code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
import torch
from torch.autograd import grad
import torch.nn as nn
from numpy import genfromtxt
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.nn.functional as F
import math
tuberculosis_data = genfromtxt('tuberculosis.csv', delimiter=',') #in the form of [t, S,L,I,T]
torch.manual_seed(1... | github_jupyter |
```
#IMPORT SEMUA LIBARARY
#IMPORT LIBRARY PANDAS
import pandas as pd
#IMPORT LIBRARY UNTUK POSTGRE
from sqlalchemy import create_engine
import psycopg2
#IMPORT LIBRARY CHART
from matplotlib import pyplot as plt
from matplotlib import style
#IMPORT LIBRARY BASE PATH
import os
import io
#IMPORT LIBARARY PDF
from fpdf im... | github_jupyter |
```
import pytest
from scipy.stats import zscore
from mne.preprocessing import create_ecg_epochs
from sklearn.model_selection import train_test_split
%run parameters.py
%run Utility_Functions.ipynb
%matplotlib qt5
data = np.load('All_Subject_IR_Index_'+str(epoch_length)+'.npy')
print(data.shape)
sb.set()
def ir_plot(da... | github_jupyter |
# Produit matriciel avec une matrice creuse
Les dictionnaires sont une façon assez de représenter les matrices creuses en ne conservant que les coefficients non nuls. Comment écrire alors le produit matriciel ?
```
from jyquickhelper import add_notebook_menu
add_notebook_menu()
```
## Matrice creuse et dictionnaire
... | github_jupyter |
<a href="https://colab.research.google.com/github/mohd-faizy/03_TensorFlow_In-Practice/blob/master/03_callbacks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# __Callbacks API__
A __callback__ is an object that can perform actions at various stag... | github_jupyter |
```
from PIL import Image
import numpy as np
import os
import cv2
import keras
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
import pandas as pd
import sys
import tensorflow as tf
%matplotlib inline
import matplotlib.pyplot as plt... | github_jupyter |
# Loan predictions
## Problem Statement
We want to automate the loan eligibility process based on customer details that are provided as online application forms are being filled. You can find the dataset [here](https://drive.google.com/file/d/1h_jl9xqqqHflI5PsuiQd_soNYxzFfjKw/view?usp=sharing). These details concern ... | github_jupyter |
# Looking at the randomness (or otherwise) of mouse behaviour
### Also, the randomness (or otherwise) of trial types to know when best to start looking at 'full task' behaviour
```
# Import libraries
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
import random
import copy... | github_jupyter |
# ClarityViz
## Pipeline: .img -> histogram .nii -> graph represented as csv -> graph as graphml -> plotly
### To run:
### Step 1:
First, run the following. This takes the .img, generates the localeq histogram as an nii file, gets the nodes and edges as a csv and converts the csv into a graphml
```
python runclar... | github_jupyter |
```
{
"nodes": [
{
"op": "null",
"name": "data",
"inputs": []
},
{
"op": "null",
"name": "mobilenet0_conv0_weight",
"attrs": {
"__dtype__": "0",
"__lr_mult__": "1.0",
"__shape__": "(8L, 3L, 3L, 3L)",
"__storage_type__": "0",
... | github_jupyter |
# Mixture Density Networks with Edward, Keras and TensorFlow
This notebook explains how to implement Mixture Density Networks (MDN) with Edward, Keras and TensorFlow.
