code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
%run ../setup/nb_setup
%matplotlib inline
```
# Compute a Galactic orbit for a star using Gaia data
Author(s): Adrian Price-Whelan
## Learning goals
In this tutorial, we will retrieve the sky coordinates, astrometry, and radial velocity for a star — [Kepler-444](https://en.wikipedia.org/wiki/Kepler-444) — and ... | github_jupyter |
```
import os
import tarfile
import urllib
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
HOUSING_PATH = os.path.join("datasets","housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"
def fetch_housing_data(housing_url = HOUSING_URL,housing_path = HOUSING_PATH):
os.m... | github_jupyter |
# Image Denoising with Autoencoders
## Introduction and Importing Libraries
___
Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All
___
```
import numpy as np
from tensorflow.keras.data... | github_jupyter |
# Lab 04 : Train vanilla neural network -- solution
# Training a one-layer net on FASHION-MNIST
```
# For Google Colaboratory
import sys, os
if 'google.colab' in sys.modules:
from google.colab import drive
drive.mount('/content/gdrive')
file_name = 'train_vanilla_nn_solution.ipynb'
import subprocess... | github_jupyter |
<a href="https://colab.research.google.com/github/TerradasExatas/IA_e_Machine_Learning/blob/main/IA_ConvNN_classificacao_MNIST.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#https://machinelearningmastery.com/how-to-develop-a-convolutional-neu... | github_jupyter |
```
from pandas import read_csv
import cv2
import glob
import os
import numpy as np
import logging
import coloredlogs
logger = logging.getLogger(__name__)
coloredlogs.install(level='DEBUG')
coloredlogs.install(level='DEBUG', logger=logger)
IM_EXTENSIONS = ['png', 'jpg', 'jpeg', 'bmp']
def read_img(img_path, img_shape=(... | github_jupyter |
<font color = "mediumblue">Note: Notebook was updated July 2, 2019 with bug fixes.</font>
#### If you were working on the older version:
* Please click on the "Coursera" icon in the top right to open up the folder directory.
* Navigate to the folder: Week 3/ Planar data classification with one hidden layer. You ca... | github_jupyter |
<a href="https://cognitiveclass.ai/">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/CCLog.png" width="200" align="center">
</a>
<h1>2D <code>Numpy</code> in Python</h1>
<p><strong>Welcome!</strong> This notebook will teach you about using <code>Numpy</code>... | github_jupyter |
```
import scipy.special as sps
import pyro
import pyro.distributions as dist
import torch
from torch.distributions import constraints
from pyro.infer import MCMC, NUTS
from scipy.stats import norm
from torch import nn
from pyro.infer.autoguide import AutoDiagonalNormal
from pyro.nn import PyroModule
from pyro import ... | github_jupyter |
```
import pdfkit
from string import Template
import numpy as np
from PyPDF2 import PdfFileReader, PdfFileMerger
import glob
temp_address = r'C:\Users\Ol\Documents\EXPERIMENTS\ACT_ID\Materials\projectsListTemp_pl.html'
# Import content
with open(r'C:\Users\Ol\Documents\EXPERIMENTS\ACT_ID\Materials\progs_.txt', 'r', en... | github_jupyter |
```
%matplotlib inline
```
# About Dipoles in MEG and EEG
For an explanation of what is going on in the demo and background information
about magentoencephalography (MEG) and electroencephalography (EEG) in
general, let's walk through some code. To execute this code, you'll need
to have a working version of python ... | github_jupyter |
# Segmentation
This notebook shows how to use Stardist (Object Detection with Star-convex Shapes) as a part of a segmentation-classification-tracking analysis pipeline.
