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# Human Activity Prediction
### Libraries
```
## Modelling
library(caret); library(rattle); library(randomForest); library(e1071); library(forecast)
## Data processing/visualization
library(dplyr); library(ggplot2); library(rattle)
```
#### Getting the data
Downloading
```
train_URL = "https://d396qusza40orc.cloud... | github_jupyter |
# Gensim
> Gensim is designed to automatically extract semantic topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible.
> Gensim is designed to process raw, unstructured digital texts (“plain text”). The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Al... | github_jupyter |
# Demo of Ch2. Linear Classifier
----
This is the sample code of TU-ETP-AD1062 Machine Learning Fundamentals.
For more information, please refer to:
https://sites.google.com/view/tu-ad1062-mlfundamentals/
## Import packages
----
- `numpy`: Provide linear algebra related computation ability, with `norm` used to measur... | github_jupyter |
### MEDC0106: Bioinformatics in Applied Biomedical Science
<p align="center">
<img src="../../resources/static/Banner.png" alt="MEDC0106 Banner" width="90%"/>
<br>
</p>
---------------------------------------------------------------
# 12 - Introduction to Biopython - Proteins Exercises
*Written by:* Mateusz Kac... | github_jupyter |
```
import json
import logging
from collections import OrderedDict
from typing import Optional
import coloredlogs
from ph4_walkingpad.profile import Profile, calories_walk2_minute, calories_rmrcb_minute
from ph4_walkingpad.analysis import StatsAnalysis
import scipy
import numpy as np
import pandas as pd
import seabor... | github_jupyter |
# Logistic Regression
(before Multinomial logistic regression)
We want to predict the probability of an input belonging to one of two classes.
---
## Study case :
Classify the zero and one digits from MNist dataset
### a) Dataset !
- Input: Images of size 28*28 where a one or two is present
- Output: 0 if the i... | github_jupyter |
```
# HIDDEN
from datascience import *
import matplotlib
matplotlib.use('Agg', warn=False)
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
import numpy as np
# Interaction
from IPython.display import display
from functools import partial
from ipywidgets import interact, interac... | github_jupyter |
```
import numpy as np
import pandas as pd
import xarray as xr
import geojson
import geopandas as gpd
from shapely.geometry import Polygon as shpPolygon
from scipy.optimize import differential_evolution
from numba import jit
from hm import gr4j, gr4j_bounds
from bokeh.plotting import figure, show, output_file
from ... | github_jupyter |
```
import numpy as np
```
**Module** is an abstract class which defines fundamental methods necessary for a training a neural network. You do not need to change anything here, just read the comments.
```
class Module(object):
def __init__ (self):
self.output = None
self.gradInput = None
s... | github_jupyter |
```
import tensorflow as tf
from tensorflow import keras
print( 'Tensorflow : ',tf.__version__)
print( ' |-> Keras : ',keras.__version__)
```
# Text generation with LSTM
This notebook contains the code samples found in Chapter 8, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-wit... | github_jupyter |
<a href="https://colab.research.google.com/github/amir1m/learning-ml/blob/master/FCML_CoinToss.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from scipy.special import comb
from scipy.stats import beta
import matplotlib.pyplot as plt
import n... | github_jupyter |
# Time Series Forecasting with Linear Learner
_**Using Linear Regression to Forecast Monthly Demand**_
---
---
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Train](#Train)
1. [Host](#Host)
1. [Forecast](#Forecast)
1. [Extensions](#Extensions)
---
## Background
Forecasting is ... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import h5py
import os
from preprocessor import Preprocessor
def save_data_set(x, y, data_type, path, s=''):
if not os.path.exists(path):
os.makedirs(path)
fname=os.path.join(path, f'x_{data_type}{s}.h5')
... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
import time
import random, argparse, logging, os
from collections import namedtuple
from minatar import Environment
import matplotlib.pyplot as plt
import numpy as np
# remove for game display
%matplotlib inline
NUM_FRAME... | github_jupyter |
<table width="100%">
<tr style="border-bottom:solid 2pt #009EE3">
<td style="text-align:left" width="10%">
<a href="open_h5.dwipynb" download><img src="../../images/icons/download.png"></a>
</td>
<td style="text-align:left" width="10%">
<a><img class="not_active_img" ... | github_jupyter |
# 08. Pseudo-Random Numbers, Simulating from Some Discrete and Continuous Random Variables
## [Inference Theory 1](https://lamastex.github.io/scalable-data-science/infty/2018/01/)
©2018 Raazesh Sainudiin. [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
- The $Uniform(0... | github_jupyter |
## Functions for TAA Component Preparation
Complementary notebook to executable on the same topic.
