neural network with PyTorch.
```
# Import things like usual
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
... | github_jupyter |
# Example: CanvasXpress boxplot Chart No. 11
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/boxplot-11.html
This example is generated using the reproducible JSON obtained from the above pag... | github_jupyter |
# Partial Dependence Plot
## Summary
Partial dependence plots visualize the dependence between the response and a set of target features (usually one or two), marginalizing over all the other features. For a perturbation-based interpretability method, it is relatively quick. PDP assumes independence between the featu... | github_jupyter |
# A - Using TorchText with Your Own Datasets
In this series we have used the IMDb dataset included as a dataset in TorchText. TorchText has many canonical datasets included for classification, language modelling, sequence tagging, etc. However, frequently you'll be wanting to use your own datasets. Luckily, TorchText ... | github_jupyter |
## Data Analysis
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(1)
# load data
df = pd.read_csv('../input_data/heartdisease_data.csv',sep= ',')
df[0:10]
```
The data contains 13 features:<br/>
0) age: Age (years) --> discrete <br/>
1... | github_jupyter |
# Single Qubit Gates
In the previous section we looked at all the possible states a qubit could be in. We saw that qubits could be represented by 2D vectors, and that their states are limited to the form:
$$ |q\rangle = \cos{(\tfrac{\theta}{2})}|0\rangle + e^{i\phi}\sin{\tfrac{\theta}{2}}|1\rangle $$
Where $\theta$ ... | github_jupyter |
```
from __future__ import absolute_import
import sys
import os
try:
from dotenv import find_dotenv, load_dotenv
except:
pass
import argparse
try:
sys.path.append(os.path.join(os.path.dirname(__file__), '../src'))
except:
sys.path.append(os.path.join(os.getcwd(), '../src'))
import pandas as pd
... | github_jupyter |
# Amazon SageMaker Experiment Trials for Distirbuted Training of Mask-RCNN
This notebook is a step-by-step tutorial on Amazon SageMaker Experiment Trials for distributed tranining of [Mask R-CNN](https://arxiv.org/abs/1703.06870) implemented in [TensorFlow](https://www.tensorflow.org/) framework.
Concretely, we will... | github_jupyter |
# Training on Multiple GPUs
:label:`sec_multi_gpu`
So far we discussed how to train models efficiently on CPUs and GPUs. We even showed how deep learning frameworks allow one to parallelize computation and communication automatically between them in :numref:`sec_auto_para`. We also showed in :numref:`sec_use_gpu` how ... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | 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 |
# Introduction to the Quantum Bit
### Where we'll explore:
* **Quantum Superposition**
* **Quantum Entanglement**
* **Running experiments on a laptop-hosted simulator**
* **Running experiments on a real quantum computer**
### Brandon Warren
### SDE, Zonar Systems
github.com/brandonwarren/intro-to-qubit contains this J... | github_jupyter |
1. Split into train and test data
2. Train model on train data normally
3. Take test data and duplicate into test prime
4. Drop first visit from test prime data
5. Get predicted delta from test prime data. Compare to delta from test data. We know the difference (epsilon) because we dropped actual visits. What percent ... | github_jupyter |
```
import numpy as np
import pandas as pd
import xarray as xr
import zarr
import math
import glob
import pickle
import statistics
import scipy.stats as stats
from sklearn.neighbors import KernelDensity
import dask
import seaborn as sns
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
def get_files():
mod... | github_jupyter |
<a href="https://colab.research.google.com/github/gordicaleksa/get-started-with-JAX/blob/main/Tutorial_3_JAX_Neural_Network_from_Scratch_Colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# MLP training on MNIST
```
import numpy as np
import jax... | github_jupyter |
# Plots
One of the most amazing feature of hist is it's powerful plotting family. Here you can see how to plot Hist.