The sections of this notebook are as follows:
1. Load images
2. Load model of choice and segment an initial image to test Stardist parameters
3. B... | github_jupyter |
# Introduction to Language Processing Concepts
### Original tutorial by Brain Lehman, with updates by Fiona Pigott
The goal of this tutorial is to introduce a few basical vocabularies, ideas, and Python libraries for thinking about topic modeling, in order to make sure that we have a good set of vocabulary to talk mor... | github_jupyter |
<a href="https://colab.research.google.com/github/saanaz379/user-comment-classifier/blob/main/solution_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
This is the first of three solutions to identify offensive language or hate speech in a set of u... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D3_ModelFitting/W1D3_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 3, Tutorial 3
# Model Fittin... | github_jupyter |
## Dependencies
```
import warnings, math, json, glob
import pandas as pd
import tensorflow.keras.layers as L
import tensorflow.keras.backend as K
from tensorflow.keras import Model
from transformers import TFAutoModelForSequenceClassification, TFAutoModel, AutoTokenizer
from commonlit_scripts import *
seed = 0
seed... | github_jupyter |
# Load raw data
```
import numpy as np
data = np.loadtxt('SlowSteps1.csv', delimiter = ',') # load the raw data, change the filename as required!
```
# Find spikes
```
time_s = (data[:,8]-data[0,8])/1000000 # set the timing array to seconds and subtract 1st entry to zero it
n_spikes = 0
spike_times = [] # in seconds... | 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.
By the end of this notebook, you'll be able to:
* Explain t... | github_jupyter |
# Choosing the number of segments - Elbow chart method
This document illustrates how to decide the number of segments (optimal $k$) using elbow charts.
## Introducing elbow chart method
**When we should (not) add more clusters**: Ideally, the lower the $SSE$ is, the better is the clustering. Although adding more cl... | github_jupyter |
# DIMAML for Autoencoder models
Training is on Celeba. Evaluation is on Tiny ImageNet
```
%load_ext autoreload
%autoreload 2
%env CUDA_VISIBLE_DEVICES=0
import os, sys, time
sys.path.insert(0, '..')
import lib
import math
import numpy as np
from copy import deepcopy
import torch, torch.nn as nn
import torch.nn.funct... | github_jupyter |
# Autonomous Driving - Car Detection
Welcome to the Week 3 programming assignment! In this notebook, you'll implement object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: [Redmon et al., 2016](https://arxiv.org/abs/1506.02640) and [Redmon and Far... | 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 |
```
# Header starts here.
from sympy.physics.units import *
from sympy import *
# Rounding:
import decimal
from decimal import Decimal as DX
from copy import deepcopy
def iso_round(obj, pv, rounding=decimal.ROUND_HALF_EVEN):
import sympy
"""
Rounding acc. to DIN EN ISO 80000-1:2013-08
place value = Run... | github_jupyter |
# Introduction to programming for Geoscientists through Python
### [Gerard Gorman](http://www.imperial.ac.uk/people/g.gorman), [Nicolas Barral](http://www.imperial.ac.uk/people/n.barral)
# Lecture 6: Files, strings, and dictionaries
Learning objectives: You will learn how to:
* Read data in from a file
* Parse strin... | github_jupyter |
```
#@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 agreed to in writing, software
# distributed u... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
# Remote Sensing Hands-On Lesson, using TGO
EPSC Conference, Berlin, September 18, 2018
## Overview
In this lesson you will develop a series of simple programs that
demonstrate the usage of SpiceyPy to compute a variety of different
geometric quantities applicable to experiments carried out by a r... | github_jupyter |
```
import numpy
from context import vaeqst
import numpy
from context import base
base.RandomCliffordGate(0,1)
```
# Random Clifford Circuit
## RandomCliffordGate
`RandomClifordGate(*qubits)` represents a random Clifford gate acting on a set of qubits. There is no further parameter to specify, as it is not any parti... | github_jupyter |
<div style="text-align:center;">
<img alt="" <img src="./images/logo_default.png"/><br/>
</div>
<h2 style="color: #6b4e3d;">Amicable numbers</h2>
<div id="problem_info" style="font-family: Consolas;"><h3>Problem 21</h3></div>
<div class="problem_content" role="problem" style='background-color: #fff; color: #111; paddi... | github_jupyter |
<font size=6>
<b>Curso de Programación en Python</b>
</font>
<font size=4>
Curso de formación interna, CIEMAT. <br/>
Madrid, Octubre de 2021
Antonio Delgado Peris
</font>
https://github.com/andelpe/curso-intro-python/
<br/>
# Tema 9 - El ecosistema Python: librería estándar y otros paquetes populares
## Obj... | github_jupyter |
# Acknowledgement
**Origine:** This notebook is downloaded at https://github.com/justmarkham/scikit-learn-videos.
Some modifications are done.