Keep in mind that if you want to use Keras and TensorFlow, like we do in this notebook, you need to set the backend of Keras to TensorFlow, [here](http:... | github_jupyter |
<H3>Importing Required Libraries
```
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
```
<H3>Getting Spark Session
```
spark = SparkSession.builder.getOrCreate()
```
<H3>Reading CSV
```
df = spark.read.csv("Big_Cities_Health_Data_Inventory.csv", header=True)
df.show(10)
```
<H3>Printin... | github_jupyter |
# Exploratory Data Analysis
* Dataset taken from https://github.com/Tariq60/LIAR-PLUS
## 1. Import Libraries
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
TRAIN_PATH = "../data/raw/dataset/tsv/train2.tsv"
VAL_PATH = "../data/raw/dataset/tsv/val2.tsv"
TEST_PATH = "../data/raw/d... | github_jupyter |
```
#hide
#skip
! [[ -e /content ]] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp collab
#default_class_lvl 3
#export
from fastai.tabular.all import *
#hide
from nbdev.showdoc import *
```
# Collaborative filtering
> Tools to quickly get the data and train models suitable for collaborative filter... | github_jupyter |
# ***Introduction to Radar Using Python and MATLAB***
## Andy Harrison - Copyright (C) 2019 Artech House
<br/>
# Pulse Train Ambiguity Function
***
Referring to Section 8.6.1, the amibguity function for a coherent pulse train is found by employing the generic waveform technique outlined in Section 8.6.3.
***
Begin b... | github_jupyter |
# 5.2 Fourier transform and Fourier series
We make use of the theory of tempered distributions (see
[@strichartz2003guide] for an introduction) and we begin by collecting
some results of independent interest, which will also be important
later.
## 5.2.1 Fourier transform
Before studying the Fourier transform, we fir... | github_jupyter |
```
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense
#df = pd.read_csv(".\\Data_USD.csv", header=None,skiprows=1)
df = pd.... | github_jupyter |
# Nonlinear recharge models
*R.A. Collenteur, University of Graz*
This notebook explains the use of the `RechargeModel` stress model to simulate the combined effect of precipitation and potential evaporation on the groundwater levels. For the computation of the groundwater recharge, three recharge models are currently... | github_jupyter |
# Place Stock Trades into Senator Dataframe
## 1. Understand the Senator Trading Report (STR) Dataframe
```
import pandas as pd
#https://docs.google.com/spreadsheets/d/1lH_LpTgRlfzKvpRnWYgoxlkWvJj0v1r3zN3CeWMAgqI/edit?usp=sharing
try:
sen_df = pd.read_csv("Senator Stock Trades/Senate Stock Watcher 04_16_2020 All ... | github_jupyter |
# Collaborative filtering on Google Analytics data
This notebook demonstrates how to implement a WALS matrix refactorization approach to do collaborative filtering.
```
import os
PROJECT = "qwiklabs-gcp-00-34ffb0f0dc65" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
... | 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');
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# Computing the 4-Velocity Time-Component $u^0$, the Magnet... | github_jupyter |
```
# import re
# import tensorflow as tf
# from tensorflow.keras.preprocessing.text import text_to_word_sequence
# tokens=text_to_word_sequence("manta.com/c/mmcdqky/lily-co")
# print(tokens)
# #to map the features to a dictioanary and then convert it to a csv file.
# # Feauture extraction
# class feature_extracto... | github_jupyter |
```
from sklearn.preprocessing import LabelBinarizer
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, model_from_json
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.constraints import maxnorm
from ... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_04_1_feature_encode.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 4: Training for... | github_jupyter |
**This notebook is an exercise in the [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/categorical-variables).**
---
By encoding **categorical variables**, you'll obtain your best resul... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | github_jupyter |
<a href="https://colab.research.google.com/github/cseveriano/spatio-temporal-forecasting/blob/master/notebooks/thesis_experiments/20200924_eMVFTS_Wind_Energy_Raw.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Forecasting experiments for GEFCOM 2... | github_jupyter |
# Use `Lale` `AIF360` scorers to calculate and mitigate bias for credit risk AutoAI model
This notebook contains the steps and code to demonstrate support of AutoAI experiments in Watson Machine Learning service. It introduces commands for bias detecting and mitigation performed with `lale.lib.aif360` module.