```
from hist import Hist
import hist
h = Hist(
hist.axis.Regular(50, -5, 5, name="S", label="s [units]", flow=False),
hist.axis.Regular(50, -5, 5, name="W", label="w [units]", flow=False),
)
i... | github_jupyter |
# Hands-on Federated Learning: Image Classification
In their recent (and exteremly thorough!) review of the federated learning literature [*Kairouz, et al (2019)*](https://arxiv.org/pdf/1912.04977.pdf) define federated learning as a machine learning setting where multiple entities (clients) collaborate in solving a ma... | github_jupyter |
```
import pandas as pd
from unidecode import unidecode
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
nltk.download('stopwords')
df = pd.read_csv('../base/review.csv',encoding='latin-1')
df.head()
im... | github_jupyter |
## ๆๅฐไบไนๆณ
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import leastsq
Xi = np.array(
[157, 162, 169, 176, 188, 200, 211, 220, 230, 237, 247, 256, 268, 287, 285, 290, 301, 311, 326, 335, 337, 345, 348,
358, 384, 396, 409, 415, 432, 440, 448, 449, 461, 467, 478, 493], dtype=np.floa... | 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 |
```
import warnings
warnings.filterwarnings('ignore') # ๅฎ่กใซๅฝฑ้ฟใฎใชใใwarninigใใ้่กจ็คบใซใใพใ. ้ๆจๅฅจ.
```
# Chapter 5: ๆฉๆขฐๅญฆ็ฟ ๅๅธฐๅ้ก
## 5-1. ๅๅธฐๅ้กใ Pythonใง่งฃใใฆใฟใใ
1. ใใผใฟใปใใใฎ็จๆ
2. ใขใใซๆง็ฏ
### 5-1-1. ใใผใฟใปใใใฎ็จๆ
ไปๅใฏwine-quality datasetใ็จใใ.
wine-quality dataset ใฏใฏใคใณใฎใขใซใณใผใซๆฟๅบฆใๅ่ณชใชใฉใฎ12่ฆ็ด ใฎๆฐๅคใใผใฟ.
่ตคใฏใคใณใจ็ฝใฏใคใณไธกๆนใใใพใใ่ตคใฏใคใณใฎๅซใพใใใใผใฟๆฐใฏ1600ใปใฉ.
ใพใใฏใ... | github_jupyter |
## Conditional Probability
- Conditional probability has many applications, we learn it by mentioning its application in text analysis
- Assume this small dataset is given:
<img src="spam_ham_data_set.png" width="600" height="600">
## Question: What is the probability that an email be spam? What is the probability ... | github_jupyter |
# Chapter 12 - Principal Components Analysis with scikit-learn
This notebook contains code accompanying Chapter 12 Principal Components Analysis with scikit-learn in *Practical Discrete Mathematics* by Ryan T. White and Archana Tikayat Ray.
## Eigenvalues and eigenvectors, orthogonal bases
### Example: Pizza nutriti... | 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 |
```
import numpy as np
import torch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
device
import torchvision
from torchvision import models
from torchvision import transforms
import os
import glob
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import... | github_jupyter |
# Global Imports
```
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.pyplot import subplots
```
### External Package Imports
```
import os as os
import pickle as pickle
import pandas as pd
```
### Module Imports
Here I am using a few of my own packages, they are availible on Github under [__thea... | github_jupyter |
# Advanced usage
This notebook shows some more advanced features of `skorch`. More examples will be added with time.
<table align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb">
<img src="https://www.tensorflow.org/images... | github_jupyter |
<a href="https://colab.research.google.com/github/lakshit2808/Machine-Learning-Notes/blob/master/ML_Models/Classification/KNearestNeighbor/KNN_first_try.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# K-Nearest Neighbor
**K-Nearest Neighbors** is ... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import scqubits as scq
import scqubits.legacy.sweep_plotting as splot
from scqubits import HilbertSpace, InteractionTerm, ParameterSweep
import numpy as np
```
.. note::
This describes a legacy version of the `HilbertSpace` class which is depreca... | github_jupyter |
# RDD basics
This notebook will introduce **three basic but essential Spark operations**. Two of them are the transformations map and filter. The other is the action collect. At the same time we will introduce the concept of persistence in Spark.
## Getting the data and creating the RDD
We will use the reduced datas... | github_jupyter |
#### Copyright IBM All Rights Reserved.
#### SPDX-License-Identifier: Apache-2.0
# Db2 Sample For Scikit-Learn
In this code sample, we will show how to use the Db2 Python driver to import data from our Db2 database. Then, we will use that data to create a machine learning model with scikit-learn.