## Agenda
1. K-nearest neighbors (KNN) classification
2. Logistic Regression
3. Review of model evaluation
4. Classification accuracy
5. Confusion matrix
6. Adjusting the c... | github_jupyter |
<a href="https://colab.research.google.com/github/google/evojax/blob/main/examples/notebooks/TutorialTaskImplementation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial: Creating Tasks
## Pre-requisite
Before we start, we need to install... | github_jupyter |
# Categorical encoders
Examples of how to use the different categorical encoders using the Titanic dataset.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from feature_engine import categorical_encoders as ce
from feature_engine.missin... | github_jupyter |
```
__author__ = 'Mike Fitzpatrick <mike.fitzpatrick@noirlab.edu>, Robert Nikutta <robert.nikutta@noirlab.edu>'
__version__ = '20211130'
__datasets__ = []
__keywords__ = []
```
## How to use the Data Lab *Store Client* Service
This notebook documents how to use the Data Lab virtual storage system via the store client... | github_jupyter |
# *Bosonic statistics and the Bose-Einstein condensation*
`Doruk Efe Gökmen -- 30/08/2018 -- Ankara`
## Non-interacting ideal bosons
Non-interacting bosons is the only system in physics that can undergo a phase transition without mutual interactions between its components.
Let us enumerate the energy eigenstates of ... | github_jupyter |
# Working with 3D city models in Python
**Balázs Dukai** [*@BalazsDukai*](https://twitter.com/balazsdukai), **FOSS4G 2019**
Tweet <span style="color:blue">#CityJSON</span>
[3D geoinformation research group, TU Delft, Netherlands](https://3d.bk.tudelft.nl/)
Repo of this talk: [https://githu... | github_jupyter |
# Working With TileMatrixSets (other than WebMercator)
[](https://mybinder.org/v2/gh/developmentseed/titiler/master?filepath=docs%2Fexamples%2FWorking_with_nonWebMercatorTMS.ipynb)
TiTiler has builtin support for serving tiles in multiple Projections by using [rio-tiler]... | github_jupyter |
# 100 pandas puzzles
Inspired by [100 Numpy exerises](https://github.com/rougier/numpy-100), here are 100* short puzzles for testing your knowledge of [pandas'](http://pandas.pydata.org/) power.
Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the... | github_jupyter |
<a href="https://colab.research.google.com/github/rjrahul24/ai-with-python-series/blob/main/01.%20Getting%20Started%20with%20Python/Python_Revision_and_Statistical_Methods.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Inheritence in Python**
Ob... | github_jupyter |
```
import pandas as pd
import numpy as np
import dask.array as da
import dask.dataframe as dd
import time
import math
from netCDF4 import Dataset
import os,datetime,sys,fnmatch
import h5py
from dask.distributed import Client, LocalCluster
cluster = LocalCluster()
client = Client(cluster)
client
%%time
def read_fileli... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("white")
#x_size, y_size = 12,8
plt.rcParams.update({'font.size': 12})
df = pd.read_csv("regression_results.csv")
f = open("data\\counters_per_route.txt", encoding="utf8")
routes = []
for l in f:
if l.sta... | github_jupyter |
# Sentiment Analysis
## Using XGBoost in SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
In this example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it wo... | github_jupyter |
```
import numpy as np
numbers=np.array([4,6,9.5])
numbers
conda install -c plotly plotly
numbers2=np.array([[1,2,3],[4,5,6]])
numbers2
numbers3=np.array([[2,'d',4],[4,'g',6]])
numbers3
evens=np.array([2*i for i in range(1,11)])
evens
evens = np.array([x for x in range(2,21,2)])
evens
three = np.array([[x for x in rang... | github_jupyter |
# Continuous Control
---
In this notebook, you will learn how to use the Unity ML-Agents environment for the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.
