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# Applied Machine ... | github_jupyter |
# CME 193 - Lecture 5 - Pandas
Before we get started, you may want to make sure that you have the following packages installed in whatever environment you're using: `pandas`
```bash
conda install pandas
```
Pandas is a package for working with tabular data.
We'll also cover dictionaries and lambda functions today... | github_jupyter |
```
import spacy
from spacy import displacy
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
nltk.download('wordnet')
from nltk.corpus import stopwords
import re
from nltk.stem import PorterStemmer
nltk.download('stopword... | github_jupyter |
<a href="https://colab.research.google.com/github/SLCFLAB/Data-Science-Python/blob/main/Day%202/2_1.%20numpy%26pandas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Numpy & Pandas basic
### Reference
https://numpy.org/doc/stable/reference/routin... | github_jupyter |
REF_top-10-0-10943-stacking-mice-and-brutal-force
ๅ่:
- https://www.kaggle.com/agehsbarg/top-10-0-10943-stacking-mice-and-brutal-force
## Import PKGs
```
import os
import time
import numpy as np
import pandas as pd
import datetime
import random
import matplotlib.pyplot as plt
from sklearn.model_selection import G... | github_jupyter |
Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad.
Create a convolutional neural network that trains to 100% accuracy on these images, which cancels training upon hitting training accuracy of >.999
Hint -- it will work best with 3 convolutional layers.
```
import tens... | github_jupyter |
# Intuition for the Maximum Mean Discrepancy two-sample test
_Thomas Viehmann_
This note sketches the intuition behind [A. Gretton et al.: A Kernel Two-Sample Test. JMLR 2012](http://www.gatsby.ucl.ac.uk/~gretton/mmd/mmd.htm).
Given a (high-dimensional) space $\mathbb{R}^d$ and iid samples $X_i \in \mathbb{R}^d, i=1... | github_jupyter |
# [Angle closure Glaucoma Evaluation Challenge](https://age.grand-challenge.org/Details/)
## Scleral spur localization Baseline ๏ผRCNN)
- To keep model training stable, images with coordinate == -1, were removed.
- For real inference, you MIGHT keep all images in val_file_path file.
## requirement install
```
!pip in... | github_jupyter |
## 13) More NumPy Plus Linear Algebra Fundamentals
Related references:
- https://jakevdp.github.io/PythonDataScienceHandbook/02.04-computation-on-arrays-aggregates.html
- https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html
- [Feature Engineering for Machine Learning](http... | github_jupyter |
# NNabla Models Finetuning Tutorial
Here we demonstrate how to perform finetuning using nnabla's pre-trained models.
## Load the model
Loading the model is very simple. All you need is just 2 lines.
```
from nnabla.models.imagenet import ResNet18
model = ResNet18()
```
You can choose other ResNet models such as `... | github_jupyter |
<small><i>June 2016 - This notebook was created by [Oriol Pujol Vila](http://www.maia.ub.es/~oriol). Source and [license](./LICENSE.txt) info are in the folder.</i></small>
# Backpropagation
```
#pip install tqdm
```
## Basic scheme
Consider the problem up to this point. Let us recall the three basic components of ... | github_jupyter |
# Thompson Sampling for Linearly Constrained Bandits
## Plots for Regret and Violation
```
import numpy as np
from matplotlib import pyplot as plt
```
# Load Data
```
results_dir = 'results/'
filename = 'edX_eta0.50_T50000_N16'
#filename = 'coupon_purchase_eta0.25_T10000_N16'
file_ext = '.npy'
#data = np.... | github_jupyter |
# [Titanic Data Set](https://www.kaggle.com/c/titanic/data)
<img src="../images/titanic.jpeg">
### Data Set Information:
The titanic data frame describes the survival status of individual passengers on the Titanic.
The titanic data frame does not contain information for the crew, but it does contain actual and estim... | github_jupyter |
# Getting started with Azure ML Data Prep SDK
Copyright (c) Microsoft Corporation. All rights reserved.<br>
Licensed under the MIT License.
Wonder how you can make the most of the Azure ML Data Prep SDK? In this "Getting Started" guide, we'll demonstrate how to do your normal data wrangling with this SDK and showcase... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
from importlib import reload
import IPython
mpl.rcParams['lines.linewidth'] = 0.25
mpl.rcParams['axes.spines.top'] = False
mpl.rcParams['axes.spines.right'] = False
mpl.rcParams['axes.linewidt... | github_jupyter |
```
%%html
<style>
div.output_stderr{
display:none
}
</style>
<a id='top'></a>
```
# Operation of parmeter based functions
* Documentation for *.yml and run_parameters funtions in ../src/mini_pipelines_toolbox.py.