Functions are presented in an order closely following the exe notebook steps:
#### Libraries
```
import os
import re
import numpy as np # basic numeric calculation
import pandas as ... | github_jupyter |
# Empirical Approximation overview
For most models we use sampling MCMC algorithms like Metropolis or NUTS. In PyMC3 we got used to store traces of MCMC samples and then do analysis using them. There is a similar concept for the variational inference submodule in PyMC3: *Empirical*. This type of approximation stores p... | github_jupyter |
# Hill Climbing
---
In this notebook, we will train hill climbing with adaptive noise scaling with OpenAI Gym's Cartpole environment.
### 1. Import the Necessary Packages
```
import gym
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
```
### 2. Define the Policy
... | github_jupyter |
# 프로젝트 1. 영화 리뷰 감정 분석
**RNN 을 이용해 IMDB 데이터를 가지고 텍스트 감정분석을 해 봅시다.**
이번 책에서 처음으로 접하는 텍스트 형태의 데이터셋인 IMDB 데이터셋은 50,000건의 영화 리뷰로 이루어져 있습니다.
각 리뷰는 다수의 영어 문장들로 이루어져 있으며, 평점이 7점 이상의 긍정적인 영화 리뷰는 2로, 평점이 4점 이하인 부정적인 영화 리뷰는 1로 레이블링 되어 있습니다. 영화 리뷰 텍스트를 RNN 에 입력시켜 영화평의 전체 내용을 압축하고, 이렇게 압축된 리뷰가 긍정적인지 부정적인지 판단해주는 간단한 분류 모델을 만드는 것이 이... | github_jupyter |
# Decision Tree Algorithm 👨🏻💻
---
## SKlearn implementation
---
### `Imports`
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
```
### `Importing Dataset`
Next, we import the dataset from the CSV file to the Pandas dataframes.
```
col = [ 'Cla... | github_jupyter |
# PANDAS!!!
IS made for working with data sets generally below or around 1 GB in size, but really this limit varies depending on the memory constraints of the device you run it on. A good rule of thumb is have at least five to ten times the amount of memory on the device as your data set. Once the data set starts to e... | github_jupyter |
# Demonstration of the Metrics To-Date
For a complete list of metrics and their documentation, please see the API Metrics [documentation](../API/simulation_api.md#metrics-computation).
This demonstration will rely on the results produced in the "How To" notebook.
```
from pprint import pprint
import pandas as pd
f... | github_jupyter |
# Tribolium embryo morphometry over time in Napari
Authors: Robert Haase, Daniela Vorkel, 2020
This is the pyclesperanto version of a workflow earlier [published for clij2](https://clij.github.io/clij2-docs/md/tribolium_morphometry/).
[ImageJ Macro original](https://github.com/clij/clij2-docs/tree/master/src/main/mac... | github_jupyter |
# Practice Assignment: Understanding Distributions Through Sampling
** *This assignment is optional, and I encourage you to share your solutions with me and your peers in the discussion forums!* **
To complete this assignment, create a code cell that:
* Creates a number of subplots using the `pyplot subplots` or `ma... | github_jupyter |
##### Copyright 2019 DeepMind Technologies Limited.
```
#@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 ... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
#Fecha
import locale
locale.setlocale(locale.LC_ALL,'es_ES.UTF-8')
import dateparser
import datetime as datet
from datetime import datetime
sns.set()
date_fmt = '%b %Y'
#Graficación
import plotly
import plotly.express as px
from plotly.subplots import ... | github_jupyter |
# 5. Compressing h5 Training/Validation Dataset
Attempting to compress the h5 dataset to allow for temporary storage of dataset on Compute Canada Cedar GPU node SSD. Compression was done using create_compressed_h5.py in the same directory.