### 1. Start the Environment
We begin by importing the ne... | github_jupyter |
```
def download(url, params={}, retries=3):
resp = None
header = {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.108 Safari/537.36"}
try:
resp = requests.get(url, params=params, headers = header)
resp.raise_for_stat... | github_jupyter |
```
%matplotlib inline
```
# Species distribution modeling
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
variables. Since we have only positiv... | github_jupyter |
```
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import matplotlib.cm as cm
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
%mat... | github_jupyter |
```
%pushd ../../
%env CUDA_VISIBLE_DEVICES=3
import json
import os
import sys
import tempfile
from tqdm.auto import tqdm
import torch
import torchvision
from torchvision import transforms
from PIL import Image
import numpy as np
torch.cuda.set_device(0)
from netdissect import setting
segopts = 'netpqc'
segmodel, se... | github_jupyter |
# Introduction to the Python language
**Note**: This notebooks is not really a ready-to-use tutorial but rather serves as a table of contents that we will fill during the short course. It might later be useful as a memo, but it clearly lacks important notes and explanations.
There are lots of tutorials that you can f... | github_jupyter |
# 14 - Introduction to Deep Learning
by [Alejandro Correa Bahnsen](albahnsen.com/)
version 0.1, May 2016
## Part of the class [Machine Learning Applied to Risk Management](https://github.com/albahnsen/ML_RiskManagement)
This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported Licen... | github_jupyter |
```
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
import tensorflow as tf
from tensorflow import keras
import warnings;
warnings.filterwarnings('ignore')
```
# Predict batches of images
```
tf.compat.v1.enable_v2_behavior... | github_jupyter |
# ML Project 6033657523 - Feedforward neural network
## Importing the libraries
```
from sklearn.metrics import mean_absolute_error
from sklearn.svm import SVR
from sklearn.model_selection import KFold, train_test_split
from math import sqrt
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squa... | github_jupyter |
# Transfer Learning
Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this not... | github_jupyter |
```
import tensorflow as tf
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input
from keras.models import Model
import numpy as np
base_model=ResNet50(weights="imagenet")
model=Model(inputs=base_model.input, outputs=base_model.g... | github_jupyter |
# Introduction to Programming
Topics for today will include:
- Mozilla Developer Network [(MDN)](https://developer.mozilla.org/en-US/)
- Python Documentation [(Official Documentation)](https://docs.python.org/3/)
- Importance of Design
- Functions
- Built in Functions
## Mozilla Developer Network [(MDN)](https://deve... | github_jupyter |
```
!conda info
```
# Variables
```
x = 2
y = '3'
print(x+int(y))
z = [1, 2, 3] #List
w = (2, 3, 4) #Tuple
import numpy as np
q = np.array([1, 2, 3]) #numpy.ndarray
type(q)
```
# Console input and output
```
MyName = input('My name is: ')
print('Hello, '+MyName)
```
# File input and output
```
fid = open('msg.t... | github_jupyter |
# PyFunc Model + Transformer Example
This notebook demonstrates how to deploy a Python function based model and a custom transformer. This type of model is useful as user would be able to define their own logic inside the model as long as it satisfy contract given in `merlin.PyFuncModel`. If the pre/post-processing st... | github_jupyter |
<a href="https://colab.research.google.com/github/mghendi/smartphonepriceclassifier/blob/main/CCI_501_ML_Project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## CCI501 - Machine Learning Project
### Name: Samuel Mwamburi Mghendi
### Admission ... | github_jupyter |
# PROYECTO CIFAR-10
## CARLOS CABAÑÓ
## 1. Librerias
Descargamos la librería para los arrays en preprocesamiento de Keras
```
from tensorflow import keras as ks
from matplotlib import pyplot as plt
import numpy as np
import time
import datetime
import random
from sklearn.preprocessing import LabelEncoder
from ten... | github_jupyter |
```
__author__ = "Jithin Pradeep"
__copyright__ = "Copyright (C) 2018 Jithin Pradeep"
__license__ = "MIT License"
__version__ = "1.0"
```
# Summary
## About the Dataset
The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.