### source code link:
##### (private) source repository: https://github.com/dlanier/minipipelines.g... | github_jupyter |
#Convective Cell Identification & TRAcking (CITRA) using Doppler Weather Radar Images
The cell below installs the Tesseract-OCR model and the Google Drive Mount sequence.
**NOTE**: - When the below cell in run, it pops up a link to request access to your google drive. Open that link and grant acces. Then copy the acc... | github_jupyter |
# Morphing basis animations
Let's make something cool:
```
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
%matplotlib inline
import sys
try:
from madminer.morphing import Morpher
exce... | github_jupyter |
```
import wave
import struct
import os
from scipy import signal
import numpy as np
import tensorflow as tf
# from tensorflow.python.ops import variable_scope as vs
tf.reset_default_graph()
path = r'C:\Users\xujiahao\Desktop\MIR-1K_for_MIREX\trainwav' #ๆไปถๅคน็ฎๅฝ
fname1 = 'left.wav'
fname2 = 'right.wav'
nframes = 9600... | 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 os
import numpy as np
import pandas as pd
path = "../data/partial_files/"
elements_list = ["players_info", "match_info", "players_lanes", "player_laning_stats",
"player_flair_stats", "champion_bans", "champion_picks",
"player_combat_stats", "player_objective_stats", "players... | github_jupyter |
# Bouts of Sleep from a month-long recording of WT C57BL/6 mice
### First set up the working environment
```
import numpy as np # calculations
import pandas as pd # dataframes and IO
import matplotlib.pyplot as plt # plotting
# show graphs/figures in notebooks
%matplotlib inline
import seaborn as sns # statistic... | github_jupyter |
# Proof of concept of new "composable" ADMM formulation
3/30/21
This notebook is a proof of concept and understanding of the new ADMM formulation, based on grouping quadratic terms and linear constraints in with the global equality constraint.
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotl... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_parent" href="https://github.com/giswqs/geemap/tree/master/tutorials/FeatureCollection/us_census_data.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_parent" h... | github_jupyter |
# Pytorch Tutorial
### 4. Saving and Loading Models and Their States
- Saving and loading model parameters
- Using ```torchvision.models```
Setup torch and torchvision.
```
import torch, torchvision
import torch.nn as nn
import torch.optim as optim
```
## Using ```torchvision.models```
Models provided by ```torch... | github_jupyter |
# Get started with the Estimator primitive
Learn how to set up and use the Estimator primitive program.
## Overview
The Estimator primitive lets you efficiently calculate and interpret expectation values of quantum operators required for many algorithms. You can specify a list of circuits and observables, then eval... | github_jupyter |
# Titanic Survival Prediction
1. [Import Libraries](#heading1)<br>
2. [Read Data](#heading2)<br>
3. [Data Cleaning & Feature Engineering](#heading3)<br>
4. [Exploratory Data Analysis](#heading4)<br>
5. [Model Building & Evaluation](#heading5)<br>
5.1 [Logistic Regression](#subheading1)<br>
5.2 [Gaussian Naive Baye... | github_jupyter |
## 0.Import Packages
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import os
import glob
import seaborn as sns
```
## 1. Load Dataset
```
dir = 'refined_dataset'
listdir = os.listdir(dir)
print(listdir)
print("The number of dataset :", len(listdir))
... | github_jupyter |
```
# !wget https://f000.backblazeb2.com/file/malay-dataset/knowledge-graph/kelm/train_X
# !wget https://f000.backblazeb2.com/file/malay-dataset/knowledge-graph/kelm/train_Y
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'mesolitica-tpu.json'
from tqdm import tqdm
import re
def cleaning(string):
string ... | github_jupyter |
# Experiment Collection #03
This notebook contains experiments regarding the use of a penalty term and enabling charging from the grid. These experiments are with the stochastic environment.
## 1. Basic Setup
```
# Jupyter setup
%load_ext autoreload
%autoreload 2
%config IPCompleter.greedy=True
import ray
ray.shutdo... | github_jupyter |
# Neural Machine Translation
Welcome to your first programming assignment for this week!
* You will build a Neural Machine Translation (NMT) model to translate human-readable dates ("25th of June, 2009") into machine-readable dates ("2009-06-25").