Some fa... | github_jupyter |
# Trade-off between classification accuracy and reconstruction error during dimensionality reduction
- Low-dimensional LSTM representations are excellent at dimensionality reduction, but are poor at reconstructing the original data
- On the other hand, PCs are excellent at reconstructing the original data but these hi... | github_jupyter |
```
imatlab_export_fig('print-png')
```
# Quadrature rules for 2.5-D resistivity modelling
We consider the evaluation of the integral
$$
\Phi(x, y, z) = \frac{2}{\pi} \int_0^\infty \tilde\Phi(k, y, z) \cos(k x)\, dk
$$
where
$$
\tilde\Phi(k, y, z) = K_0\left({k}{\sqrt{y^2 + z^2}}\right).
$$
The function $\tilde\Ph... | github_jupyter |
# Introduction to Gym toolkit
## Gym Environments
The centerpiece of Gym is the environment, which defines the "game" in which your reinforcement algorithm will compete. An environment does not need to be a game; however, it describes the following game-like features:
* **action space**: What actions can we take on ... | github_jupyter |
| Name | Description | Date
| :- |-------------: | :-:
|<font color=red>__Reza Hashemi__</font>| __Function approximation by linear model and deep network LOOP test__. | __On 10th of August 2019__
# Function approximation with linear models and neural network
* Are Linear models sufficient for approximating transcede... | github_jupyter |
# Controlling Flow with Conditional Statements
Now that you've learned how to create conditional statements, let's learn how to use them to control the flow of our programs. This is done with `if`, `elif`, and `else` statements.
## The `if` Statement
What if we wanted to check if a number was divisible by 2 and if... | github_jupyter |
# Laboratorio 8
```
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_confusion_matrix
%matplotlib inline
digits_X, digits_y = datasets.load_digits(r... | github_jupyter |
# **Spit some [tensor] flow**
We need to learn the intricacies of tensorflow to master deep learning
`Let's get this over with`
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
print(tf.__version__)
def evaluation_tf(report, y_test, y_pred, classes):
plt... | github_jupyter |
# Google Apps Workspace
## Imports
```
%matplotlib inline
import os
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
```
## Load Dataset
```
apps_df = pd.read_csv('googleplaystore.csv', index_col = 0)
reviews_df = pd.read_csv('googleplaystore_user_reviews.csv', index... | github_jupyter |
# Geolocalizacion de dataset de escuelas argentinas
```
#Importar librerias
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
```
### Preparacion de data
```
# Vamos a cargar un padron de escuelas de Argentina
# Estos son los nombres de columna... | github_jupyter |
# Combining DataFrames with pandas
In many "real world" situations, the data that we want to use come in multiple
files. We often need to combine these files into a single DataFrame to analyze
the data. The pandas package provides [various methods for combining
DataFrames](http://pandas.pydata.org/pandas-docs/stable/m... | github_jupyter |
# Chapter 13: Analyzing sound waves with Fourier Series
Helper functions
```
import matplotlib.pyplot as plt
def plot_function(f,xmin,xmax,**kwargs):
ts = np.linspace(xmin,xmax,1000)
plt.plot(ts,[f(t) for t in ts],**kwargs)
def plot_sequence(points,max=100,line=False,**kwargs):
if line:
plt.p... | github_jupyter |
# info
##### クレンジング
1. 欠損値があった場合、基礎分析の結果に基づいて値埋めか行の削除を行なっている
1. 表記揺れがあった場合、漏れなく修正している
1. 水準数が多く、なおかつまとめられそうな質的変数があった場合に、論理的な基準に基づいて値をまとめている
##### 特徴量エンジニアリング
1. 質的変数を量的変数(加減乗除して意味のある数値)に変換している
1. 量的変数を基礎分析の結果をもとに変換している
1. 量的変数のスケーリングを行っている
1. 元データを素に、有用であると考えられるような特徴を少なくとも1は生成している
# init
```
import numpy as np
impo... | github_jupyter |
# FloPy shapefile export demo
The goal of this notebook is to demonstrate ways to export model information to shapefiles.
This example will cover:
* basic exporting of information for a model, individual package, or dataset
* custom exporting of combined data from different packages
* general exporting and importing of... | github_jupyter |
# BERT finetuning on AG_news-4
## Librairy
```
# !pip install transformers==4.8.2
# !pip install datasets==1.7.0
import os
import time
import pickle
import numpy as np
import torch
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_scor... | github_jupyter |
This tutorial shows how to generate an image of handwritten digits using Deep Convolutional Generative Adversarial Network (DCGAN).