```
import sys
import os
import random
import h5py
from collections import Coun... | github_jupyter |
# Tutorial 07: Networks from Custom Templates
In the previous tutorial, we discussed how OpenStreetMap files can be simulated in Flow. These networks, however, may at time be imperfect, as we can see in the toll section of the Bay Bridge (see the figure below). The simulators SUMO and Aimsun both possess methods for a... | github_jupyter |
```
from __future__ import print_function
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
#from sklearn.datasets import fetch_mldata
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preproces... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("whitegrid")
```
# Model: xgboost
```
from sklearn.metrics import classification_report, confusion_matrix, plot_confusion_matrix, roc_auc_score, roc_curve, precision_recall_curve
from sklearn.pipeline impor... | github_jupyter |
```
import pandas as pd
import numpy as np
import nltk
from collections import Counter
from sklearn.metrics import log_loss
from scipy.optimize import minimize
import multiprocessing
import difflib
import time
import gc
import xgboost as xgb
from sklearn.cross_validation import train_test_split
from sklearn.feature_ex... | github_jupyter |
# Tutorial Part 21: Introduction to Bioinformatics
So far in this tutorial, we've primarily worked on the problems of cheminformatics. We've been interested in seeing how we can use the techniques of machine learning to make predictions about the properties of molecules. In this tutorial, we're going to shift a bit an... | github_jupyter |
# Задание 2.1 - Нейронные сети
В этом задании вы реализуете и натренируете настоящую нейроную сеть своими руками!
В некотором смысле это будет расширением прошлого задания - нам нужно просто составить несколько линейных классификаторов вместе!
<img src="https://i.redd.it/n9fgba8b0qr01.png" alt="Stack_more_layers" wi... | github_jupyter |
# Evaluate likelihood ratio
```
import sys, os
sys.path.append('../')
import logging
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter
from scipy.stats import norm
from sklearn.metrics import roc_curve
from inference.utils import s_from_r, shuffle
impor... | github_jupyter |
*Copyright (c) Microsoft Corporation. All rights reserved.*
*Licensed under the MIT License.*
# Natural Language Inference on MultiNLI Dataset using Transformers
# Before You Start
It takes about 4 hours to fine-tune the `bert-large-cased` model on a Standard_NC24rs_v3 Azure Data Science Virtual Machine with 4 NV... | github_jupyter |
# This notebook refines the model using only playable songs:
### While generating new songs to play we only want to generate songs with difficulty of 8. Therefore we will load the refined model and then finetune it only to operate on our desired songs
```
from pathlib import Path
import pandas as pd
import re
#Get t... | github_jupyter |
# Getting Data Ready
Forecasting is used in a variety of applications and business use cases: For example, retailers need to forecast the sales of their products to decide how much stock they need by location, Manufacturers need to estimate the number of parts required at their factories to optimize their supply chain... | github_jupyter |
# Solving the Taxi Problem Using SARSA
### Goal:
Say our agent is the driving the taxi. There are totally four locations and the agent has to
pick up a passenger at one location and drop at the another. The agent will receive +20
points as a reward for successful drop off and -1 point for every time step it takes. Th... | github_jupyter |
```
# %pip install --upgrade pip --user
# %pip install zarr --user
# %pip install tables --user
# %pip install git+https://github.com/simpeg/simpeg.git@simulation-tdem --user
# %pip install dask dask_jobqueue --user
# %pip install git+https://github.com/simpeg-research/casingSimulations.git@simulation --user
import num... | github_jupyter |
```
%%capture
!apt-get install cmake
!apt-get install zlib1g-dev
!pip install gym[atari]
!pip install JSAnimation
import numpy as np
# import cPickle as pickle
import matplotlib.pyplot as plt
from JSAnimation.IPython_display import display_animation
from matplotlib import animation
import gym
from keras.models import ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Water/usgs_watersheds.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 |
# Deploying Tensorflow models on Verta
Within Verta, a "Model" can be any arbitrary function: a traditional ML model (e.g., sklearn, PyTorch, TF, etc); a function (e.g., squaring a number, making a DB function etc.); or a mixture of the above (e.g., pre-processing code, a DB call, and then a model application.) See mo... | github_jupyter |
```
from osgeo import gdal,ogr,osr
raster = r'/usgs/data0/bathy/sandy/zip3/big.tif'
ofile = r'/usgs/data2/notebook/data/big.ncml'
def GetExtent(gt,cols,rows):
''' Return list of corner coordinates from a geotransform
@type gt: C{tuple/list}
@param gt: geotransform
@type cols: C{int}
... | github_jupyter |
# Simulated Sky Signal in time domain
In this lesson we will use the TOAST Operator `OpSimPySM` to create timestreams for an instrument given a sky model.