Many wine connoisse... | github_jupyter |
# Taylor problem 3.23
last revised: 04-Jan-2020 by Dick Furnstahl [furnstahl.1@osu.edu]
**This notebook is almost ready to go, except that the initial conditions and $\Delta v$ are different from the problem statement and there is no statement to print the figure. Fix these and you're done!**
This is a conservatio... | github_jupyter |
```
import os
from pprint import pprint
import torch
import torch.nn as nn
from transformers import BertForTokenClassification, BertTokenizer
from transformers import AdamW
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from sklearn.model_selection import train_test_split
impo... | github_jupyter |
# Flux.pl
The `Flux.pl` Perl script takes four input parameters:
`Flux.pl [input file] [output file] [bin width (s)] [geometry base directory]`
or, as invoked from the command line,
`$ perl ./perl/Flux.pl [input file] [output file] [bin width (s)] [geometry directory]`
## Input Parameters
* `[input file]`
`Flux.... | github_jupyter |
# Character-level recurrent sequence-to-sequence model
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2017/09/29<br>
**Last modified:** 2020/04/26<br>
**Description:** Character-level recurrent sequence-to-sequence model.
## Introduction
This example demonstrates how to implement a basic ... | github_jupyter |
**Note**: There are multiple ways to solve these problems in SQL. Your solution may be quite different from mine and still be correct.
**1**. Connect to the SQLite3 database at `data/faculty.db` in the `notebooks` folder using the `sqlite` package or `ipython-sql` magic functions. Inspect the `sql` creation statement ... | github_jupyter |
# Frequent opiate prescriber
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import preprocessors as pp
sns.set(style="darkgrid")
data = pd.read_csv('../data/prescriber-info.csv')
data.head()
```
## Variable Separation
```
uniq_cols = ['NPI']
cat_cols = list(data.... | github_jupyter |
# The Shared Library with GCC
When your program is linked against a shared library, only a small table is created in the executable. Before the executable starts running, **the operating system loads the machine code needed for the external functions** - a process known as **dynamic linking.**
* Dynamic linkin... | github_jupyter |
# Searching the UniProt database and saving fastas:
This notebook is really just to demonstrate how Andrew finds the sequences for the datasets. <br>
If you do call it from within our github repository, you'll probably want to add the fastas to the `.gitignore` file.
```
# Import bioservices module, to run remote U... | github_jupyter |
# Introduction
In this post,we will talk about some of the most important papers that have been published over the last 5 years and discuss why theyโre so important.We will go through different CNN Architectures (LeNet to DenseNet) showcasing the advancements in general network architecture that made these architectu... | github_jupyter |
## Probalistic Confirmed COVID19 Cases- Denmark
**Jorge: remember to reexecute the cell with the photo.**
### Table of contents
[Initialization](#Initialization)
[Data Importing and Processing](#Data-Importing-and-Processing)
1. [Kalman Filter Modeling: Case of Denmark Data](#1.-Kalman-Filter-Modeling:-Case-of-Denm... | github_jupyter |
## Dependencies
```
import json, warnings, shutil, glob
from jigsaw_utility_scripts import *
from scripts_step_lr_schedulers import *
from transformers import TFXLMRobertaModel, XLMRobertaConfig
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses, layers
SEED = 0
seed_ev... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib as plt
from shapely.geometry import Point, Polygon
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
import zipfile
import requests
import os
import shutil
... | github_jupyter |
# Advanced RNNs
<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png" width=150>
In this notebook we're going to cover some advanced topics related to RNNs.
1. Conditioned hidden state
2. Char-level embeddings
3. Encoder and decoder
4. Attentional mechanisms
5. Implementation
... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 5, Tutoria... | github_jupyter |
**Instructions:**
1. **For all questions after 10th, Please only use the data specified in the note given just below the question**
2. **You need to add answers in the same file i.e. PDS_UberDriveProject_Questions.ipynb' and rename that file as 'Name_Date.ipynb'.You can mention the date on which you will be uploading... | github_jupyter |
# Robust Scaler - Experimento
Este รฉ um componante que dimensiona atributos usando estatรญsticas robustas para outliers. Este Scaler remove a mediana e dimensiona os dados de acordo com o intervalo quantil (o padrรฃo รฉ Amplitude interquartil). Amplitude interquartil รฉ o intervalo entre o 1ยบ quartil (25ยบ quantil) e o 3ยบ ... | github_jupyter |
<a href="https://colab.research.google.com/github/ralsouza/python_fundamentos/blob/master/src/05_desafio/05_missao05.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## **Missรฃo: Analisar o Comportamento de Compra de Consumidores.**
### Nรญvel de Difi... | github_jupyter |
# Criminology in Portugal (2011)
## Introduction
> In this _study case_, it will be analysed the **_crimes occurred_** in **_Portugal_**, during the civil year of **_2011_**. It will analysed all the _categories_ or _natures_ of this **_crimes_**, _building some statistics and making some filtering of data related to... | github_jupyter |
##### Copyright 2021 The Cirq Developers
```
#@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 agre... | github_jupyter |
<a href="https://colab.research.google.com/github/Shubham0Rajput/Feature-Detection-with-AKAZE/blob/master/AKAZE_code.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#IMPORT FILES
import matplotlib.pyplot as plt
import cv2
#matplotlib inline
#MO... | github_jupyter |
# Entities Recognition
<div class="alert alert-info">
This tutorial is available as an IPython notebook at [Malaya/example/entities](https://github.com/huseinzol05/Malaya/tree/master/example/entities).