Generative Adversarial Networks (GANs) are one of the most interesting fields in machine learning. The standard GAN consists of two models, a generative and a discriminator one. Two model... | github_jupyter |
## Setup
If you are running this generator locally(i.e. in a jupyter notebook in conda, just make sure you installed:
- RDKit
- DeepChem 2.5.0 & above
- Tensorflow 2.4.0 & above
Then, please skip the following part and continue from `Data Preparations`.
To increase efficiency, we recommend running this molecule gene... | github_jupyter |
```
import pandas as pd
import numpy as np
import glob
result_file = '/tmp/fuzzydatatest/20220209-150332_perf.csv'
perf_df = pd.read_csv(result_file, index_col=0)
perf_df
import numpy as np
perf_df['end_time_seconds'] = np.cumsum(perf_df.elapsed_time)
perf_df['start_time_seconds'] = end_time.shift().fillna(0)
perf_df
... | github_jupyter |
# DataMining TwitterAPI
Requirements:
- TwitterAccount
- TwitterApp credentials
## Imports
The following imports are requiered to mine data from Twitter
```
# http://tweepy.readthedocs.io/en/v3.5.0/index.html
import tweepy
# https://api.mongodb.com/python/current/
import pymongo
import json
import sys
```
## Access... | github_jupyter |
# Graphs from the presentation
```
import matplotlib.pyplot as plt
%matplotlib notebook
# create a new figure
plt.figure()
# create x and y coordinates via lists
x = [99, 19, 88, 12, 95, 47, 81, 64, 83, 76]
y = [43, 18, 11, 4, 78, 47, 77, 70, 21, 24]
# scatter the points onto the figure
plt.scatter(x, y)
# create a... | github_jupyter |
<a href="https://colab.research.google.com/github/VxctxrTL/daa_2021_1/blob/master/28Octubre.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### SOLUCION 1
```
h1 = 0
h2 = 0
m1 = 0
m2 = 0 # 1440 + 24 *6
contador = 0 # 5 + (1440 + ?) * 2 + 144 + ... | github_jupyter |
## Getting ready
```
import tensorflow as tf
import tensorflow.keras as keras
import pandas as pd
import numpy as np
census_dir = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/'
train_path = tf.keras.utils.get_file('adult.data', census_dir + 'adult.data')
test_path = tf.keras.utils.get_file('adult.t... | github_jupyter |
# Automated Machine Learning
#### Forecasting away from training data
## Contents
1. [Introduction](#Introduction)
2. [Setup](#Setup)
3. [Data](#Data)
4. [Prepare remote compute and data.](#prepare_remote)
4. [Create the configuration and train a forecaster](#train)
5. [Forecasting from the trained model](#forecasti... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import scipy
import matplotlib.pyplot as plt
df_Dodgers = pd.read_csv('dodgers.csv')
df_Dodgers.head()
# Takes binary categories and returns 0 or 1
def binning_cats(word, zero='no', one='yes'):
if word.strip().lower()==zero:
return(0)
elif word.strip().lower... | github_jupyter |
```
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi']= 100
import seaborn... | github_jupyter |
# Introduction and Foundations: Titanic Survival Exploration
> Udacity Machine Learning Engineer Nanodegree: _Project 0_
>
> Author: _Ke Zhang_
>
> Submission Date: _2017-04-27_ (Revision 2)
## Abstract
In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of ... | github_jupyter |
<a href="https://colab.research.google.com/github/darshanbk/100-Days-Of-ML-Code/blob/master/Getting_started_with_BigQuery.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Before you begin
1. Use the [Cloud Resource Manager](https://console.clou... | github_jupyter |
# Solving Multi-armed Bandit Problems
We will focus on how to solve the multi-armed bandit problem using four strategies, including epsilon-greedy, softmax exploration, upper confidence bound, and Thompson sampling. We will see how they deal with the exploration-exploitation dilemma in their own unique ways. We will a... | 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 |
# Data Space Report
<img src="images/polito_logo.png" alt="Polito Logo" style="width: 200px;"/>
## Pittsburgh Bridges Data Set
<img src="images/andy_warhol_bridge.jpg" alt="Andy Warhol Bridge" style="width: 200px;"/>
Andy Warhol Bridge - Pittsburgh.