```
# Load common tools for all lessons
import sys
sys.path.insert(0, "..")
from lesson_tools import (
fake_focalplane
)
# Capture C++ output in the jupyter c... | github_jupyter |
# PyEcharts
**注意**
- 本文案例来源:https://github.com/pyecharts/pyecharts-gallery
- 本文用来直接复制到使用地方进行使用...T_T
## 折线图
```
import pyecharts.options as opts
from pyecharts.charts import Line
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
y_data2 = [i*1.5 for i in y_data]... | github_jupyter |
# Image Classification using Pre-trained model
## Step 1- Download the model
```
!omz_downloader --name inception-resnet-v2-tf
```
## Step 2 - Import the libraries
```
import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core
from pathlib import Path
from IPython.display import... | github_jupyter |
# Lesson 04. Python intro
**Udacity Full Stack Web Developer Nanodegree program**
Part 01. Programming fundamentals and the web
[Programming foundations with Python](https://www.udacity.com/course/programming-foundations-with-python--ud036)
Brendon Smith
br3ndonland
## 01. What will we create?
Kunal, Udacity... | github_jupyter |
This is one of the Objectiv example notebooks. For more examples visit the
[example notebooks](https://objectiv.io/docs/modeling/example-notebooks/) section of our docs. The notebooks can run with the demo data set that comes with the our [quickstart](https://objectiv.io/docs/home/quickstart-guide/), but can be used t... | github_jupyter |
# 5 - Creating, getting and visualizing Mesh groups and Mesh Fields
**This Notebook will introduce you to**:
1. what is a Mesh Group
2. the Mesh Group data model
3. how to create a mesh Group
4. the Mesh Field data model
5. how to add a field to a Mesh Group
6. how to get Mesh Groups and Mesh Fields
... | github_jupyter |
<div style="text-align: right">Dino Konstantopoulos, 3 June 2021</div>
# Introducing sentence transformers
A python package called **sentence-transformers** that has specifically been optimized for doing semantic textual similarity searches. The model creates a 1024-dimensional embedding for each sentence, and the sim... | github_jupyter |
# Basic CNN based digit recognizer
In this tutorial we shall go through a bangla digit recognizer model in details. Our model is going to be based on a convolutional neural network (CNN). The focus is to get familiar with the components of a bangla digit recognizer framework. There are three steps in building this dig... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=1
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experim... | github_jupyter |
```
%pylab inline
import pandas as pd
import os
# Just use 1 GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import pandas as pd
from pyvirchow.io import WSIReader
from pyvirchow.morphology import TissuePatch
from matplotlib.patches import Polygon
from s... | github_jupyter |
<a href="https://colab.research.google.com/github/OUCTheoryGroup/colab_demo/blob/master/02_Unsupervised_Segmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Unsupervised Image Segmentation. *ICASSP* 2018
**图片无监督语义分割**，作者是东京大学的 Asako Kanezak... | github_jupyter |
# Monte Carlo Simulations with the Efficient Frontier
### Summary of Efficient Frontier
The Efficient fronter is a set of optimal portfolios that offer the highest expected return for a defined level of risk. It provides a great visualization on how to choose an optimal portfolio mathematically. _*Risk is defined as t... | github_jupyter |
# Think Bayes solutions: Chapter 4
This notebook presents solutions to exercises in Think Bayes.