* You will do this using an attention model, one of the most sophist... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:
corpus = fopen.read().split('\n')
with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:
corpus.extend(fopen.read().split('\n'))
with open('../Malaya-D... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/5.1_Text_classification_examples_in_SparkML_S... | github_jupyter |
<a href="https://colab.research.google.com/github/satyajitghana/TSAI-DeepNLP-END2.0/blob/main/05_NLP_Augment/SSTModel.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! nvidia-smi
! pip install pytorch-lightning --quiet
! pip install OmegaConf --... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import numpy as np
import tensorflow as tf
import json
with open('dataset-bpe.json') as fopen:
data = json.load(fopen)
train_X = data['train_X']
train_Y = data['train_Y']
test_X = data['test_X']
test_Y = data['test_Y']
EOS = 2
GO = 1
vocab_size = 32000
train_Y ... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
from tqdm import tnrange, tqdm_notebook, tqdm
import skorch
import torchvision.datasets as dset
import torchvision.transforms as T
import torch... | github_jupyter |
KEGG
====
KEGG (<http://www.kegg.jp/>) is a database resource for understanding
high-level functions and utilities of the biological system, such as the
cell, the organism and the ecosystem, from molecular-level information,
especially large-scale molecular datasets generated by genome sequencing
and other high-throug... | github_jupyter |
Copyright 2020 DeepMind Technologies Limited.
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](https://www.apache.org/licenses/LICENSE-2.0)
Unless requ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automa... | github_jupyter |
```
%pylab inline
import numpy as np
from scipy.integrate import odeint
import itertools
from Oracle_Training import *
import json
from SparseARD import*
np.random.seed(0)
retrain = False
noise_percent = 0.1
n_trials = 10
n_sample = 2500
tol = 1e-8 # tolerance for ARD algorithm
verbose = True
# Translated into Pytho... | github_jupyter |
# SYMPAIS Torus Demo
[](https://colab.research.google.com/github/ethanluoyc/sympais/blob/master/notebooks/torus_demo.ipynb)
This notebook provides a visual illustration of the SYMPAIS algorithm.
## Setup
```
try:
import google.colab
IN_COL... | 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/introduction).**
---
As a warm-up, you'll review some machine learning fundamentals and submit you... | github_jupyter |
```
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import roc_curve, auc
import pandas as pd
import time
from scipy import interp
from sklearn.preprocessing import FunctionTransfor... | github_jupyter |
## Integraciรณn y procesamiento de los datos
Primeramente importaremos todas las librerรญas que vamos a necesitar para el procesamiento de los datos, pandas para el manejo de data frames, matplotlib para generar las grรกficas, scipy para crear clusteres herarquicos y sklearn para hacer clusteres
Luego importamos los dat... | github_jupyter |
```
#loading libraries
import pandas as pd
import string
import seaborn as sns
import nltk
from nltk import word_tokenize
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
import xgboost as xgb
from sklearn.ensemble import ... | github_jupyter |
# Training Unet & Attention Unet
## Dependencies
Install, load, and initialize all required dependencies for this experiment.
### Install Dependencies
```
import sys
!{sys.executable} -m pip install -q -e ../../utils/
```
### Import Dependencies
# System libraries
```
from __future__ import absolute_import, divis... | github_jupyter |
# Quick Start
Below is a simple demo of interaction with the environment of the VM scheduling scenario.
```
from maro.simulator import Env
from maro.simulator.scenarios.vm_scheduling import AllocateAction, DecisionPayload
env = Env(scenario="vm_scheduling", topology="azure.2019.10k", start_tick=0, durations=8638, sn... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import higlass
import higlass.tilesets
from higlass.client import Track, View
```
## Synced heatmaps
```
from higlass.client import View, Track
import higlass
t1 = Track(track_type='top-axis', position='top')
t2 = Track(track_type='heatmap', position='center',
tileset... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Find the single different char between two strings.
* [Constraints](#Constraints)
* [Test Cases](#Test-Ca... | github_jupyter |
Similar to fiducial drift correction, 3D imaging based on astigmatism is implemented in B-Store in separate parts:
1. the `CalibrateAstigmatism` processor that is used to launch the interactive calibration, and
2. a `ComputeTrajectories` class that describes the algorithm for fitting smoothed curves to the beads' x- a... | github_jupyter |
In this notebook, we'll learn how to use GANs to do semi-supervised learning.