</div>
<div class="alert alert-warning">
This module only trained on standard language structure, so it is no... | github_jupyter |
# Shallow regression for vector data
This script reads zip code data produced by **vectorDataPreparations** and creates different machine learning models for
predicting the average zip code income from population and spatial variables.
It assesses the model accuracy with a test dataset but also predicts the number to... | github_jupyter |
# RNN Sentiment Classifier
In this notebook, we use an RNN to classify IMDB movie reviews by their sentiment.
[](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/rnn_sentiment_classifier... | github_jupyter |
# Intro to Jupyter Notebooks
### `Jupyter` is a project for developing open-source software
### `Jupyter Notebooks` is a `web` application to create scripts
### `Jupyter Lab` is the new generation of web user interface for Jypyter
### But it is more than that
#### It lets you insert and save text, equations & visuali... | github_jupyter |
# Introduction to geospatial vector data in Python
```
%matplotlib inline
import pandas as pd
import geopandas
pd.options.display.max_rows = 10
```
## Importing geospatial data
Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, P... | github_jupyter |
## Computer Vision Learner
[`vision.learner`](/vision.learner.html#vision.learner) is the module that defines the [`cnn_learner`](/vision.learner.html#cnn_learner) method, to easily get a model suitable for transfer learning.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
```
## Transfer learning... | github_jupyter |
# Trax : Ungraded Lecture Notebook
In this notebook you'll get to know about the Trax framework and learn about some of its basic building blocks.
## Background
### Why Trax and not TensorFlow or PyTorch?
TensorFlow and PyTorch are both extensive frameworks that can do almost anything in deep learning. They offer a... | github_jupyter |
<h1>Data Exploration</h1>
<p>In this notebook we will perform a broad data exploration on the <code>Hitters</code> data set. Note that the aim of this exploration is not to be completely thorough; instead we would like to gain quick insights to help develop a first prototype. Upon analyzing the output of the prototype,... | github_jupyter |
<a href="https://colab.research.google.com/github/choderalab/pinot/blob/master/scripts/adlala_mol_graph.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# import
```
! rm -rf pinot
! git clone https://github.com/choderalab/pinot.git
! pip install dg... | github_jupyter |
# Data Loading Tutorial
```
cd ../..
save_path = 'data/'
from scvi.dataset import LoomDataset, CsvDataset, Dataset10X, AnnDataset
import urllib.request
import os
from scvi.dataset import BrainLargeDataset, CortexDataset, PbmcDataset, RetinaDataset, HematoDataset, CbmcDataset, BrainSmallDataset, SmfishDataset
```
## G... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Indexed Expressions: Representing and manipulating tensor... | github_jupyter |
```
%matplotlib inline
# Importing standard Qiskit libraries and configuring account
from qiskit import QuantumCircuit, execute, Aer, IBMQ
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.visualization import *
# Loading your IBM Q account(s)
provider = IBMQ.load_account()
... | github_jupyter |
```
import wget, json, os, math
from pathlib import Path
from string import capwords
from pybtex.database import parse_string
import pybtex.errors
from mpcontribs.client import Client
from bravado.exception import HTTPNotFound
from pymatgen.core import Structure
from pymatgen.ext.matproj import MPRester
from tqdm.noteb... | github_jupyter |
# DeepDreaming with TensorFlow
>[Loading and displaying the model graph](#loading)
>[Naive feature visualization](#naive)
>[Multiscale image generation](#multiscale)
>[Laplacian Pyramid Gradient Normalization](#laplacian)
>[Playing with feature visualzations](#playing)
>[DeepDream](#deepdream)
This notebook demo... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=3
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
```
import sys
sys.path.append('../scripts/')
from robot import *
from scipy.stats import multivariate_normal
import random #่ฟฝๅ
import copy
class Particle:
def __init__(self, init_pose, weight):
self.pose = init_pose
self.weight = weight
def motion_update(self, nu, omega, time, noise_... | github_jupyter |
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