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
```
from __future__ import print_function, division
import numpy as np
import thinkbayes2
from thinkbayes2 import Pmf, Cdf, Suite
import ... | github_jupyter |
# Templating and Jinja2
---
```html
<div class="simplelist">
<ul>
<li>Item 1</li>
<li>Item 2</li>
<li>Item 3</li>
</ul>
</div>
```
<div class="simplelist">
<ul>
<li>Item 1</li>
<li>Item 2</li>
<li>Item 3</li>
</ul>
</div>
```html
<table>
<thead>
... | github_jupyter |
# Accessing Physical Quantities
In order to compute the synthetic spectrum, TARDIS must either be told
or must calculate many physical properties of the model. To understand and
test the code it can be important to look at these values. One
easy way to do this is to run TARDIS in an interactive mode and then
inspect t... | github_jupyter |
# Optimization Methods
Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorit... | github_jupyter |
**Copyright 2018 Google LLC.**
Licensed under the Apache License, Version 2.0 (the "License");
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 r... | github_jupyter |
# Amazon SageMaker と Amazon Redshift を利用した、高速、柔軟、セキュアな機械学習基盤の構築
必要な Python Package をインポートします。
```
# Import packages
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import boto3
import json
```
## Obtain parameters from AWS CloudFormation
AWS CloudFormation で設定したパラメータを取得します。
```
# Please edit st... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#Intro" data-toc-modified-id="Intro-1"><span class="toc-item-num">1 </span>Intro</a></span></li><li><span><a href="#Save-and-Restore-Variables" data-toc-modified-id="Save-... | github_jupyter |
## Coding Exercise #0707
### 1. Convolutional Neural Network (color images):
```
import numpy as np
import pandas as pd
# import tensorflow as tf
# from keras.datasets.cifar10 import load_data
import tensorflow.compat.v1 as tf
from tensorflow.keras.datasets.cifar10 import load_data
import matplotlib.pyplot as plt
tf... | github_jupyter |
```
#Import python packages. Some may need to be installed using the Python Package Manager.
import os
import datetime
import exifread
from PIL import Image
import wikipedia
#This is the only variable that needs to be set. It is the path to the folder of images.
path = "C:\\1_projects\\138_fedgis2021\\images\\"
#This... | github_jupyter |
# Example: Regenerating Data from
# [R. Wu et al. / Elec Acta 54 25 (2010) 7394–7403](http://www.sciencedirect.com/science/article/pii/S0013468610009503)
Import the modules
```
import scipy as sp
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
import openpnm.models.geometry as gm
import openpn... | github_jupyter |
```
import pandas as pd
import numpy as np
```
# 自由现金流估值法 DCF
有以下四种模型：
1. 零增长模型
2. 不变增长模型
3. 两阶段模型
4. 三阶段模型
不同的是自由现金流的使用和贴现的方式不同。
**计算步骤**：
1. 计算自由现金流并依据相应的方法折现($\star\star\star\star\star$, the most important, this is what the code solves)
2. 计算股权价值= 1.+金融资产+长期股权投资-公司债务
3. 计算少数股东比例
4. 归属于上市公司股东的价值=股权价值$\times$(1-... | github_jupyter |
< [Classes](PythonIntroCh7.ipynb) | [Contents](PythonIntro.ipynb) | [File I/O](PythonIntroCh9.ipynb) >
# 8. Modules
## 8.1 Introduction
Last lesson we covered the killer topic of Classes. As you can remember, classes are neat combinations of variables and functions in a nice, neat package. Programming lingo calls this... | github_jupyter |
# Functions
If you find yourself doing the same thing over and over again in your code, it might be time to write a function.
Functions are blocks of reusable code -- little boxes that (usually) take inputs and return outputs. In Excel, `=SUM()` is a function. `print()` is one of Python's built-in function.
You can ... | github_jupyter |
# Convolutional Neural Networks with Tensorflow
"Deep Learning" is a general term that usually refers to the use of neural networks with multiple layers that synthesize the way the human brain learns and makes decisions. A convolutional neural network is a kind of neural network that extracts *features* from matrices ... | github_jupyter |
# Thematic Reports
Thematic reports run historical analyses on the exposure of a portfolio to various Goldman Sachs Flagship Thematic baskets over a specified date range.
### Prerequisite
To execute all the code in this tutorial, you will need the following application scopes:
- **read_product_data**
- **read_financ... | github_jupyter |
# Pythonic APIs: the workshop notebook
## Tutorial overview
* Introduction
* A simple but full-featured Pythonic class
* **Exercise:** custom formatting and alternate constructor
* A Pythonic sequence
* **Exercise:** implementing sequence behavior
* *Coffee break*
* A Pythonic sequence (continued)
* **Exercise:... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn import preprocessing
from sklearn.linear_model import Log... | github_jupyter |
# [모듈 3.1] 모델 배포 및 추론 (VPC 및 No VPC 모두에서 사용 가능)
이 노트북은 아래와 같은 작업을 합니다.