Each image is 28 pixels in height and... | github_jupyter |
## plan_pole_transect
Visualize pole locations on Pea Island beach transect.
Profiles were extracted from SfM maps by Jenna on 31 August 2021 - Provisional Data.
#### Read in profiles
Use pandas to read profiles; pull out arrays of x, y (UTM meters, same for all profiles) and z (m NAVD88).
Calculate distance along... | github_jupyter |
```
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(style='white', color_codes=True)
dataset = pd.read_csv("/content/drive/MyDrive/Colab Notebooks/ortho_knnnb.csv")
dataset.head()
print("Dimension... | github_jupyter |
# Analysis of how mentions of a stock on WSB relates to stock prices
WallStreetBets is a popular forum on reddit known for going to the moon, apes and stonks. Jokes aside, despite all of the ridiculous bad trades, undecipherable jargon and love for memes, it's effect on the stock market is undeniable. Therefore in thi... | github_jupyter |
# NumPy arrays
Nikolay Koldunov
koldunovn@gmail.com
This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes.
================
<img height="100" src="files/numpy.png" >
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools... | github_jupyter |
<a href="https://colab.research.google.com/github/moonryul/course-v3/blob/master/MidTermPart2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Creating your own dataset from Google Images
*by: Francisco Ingham and Jeremy Howard. Inspired by [Adria... | github_jupyter |
<img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="35%" align="right" border="0"><br>
# Python for Finance (2nd ed.)
**Mastering Data-Driven Finance**
© Dr. Yves J. Hilpisch | The Python Quants GmbH
<img src="http://hilpisch.com/images/py4fi_2nd_shadow.png" width="300px" align="left">
... | github_jupyter |
## RAC/DVR step 1: diagonalize **H**($\lambda$)
```
import numpy as np
import sys
import matplotlib.pyplot as plt
%matplotlib qt5
import pandas as pd
#
# extend path by location of the dvr package
#
sys.path.append('../../Python_libs')
import dvr
import jolanta
amu_to_au=1822.888486192
au2cm=219474.63068
au2eV=27.211... | github_jupyter |
Deep Learning
=============
Assignment 4
------------
Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.
The goal of this assignment is make the neural network convol... | 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 |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import ipdb
import dan_utils
warnings.filterwarnings("ignore")
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.tsa.stattools import adfuller as ADF
from sta... | github_jupyter |
# STEP 4 - Making DRL PySC2 Agent
```
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('..')