In supervised learning, we have a training set of inputs $x$ and class labels $y$. We train a model that takes $x$ as input and gives $y$ as output.
In semi-supervised learning, our goal is still to train a model that takes $x$ as input and... | github_jupyter |
```
project_id = 'elife-data-pipeline'
source_dataset = 'de_dev'
output_dataset = 'de_dev'
output_table_prefix = 'data_science_'
mv_prefix = 'mv_'
max_workers = 10
max_editors = 100
email = 'd.ecer@elifesciences.org'
import logging
from datetime import datetime
from functools import partial
from concurrent.futures imp... | github_jupyter |
```
%matplotlib inline
```
# Training a Classifier
This is it. You have seen how to define neural networks, compute loss and make
updates to the weights of the network.
Now you might be thinking,
## What about data?
Generally, when you have to deal with image, text, audio or video data,
you can use standard pytho... | github_jupyter |
# Known issues
## A float quantity is Iterable
https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterable
This tests if the object has "__iter__"
```
import collections
from physipy import m
isinstance(m, collections.abc.Iterable)
```
## Array repr with 0 value
Pick best favunit take the smalle... | github_jupyter |
```
from las import LASReader
import numpy as np
import pandas as pd
from scipy import signal
import matplotlib.pyplot as plt
file = r'./data/7120_1_3.las'
def loadLog(file):
"""
# Import sonic log into Numpy.
"""
log = LASReader(file, null_subs=np.nan)
return log
def npSonic(file):
"""
... | github_jupyter |
# Spark streaming basics project
_____
### Note on Streaming
Streaming is something that is rapidly advancing and changing fast, there are multiple new libraries every year, new and different services always popping up, and what is in this notebook may or may not apply to you. Maybe your looking for something specifi... | github_jupyter |
# Title: Alert Investigation (Windows Process Alerts)
**Notebook Version:** 1.0<br>
**Python Version:** Python 3.6 (including Python 3.6 - AzureML)<br>
**Required Packages**: kqlmagic, msticpy, pandas, numpy, matplotlib, networkx, ipywidgets, ipython, scikit_learn<br>
**Platforms Supported**:<br>
- Azure Notebooks Fre... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import laser.fresnel_propag as prop
from laser.misc import gauss2D
import laser.zernike as zern
```
# Example 1: Propagation through an optical setup with a hole
## Laser parameters
```
lam = 8e-7 # Wavelength (in m)
k = 2*np.pi/lam # Wave vector
fwhm = 0.07 # I... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 4: Training a Neural Network**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information visi... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
```
# Copyright 2020 NVIDIA. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%env CUDA_VISIBLE_DEVICES=2
import numpy as np
import pandas as pd
import os
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import umap
from firelight.visualizers.colorization import get_distinct_colors
from matplotlib.colors import ListedColormap
import pic... | github_jupyter |
# CROP Arima model
This notebook checks outputs of the Arima model
```
#!pip3 install psycopg2
#!pip3 install plotly
import os
from datetime import datetime, timedelta
import psycopg2
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matp... | github_jupyter |
# The Egg data object
This tutorial will go over the basics of the `Egg` data object, the essential quail data structure that contains all the data you need to run analyses and plot the results. An egg is made up of two primary pieces of data:
1. `pres` data - words/stimuli that were presented to a subject
2. `rec`... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import glob
np.random.seed(42)
# translate SNANA types
types_names = {90:'Ia', 67: '91bg', 52:'Iax', 42:'II', 62:'Ibc',
95: 'SLSN', 15:'TDE', 64:'KN', 88:'AGN', 92:'RRL', 65:'M-dwarf',
16:'EB',53:'Mira', 6:'MicroL', 991:'MicroLB', 992:... | github_jupyter |
# Graphillionใซ่งฆใใฆใฟใใ
ใใใใGraphillionใฎ่งฃ่ชฌใซๅ
ฅใใพใ๏ผใพใใฏใฏใใใซๆฐใไธใใๅงใใๅ้กใ็ดนไปใ๏ผใใใGraphillionใไฝฟใฃใฆใฉใฎใใใซ่งฃใใใๅ
ทไฝ็ใชใณใผใใไบคใใฆ่งฃ่ชฌใใพใ๏ผGraphillionใฎๆฉ่ฝใฎ่ฉณ็ดฐใใใณๅ