- 엔드포인트 생성
- SageMaker Estimator 생성
- Training Job 을 Estimator 에 연결
- 엔드포인트 생성은 위의 방법 말고도 다른 방법이 추가적으로 있습니다. 아래를 참고 하세요
- https://docs.aws.amazon.com/ko_kr/sagemaker/latest/dg/ex1-deploy-model.html
- 엔드포인트 대상으로 추론
- 추론 예... | github_jupyter |
This example notebook uses the averaging functions found ins the diff_classifier msd module to find average msd profiles over input msd datasets using precision-weighted averaging. Precision is the inverse of the standard squared error. This increases the contribution of videos that have many particles and more homogen... | github_jupyter |
### DCGANs `MNIST` dataset.
```
import tensorflow as tf
from tensorflow.keras import layers, Model
from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Reshape, Conv2DTranspose, MaxPooling2D, UpSampling2D, LeakyReLU
from tensorflow.keras.activations import relu
from tensorflow.keras.models import Sequent... | github_jupyter |
# Random Forests - Redux
From Fastai ML1 [Lesson 1 Intro to Random Forests](https://github.com/fastai/fastai/blob/master/courses/ml1/lesson1-rf.ipynb)
This notebook turned into a redux of my [first RF Code Along](https://github.com/WNoxchi/Kaukasos/blob/master/FAML1/Lesson1-RandomForests.ipynb) with notes.
---
## ... | github_jupyter |
<img src="http://upload.wikimedia.org/math/7/5/2/752fd6396a9c9d026f10eccb39ddca15.png"/>
$$V(x) = w\left(\frac{L}{2} - x\right)$$
$$M(x) = \frac{w}{2}\left(L x - x^2\right)$$
$$\theta(x) = \frac{- w}{2 EI}\left(\frac{L x^2}{2} - \frac{x^3}{3} +C\right)$$
$$\Delta(x) = \frac{- w}{2 EI}\left(\frac{L x^3}{6} - \frac{x... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#export
from fastai.torch_basics import *
from fastai.data.all import *
#hide
from nbdev.showdoc import *
#default_exp text.core
#default_cls_lvl 3
```
# Text core
> Basic function to preprocess text before assembling it in a `Dat... | github_jupyter |
_Lambda School Data Science_
This sprint, your project is about water pumps in Tanzania. Can you predict which water pumps are faulty?
# Decision Trees
#### Objectives
- clean data with outliers
- impute missing values
- use scikit-learn for decision trees
- understand why decision trees are useful to model non-line... | github_jupyter |
```
from openeye import oechem, oedepict
import oenotebook as oenb
import pandas as pd
def depict_smiles(smiles):
mol = oechem.OEMol()
oechem.OESmilesToMol(mol,smiles)
return oenb.draw_mol(mol)
depict_smiles(smiles)
```
## SM11
Initial mol is the same as the tautomer: SM11_micro018 and SM11_micro020
SM1... | github_jupyter |
```
import numpy as np
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web
import datetime
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_pdf import PdfPages
import random
import pingouin
def gen_pco... | github_jupyter |
# Translate demand files format: from .dat to flow.csv
```
import sys
import pandas as pd
import xml.etree.ElementTree as ET
import datetime
import re
import nltk
import numpy
import os
from IPython.display import display, HTML
low_memory=False
PATH="data/OD_MADRID_v2"
SCENARIO="madrid_barrio_salamanca_od"
SCENARIO="... | github_jupyter |
## **MODULE 3: Fundamental analysis using Regression**
###***3.1***
```
# Import pandas library
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv('/content/GOLD.csv') #data of the last 2 years price action of Indian (MCX) gold standard.
df.head(5)
df.tail(5) #EXPLORATION OF DATA... | github_jupyter |
```
# Copyright 2020 IITK EE604A Image Processing. All Rights Reserved.
#
# Licensed under the MIT License. Use and/or modification of this code outside of EE604 must reference:
#
# © IITK EE604A Image Processing
# https://github.com/ee604/ee604_assignments
#
# Author: Shashi Kant Gupta and Prof K. S. Venkatesh, Depa... | github_jupyter |
# Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that.