### unfortunately, PySC2 uses Abseil, which treats python code as if its run like an app
# This does not play well with jupyter notebook
# So we will need to monkeypatch sys.argv
import sys
#sys.argv = ["python... | github_jupyter |
```
# HIDDEN
from datascience import *
from prob140 import *
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
import math
from scipy import stats
```
## MGFs, the Normal, and the CLT ##
Let $Z$ be standard normal. Then the mgf of $Z$ is given by
$$
M_Z(t) ~ = ~ e... | 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 |
# ENGR 1330 Exam 1 Sec 003/004 Fall 2020
Take Home Portion of Exam 1
<hr>
## Full name
## R#:
## HEX:
## ENGR 1330 Exam 1 Sec 003/004
## Date:
<hr>
## Question 1 (1 pts):
Run the cell below, and leave the results in your notebook (Windows users may get an error, leave the error in place)
```
#### RUN! the Cell ####
... | github_jupyter |
# 转置卷积
:label:`sec_transposed_conv`
到目前为止，我们所见到的卷积神经网络层，例如卷积层（ :numref:`sec_conv_layer`）和汇聚层（ :numref:`sec_pooling`），通常会减少下采样输入图像的空间维度（高和宽）。
然而如果输入和输出图像的空间维度相同，在以像素级分类的语义分割中将会很方便。
例如，输出像素所处的通道维可以保有输入像素在同一位置上的分类结果。
为了实现这一点，尤其是在空间维度被卷积神经网络层缩小后，我们可以使用另一种类型的卷积神经网络层，它可以增加上采样中间层特征图的空间维度。
在本节中，我们将介绍
*转置卷积*（transposed convol... | github_jupyter |
# **LSTM - Time Series Prediction**
## **Importing libraries**
```
import pandas
import matplotlib.pyplot as plt
import numpy
import math
from tqdm import tqdm
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from sklearn.preprocessing... | github_jupyter |
```
from tensorflow import keras
from tensorflow.keras import *
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.regularizers import l2#正则化L2
import tensorflow as tf
import numpy as np
import pandas as pd
normal = np.loadtxt(r'F:\张老师课题学习内容\code\数据集\试验数据(包括压力脉动和振动)\2013.9... | github_jupyter |
# The YUSAG Football Model
by Matt Robinson, matthew.robinson@yale.edu, Yale Undergraduate Sports Analytics Group
This notebook introduces the model we at the Yale Undergraduate Sports Analytics Group (YUSAG) use for our college football rankings. This specific notebook details our FBS rankings at the beginnin... | github_jupyter |
### Using fmriprep
[fmriprep](https://fmriprep.readthedocs.io/en/stable/) is a package developed by the Poldrack lab to do the minimal preprocessing of fMRI data required. It covers brain extraction, motion correction, field unwarping, and registration. It uses a combination of well-known software packages (e.g., FSL,... | github_jupyter |
Скачайте данные в формате csv, выберите из таблицы данные по России, начиная с 3 марта 2020 г. (в этот момент впервые стало больше 2 заболевших). В качестве целевой переменной возьмём число случаев заболевания (столбцы total_cases и new_cases); для упрощения обработки можно заменить в столбце new_cases все нули на един... | github_jupyter |
SVM
```
import pandas as pd
from sklearn import svm, metrics
from sklearn.model_selection import train_test_split
wesad_eda = pd.read_csv('D:\data\wesad-chest-combined-classification-eda.csv') # need to adjust a path of dataset
wesad_eda.columns
original_column_list = ['MEAN', 'MAX', 'MIN', 'RANGE', 'KURT', 'SKEW', 'M... | github_jupyter |
# Time series analysis on AWS
*Chapter 1 - Time series analysis overview*
## Initializations
---
```
!pip install --quiet tqdm kaggle tsia ruptures
```
### Imports
```
import matplotlib.colors as mpl_colors
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
import n... | github_jupyter |
# MOHID visualisation tools
```
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><inpu... | github_jupyter |
## ML Lab 3
### Neural Networks
In the following exercise class we explore how to design and train neural networks in various ways.
#### Prerequisites:
In order to follow the exercises you need to:
1. Activate your conda environment from last week via: `source activate <env-name>`
2. Install tensorflow (https://www... | github_jupyter |
# data acquisition / processing homework 2
> I pledge my Honor that I have abided by the Stevens Honor System. - Joshua Schmidt 2/27/21
## Problem 1
a. For a stationary AR(1) time series x(t), x(t) is uncorrelated to x(t-l) for l>=2.
This is false. For AR(1), $x(t) = a_0 + a_1 \cdot x(t - 1) + \epsilon_t$. In this ... | github_jupyter |
## Initiate the vissim instance
```
# COM-Server
import win32com.client as com
import igraph
import qgrid
from VISSIM_helpers import VissimRoadNet
from os.path import abspath, join, exists
import os
from shutil import copyfile
import pandas as pd
import math
from pythoncom import com_error
```
Add autocompletion for ... | github_jupyter |
## Some more on ```spaCy``` and ```pandas```
First we want to import some of the packages we need.