Report created by Student Francesco Maria Chiarlo s253666, ... | github_jupyter |
# Setup
First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20.
``... | github_jupyter |
<a href="https://colab.research.google.com/github/JamesHorrex/AI_stock_trading/blob/master/SS_AITrader_INTC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%matplotlib inline
import numpy as np
import tensorflow as tf
print(tf.__version__)
!pip ... | github_jupyter |
# Deriving a Point-Spread Function in a Crowded Field
### following Appendix III of Peter Stetson's *User's Manual for DAOPHOT II*
### Using `pydaophot` form `astwro` python package
All *italic* text here have been taken from Stetson's manual.
The only input file for this procedure is a FITS file containing reference... | github_jupyter |
```
# python standard library
import sys
import os
import operator
import itertools
import collections
import functools
import glob
import csv
import datetime
import bisect
import sqlite3
import subprocess
import random
import gc
import shutil
import shelve
import contextlib
import tempfile
import math
import pickle
# ... | github_jupyter |
# 内容
- lightGBMモデル初版
- ターゲットエンコーディング：Holdout TS
- 外部データ3つ(ステージ面積1,ステージ面積2,ブキ)を結合
- ステージ面積1:
https://probspace-stg.s3-ap-northeast-1.amazonaws.com/uploads/user/c10947bba5cde4ad3dd4a0d42a0ec35b/files/2020-09-06-0320/stagedata.csv
- ステージ面積2:https://stat.ink/api-info/stage2
- ブキ:https://stat.ink/api-info/we... | github_jupyter |
```
import numpy as np
from copy import deepcopy
from scipy.special import expit
from scipy.optimize import minimize
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression as skLogisticRegression
from sklearn.multiclass import OneVsRestClassifier as skOneVsRestClassifier
class OneVsR... | github_jupyter |
```
import tensorflow as tf
from keras.layers import Conv1D, Dense, Dropout, Concatenate, GlobalAveragePooling1D, GlobalMaxPooling1D, Input, MaxPooling1D, Flatten
from keras.optimizers import Adam
from keras.losses import sparse_categorical_crossentropy
from sklearn.model_selection import train_test_split
from keras.ut... | github_jupyter |
# OPTIMIZATION PHASES
### List of variables
<table>
<thead>
<tr>
<th style="width: 10%">Variable</th>
<th style="width: 45%">Description</th>
<th style="width: 30%">Comment</th>
</tr>
</thead>
<tbody>
<tr>
<td>$B$</td>
<td... | github_jupyter |
# BLU15 - Model CSI
## Intro:
It often happens that your data distribution changes with time.
More than that, sometimes you don't know how a model was trained and what was the original training data.
In this learning unit we're going to try to identify whether an existing model meets our expectations and redeploy... | github_jupyter |
# Reviewing Automated Machine Learning Explanations
As machine learning becomes more and more and more prevelant, the predictions made by models have greater influence over many aspects of our society. For example, machine learning models are an increasingly significant factor in how banks decide to grant loans or doc... | 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 |
```
import numpy as np
#Load the predicted 9x12 array
#1st pass
im1=np.array([[4,4,4,4,4,4,4,4,4,4,4,4],
[6,6,2,1,6,6,6,6,6,1,1,2],
[6,6,6,1,1,6,6,6,6,1,1,2],
[2,6,6,6,1,5,5,5,6,1,1,2],
[5,6,6,6,5,5,5,5,5,1,5,5],
[5,5,2,5,5,5,5,5,5,1,5,5],
... | github_jupyter |
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gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Start-to-Finish Example: Unit Testing `GiRaFFE_NRPy`: $A_... | github_jupyter |
http://www.yr.no/place/Norway/Telemark/Vinje/Haukeliseter/climate.month12.html
```
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import numpy as np
import csv
import pandas as pd
import datetime
from datetime import date
import calendar
%matplotlib inline
year = np.arange(2000,2017, 1)
T_av = [-4.1... | github_jupyter |
# DB2 Jupyter Notebook Extensions
Version: 2021-08-23
This code is imported as a Jupyter notebook extension in any notebooks you create with DB2 code in it. Place the following line of code in any notebook that you want to use these commands with:
<pre>
%run db2.ipynb
</pre>
This code defines a Jupyter/Python mag... | github_jupyter |
# Profiling TensorFlow Multi GPU Multi Node Training Job with Amazon SageMaker Debugger
This notebook will walk you through creating a TensorFlow training job with the SageMaker Debugger profiling feature enabled. It will create a multi GPU multi node training using Horovod.