So rather than writing:
>"Congratulations on the promotion! Let's get coffee and ... | github_jupyter |
# Bayesian Estimation of Orbital Scaling Parameters
## Introduction
These notes briefly outline a Bayesian approach to estimating the statistical distribution of the orbital scaling (or $\lambda$) parameters from NIST data and their associated experimental error bars. The atomic structure calculation can be viewed ... | 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 |
```
import open3d as o3d
import numpy as np
import sys
# monkey patches visualization and provides helpers to load geometries
sys.path.append('..')
import open3d_tutorial as o3dtut
# change to True if you want to interact with the visualization windows
o3dtut.interactive = False
```
# File IO
This tutorial shows how ... | github_jupyter |
```
%matplotlib inline
```
PyTorch是什么?
================
基于Python的科学计算包，服务于以下两种场景:
- 作为NumPy的替代品，可以使用GPU的强大计算能力
- 提供最大的灵活性和高速的深度学习研究平台
开始
---------------
Tensors（张量）
^^^^^^^
Tensors与Numpy中的 ndarrays类似，但是在PyTorch中
Tensors 可以使用GPU进行计算.
```
from __future__ import print_function
import torch
```
创建一个 5x3 矩阵... | github_jupyter |
# Getting started with machine learning <br> using scikit-learn
## James Bourbeau
### Big Data Madison Meetup
April 24, 2018
### GitHub repo with materials:
https://github.com/jrbourbeau/big-data-madison-ml-sklearn <br>
### Slides:
https://jrbourbeau.github.io/big-data-madison-ml-sklearn
### Contact:
E-mail: james... | github_jupyter |
# Generating 3D People in Scenes without People
Here we give a frontend demo of how to generate body meshes in a scene without people.
+ First, we use a pre-trained conditional VAE model to generate body meshes. Here we only show the one-stage model without scene loss.
+ Second, we perform scene geometry-aware fitti... | github_jupyter |
# 02 - XOR Modell mit TensorFlow
```
# see https://aimatters.wordpress.com/2016/01/16/solving-xor-with-a-neural-network-in-tensorflow/
import tensorflow as tf
import time
```
#### Trainings- und Testdaten
```
XOR_X = [[0,0],[0,1],[1,0],[1,1]]
XOR_Y = [[0],[1],[1],[0]]
```
#### Weight und Bias definieren
```
x_ = ... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
from datetime import datetime,timedelta,date,time
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.cluster import KMeans
from collections import Counter
column_names = ['DateTime', 'y']
area = 'C:/Users/home/Desktop/Smart-Meter/ratiodata/'
dir_path = o... | github_jupyter |
```
import json
import uuid
from pymongo import MongoClient
db_dict = {
"id": "evmirna",
"title": "EVmiRNA",
"url": "http://bioinfo.life.hust.edu.cn/EVmiRNA/",
"description": "EVmiRNA is a database of miRNA profiling in extracellular vesicles",
"basicInfo": "Extracellular vesicles (EVs) released by ... | github_jupyter |
# Checkpoints
Sometimes, it might be useful to store some checkpoints while executing an algorithm. In particular, if a run is very time-consuming.
**pymoo** offers to resume a run by serializing the algorithm object and loading it. Resuming runs from checkpoints is possible
- the functional way by calling the `min... | github_jupyter |
# Introduction
This notebook demostrates the prediction pipeline for the trained classifiers. With the 3 pretrained classifiers, you can easily classify a new structure that is not included in the original training set.
**Note**:
- For easier readability, you can change the fontsize of this notebook by navigating to... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# 05. Train in Spark
* Create Workspace
* Create Experiment
* Copy relevant files to the script folder
* Configure and Run
## Prerequisites
Make sure you go through the [00. Installation and Configuration](00.configuration.ipyn... | github_jupyter |
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAADhCAYAAAC+/w30AAAABmJLR0QA/wD/AP+gvaeTAAAACXBIWXMAAC4jAAAuIwF4pT92AAAAB3RJTUUH4wEeDgYF/Qy0kwAAIABJREFUeNrsnXl4G9W5/z8zWrxNHDt29j0kBEgQMYQdwtqW0CKgtHS5XShtb1vfbrSltOpduro/StfbW3VvaaG0QEtApQ1Q9n1JIlDCEkL23fES2+NFsjTz++MckUHYia0ZW5J9vs+jR7JkHc3MOXPe77uDgoKCgoKC... | github_jupyter |
##### Copyright 2020 The TensorFlow IO 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 ... | github_jupyter |
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