```
import os
import spacy
# Remember we need to initialise spaCy
nlp = spacy.load("en_core_web_sm")
```
We can inspect this object and see that it's what we've been called a ```spaCy``` object.
```
type(nlp)
```
We... | github_jupyter |
# #05 - Exploring Utils
Falar sobre para se trabalhar com trajetórias pode ser necessária algumas c onversões envolvendo tempo e data, distância e etc, fora outros utilitários.
Falar dos módulos presentes no pacote utils
- constants
- conversions
- datetime
- distances
- math
- mem
- trajectories
- transformations
... | github_jupyter |
# Initial Modelling notebook
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
import bay12_solution_eposts as solution
```
## Load data
```
post, thread = solution.prepare.load_dfs('train')
post.head(2)
thread.head(2)
```
I will set the thread... | github_jupyter |
```
import numpy as np
import pandas as pd
import pydicom
import os
import random
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold
import warnings
warnings.filterwarnings("ignore")
import torch.nn as n... | github_jupyter |
```
# Import Dependencies
import os
import csv
# Establish filepath
budget_csv = os.path.join(".", "resources", "budget_data.csv")
output_file = os.path.join(".", "financial_analysis.txt")
# Index Reference for the Profit and Loss List
# Track Financial Parameters
# Open and read csv file
with open(budget_csv, newlin... | github_jupyter |
```
import numpy as np
import pandas as pd
```
# Pandas Metodları ve Özellikleri
### Veri Analizi için Önemli Konular
#### Eksik Veriler (Missing Value)
```
data = {'Istanbul':[30,29,np.nan],'Ankara':[20,np.nan,25],'Izmir':[40,39,38],'Antalya':[40,np.nan,np.nan]}
weather = pd.DataFrame(data,index=['pzt','sali','car... | github_jupyter |
<a href="https://colab.research.google.com/github/keivanipchihagh/Intro_To_MachineLearning/blob/master/Models/Newswires_Classification_with_Reuters.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Newswires Classification with Reuters
##### Import... | github_jupyter |
# Interpreting text models: IMDB sentiment analysis
This notebook loads pretrained CNN model for sentiment analysis on IMDB dataset. It makes predictions on test samples and interprets those predictions using integrated gradients method.
The model was trained using an open source sentiment analysis tutorials describ... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection... | github_jupyter |
# autotimeseries
> Nixtla SDK. Time Series Forecasting pipeline at scale.
[](https://github.com/Nixtla/nixtla/actions/workflows/python-sdk.yml)
[](https://pypi.o... | github_jupyter |
```
#!/usr/bin/env python
# encoding: utf-8
"""
@Author: yangwenhao
@Contact: 874681044@qq.com
@Software: PyCharm
@File: cam_2.py
@Time: 2021/4/12 21:47
@Overview:
Created on 2019/8/4 上午9:37
@author: mick.yi
"""
import os
import pdb
import numpy as np
import torch
from torch.nn.parallel.distributed import Di... | github_jupyter |
# The Structure and Geometry of the Human Brain
[Noah C. Benson](https://nben.net/) <[nben@uw.edu](mailto:nben@uw.edu)>
[eScience Institute](https://escience.washingtonn.edu/)
[University of Washington](https://www.washington.edu/)
[Seattle, WA 98195](https://seattle.gov/)
## Introduction
This notebook i... | github_jupyter |
# Baye's Theorem
### Introduction
Befor starting with *Bayes Theorem* we can have a look at some definitions.
**Conditional Probability :**
Conditional Probability is the Probability of one event occuring with some Relationship to one or more events.
Let A and B be the two interdependent event,where A has already oc... | github_jupyter |
[learning-python3.ipynb]: https://gist.githubusercontent.com/kenjyco/69eeb503125035f21a9d/raw/learning-python3.ipynb
Right-click -> "save link as" [https://gist.githubusercontent.com/kenjyco/69eeb503125035f21a9d/raw/learning-python3.ipynb][learning-python3.ipynb] to get most up-to-date version of this notebook file.
... | github_jupyter |
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