### (Optional) Install SageMaker and SMDe... | github_jupyter |
## Лабораторная работа 2 - Линейная и полиномиальная регрессия.
Одна из множества задач, которой занимается современная физика это поиск материала для изготовления сверхпроводника, работающего при комнатной температуре. Кроме теоретических методов есть и подход со стороны статистики, который подразумевает анализ базы ... | github_jupyter |
```
import sys
import os
sys.path.append(os.path.abspath("../src/"))
import extract.data_loading as data_loading
import extract.compute_predictions as compute_predictions
import extract.compute_shap as compute_shap
import extract.compute_ism as compute_ism
import model.util as model_util
import model.profile_models as ... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#init" data-toc-modified-id="init-1"><span class="toc-item-num">1 </span>init</a></span></li><li><span><a href="#モデリング" data-toc-modified-id="モデリング-2"><span class="toc-item-num">2 </spa... | github_jupyter |
<a href="https://colab.research.google.com/github/poojan-dalal/fashion-MNIST/blob/master/Course_1_Part_4_Lesson_2_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import tensorflow as tf
print(tf.__version__)
```
The Fashion MNIST data ... | github_jupyter |
# [Advent of Code 2020: Day 10](https://adventofcode.com/2020/day/10)
## \-\-\- Day 10: Adapter Array \-\-\-
Patched into the aircraft's data port, you discover weather forecasts of a massive tropical storm. Before you can figure out whether it will impact your vacation plans, however, your device suddenly turns off!... | github_jupyter |
<img style="float: right;" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAOIAAAAjCAYAAACJpNbGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAABR0RVh0Q3JlYXRpb24gVGltZQAzLzcvMTNND4u/AAAAHHRFWHRTb2Z0d2FyZQBBZG9iZSBGaXJld29ya3MgQ1M26LyyjAAACMFJREFUeJztnD1y20gWgD+6nJtzAsPhRqKL3AwqwQdYDpXDZfoEppNNTaWbmD7BUEXmI3EPMF... | github_jupyter |
# 7.6 Transformerモデル（分類タスク用）の実装
- 本ファイルでは、クラス分類のTransformerモデルを実装します。
※ 本章のファイルはすべてUbuntuでの動作を前提としています。Windowsなど文字コードが違う環境での動作にはご注意下さい。
# 7.6 学習目標
1. Transformerのモジュール構成を理解する
2. LSTMやRNNを使用せずCNNベースのTransformerで自然言語処理が可能な理由を理解する
3. Transformerを実装できるようになる
# 事前準備
書籍の指示に従い、本章で使用するデータを用意します
```
import math
import num... | github_jupyter |
# Run model module locally
```
import os
# Import os environment variables for file hyperparameters.
os.environ["TRAIN_FILE_PATTERN"] = "gs://machine-learning-1234-bucket/gan/data/cifar10/train*.tfrecord"
os.environ["EVAL_FILE_PATTERN"] = "gs://machine-learning-1234-bucket/gan/data/cifar10/test*.tfrecord"
os.environ[... | github_jupyter |
```
# !/usr/bin/env python
# 测试tensorflow是否安装好
import numpy as np
import tensorflow as tf
# Prepare train data
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10
# Define the model
X = tf.placeholder("float")
Y = tf.placeholder("float")
w = tf.Variable(0.0, name="we... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import xarray as xr
import seaborn as sns
sns.set()
```
#### Check surface fluxes of CO$_2$
```
# check the data folder to swith to another mixing conditions
#ds = xr.open_dataset('data/results_so4_adv/5_po75-25_di10e-9/water.nc')
ds = xr.open... | github_jupyter |
# Using Ray for Highly Parallelizable Tasks
While Ray can be used for very complex parallelization tasks,
often we just want to do something simple in parallel.
For example, we may have 100,000 time series to process with exactly the same algorithm,
and each one takes a minute of processing.
Clearly running it on a s... | github_jupyter |
<a href="https://colab.research.google.com/github/Collin-Campbell/DS-Unit-2-Linear-Models/blob/master/module3-ridge-regression/LS_DS_213_assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 1, Module... | github_jupyter |
# Zircon model training notebook; (extensively) modified from Detectron2 training tutorial
This Colab Notebook will allow users to train new models to detect and segment detrital zircon from RL images using Detectron2 and the training dataset provided in the colab_zirc_dims repo. It is set up to train a Mask RCNN mode... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
%matplotlib inline
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
... | github_jupyter |
## Import packages
```
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
# general packages
import numpy as np
import matplotlib.pyplot as plt
import os
import seaborn as sns
# sklearn models
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.... | github_jupyter |
<table width=60% >
<tr style="background-color: white;">
<td><img src='https://www.creativedestructionlab.com/wp-content/uploads/2018/05/xanadu.jpg'></td>></td>
</tr>
</table>
---
<img src='https://raw.githubusercontent.com/XanaduAI/strawberryfields/master/doc/_static/strawberry-fields-text.png'>
---... | github_jupyter |
```
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
```
# Pytorch: An automatic differentiation tool
`Pytorch`를 활용하면 복잡한 함수의 미분을 손쉽게 + 효율적으로 계산할 수 있습니다!
`Pytorch`를 활용해서 복잡한 심층 신경망을 훈련할 때, 오차함수에 대한 파라미터의 편미분치를 계산을 손쉽게 수행할수 있습니다!
## Pytorch 첫만남
우리에게 아래와 같은 간단한 선형식이 주어져있다고 생각해볼까요... | github_jupyter |
# Analyzing IMDB Data in Keras
```
# Imports
import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
```
... | github_jupyter |
<a href="https://colab.research.google.com/github/phreakyphoenix/MXNet-GluonCV-AWS-Coursera/blob/master/Module_5_LeNet_on_MNIST.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Graded Assessment
In this assessment you will write a full end-to-end ... | github_jupyter |
```
import torch
from torchtext import data
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
SEED = 1
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
with open('../stanford-corenlp-full-2018-10-05/stanfordSentimentTreebank/dictionary.txt','r') as f:
dic = f.readlin... | github_jupyter |
# Callbacks and Multiple inputs
```
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.preprocessing import scale
from keras.optimizers import SGD
from keras.layers import Dense, Input, concatenate, BatchNormalization
from keras.callbacks import EarlyStopping, Tens... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
train_df = pd.read_csv(Path('./Resources/2019loans.csv'))
test_df =... | github_jupyter |
#Import Data
```
import numpy as np
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
# load data
import os
from google.colab import drive
drive.mount('/content/drive')
filedir = './drive/My Drive/Final/CNN_data'
with open(filedir + '/' + 'feature_extracted', 'rb') as f:
X = np.load(f)... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/extract_value_to_points.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank"... | github_jupyter |
# Fmriprep
Today, many excellent general-purpose, open-source neuroimaging software packages exist: [SPM](https://www.fil.ion.ucl.ac.uk/spm/) (Matlab-based), [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki), [AFNI](https://afni.nimh.nih.gov/), and [Freesurfer](https://surfer.nmr.mgh.harvard.edu/) (with a shell interface).... | github_jupyter |
# Software Engineering
Software engineering was first introduced in the 1960s in an effort to treat more rigorously the often frustrating task of designing and developing computer programs. It was around this time that the computer community became increasingly worried about the fact that software projects were typica... | github_jupyter |
```
# dataset
# https://cogcomp.seas.upenn.edu/Data/QA/QC/
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import pickle
import string
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import nltk
from nltk.corpus import stopwords
from nltk.stem.p... | github_jupyter |
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