้จใงใฉใฎใใใชๅฆ็ใ่ตฐใฃใฆใใใฎใใซใคใใฆใฏๆฌก็ซ ไปฅ้ใง่งฃ่ชฌใใพใ๏ผ
## ๆฐใไธใใๅงใใๅ้ก
ใพใใฏไปฅไธใฎๅ็ปใๅพก่ฆงใใ ใใ๏ผ
```
from IPython.display import YouTubeVideo
YouTubeVideo("Q4gTV4r0zRs")
```
ใใฎๅ็ปใงๅใไธใใฆใใๅ้กใ**ๆฐใไธใใๅงใใๅ้ก**ใจใใถใใจใซใใพใ๏ผๅ... | github_jupyter |
```
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.font_manager
from matplotlib.patches import Rectangle, PathPatch
from matplotlib.textpath import TextPath
import matplotlib.transforms as mtrans
%matplotlib inline
MPL_BLUE = '#11557c'
ziti = mpl... | github_jupyter |
# Example for computing a price serie's spectrogram
```
# Put these at the top of every notebook, to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from datetime import datetime
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
pd.set_optio... | github_jupyter |
# inference only demo
We're done! We have a working pair of models which produce meaninful shared embeddings for text and images, which we can use to run image searches without relying on detailed metadata. The only thing to do now is ensure that the search process is fast enough to be practical, and lay out all of the... | github_jupyter |
<span style="color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;">An Exception was encountered at '<a href="#papermill-error-cell">In [8]</a>'.</span>
```
YEAR = "2020"
BASE_DIR = "."
# Parameters
id = None
YEAR = "2302"
BASE_DIR = "/Users/cfe/Dev/jupyter-api/src"
DATA_DIR = "/Users/cfe... | github_jupyter |
# 20 Newsgroups text classification with pre-trained word embeddings
In this notebook, we'll use pre-trained [GloVe word embeddings](http://nlp.stanford.edu/projects/glove/) for text classification using PyTorch. Tokenization and word-to-id mapping is done using [gensim](https://radimrehurek.com/gensim/index.html). Th... | github_jupyter |
# Striplog expert functions
This notebooks looks at the main `striplog` object. For the basic objects it depends on, see [Basic objects](./Basic_objects.ipynb).
First, import anything we might need.
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import striplog
striplog.__version__
from ... | github_jupyter |
# Human Protien Atlas Data Processing
Here we are pulling data in and processing it.
* [Here](http://www.sciencemag.org/content/347/6220/1260419.full) is the paper by Uhlen et al. on the dataset
* The data was obtained from [proteinatlas.org](http://www.proteinatlas.org/)
```
%matplotlib inline
import pandas as pd
... | github_jupyter |
```
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
```
## Exercise 1
- load the dataset: `../data/international-airline-passengers.csv`
- inspect it using the `.info()` and `.head()` commands
- use the function `pd.to_datetime()` to change the column type of 'Month' to a da... | github_jupyter |
# Merge
Combine data files into a CSV that's ready for analysis
```
import pandas as pd
```
Import data files
```
deaths_df = pd.read_csv(
"../input/processed/death-records.csv",
parse_dates=["date_of_death", "date_of_birth"],
dtype={
"last_name": str,
"first_name": str,
"middle_... | github_jupyter |
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
## Model Training Script for Synapse-AI-Retail-Recommender
Model Author (Data Scientist): Xiaoyong Zhu
This script is an adapted script of the full Model Training script that can be found in `4. ML Model Building`. This is a slimmed down vers... | github_jupyter |
<a href="https://colab.research.google.com/github/hatimnaitlho/ml-sklearn/blob/master/ExtraTreeClassifier_for_breast_cancer_diagnosis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Extra Tree Classifier for Breast Cancer Diagnosis
In this notebo... | github_jupyter |
```
%pylab inline
import phreeqpython
import pandas as pd
pp = phreeqpython.PhreeqPython(database='phreeqc.dat')
```
## Oxygen
```
pressure_range = np.linspace(0.01, 100, 100)
o2 = []
for p in pressure_range:
sol = pp.add_solution({'temp':27})
gas = pp.add_gas({'O2(g)':p}, pressure=p, fixed_pressure=True)
... | github_jupyter |
# Differences that modeling cause to the baseline model in i2b2 data
for reference, command that was run within scripts/ was ```CUDA_VISIBLE_DEVICES=<device_no> python main.py --<cross_validate/use_test> --dataset=i2b2 --preprocessing_type=<entity_blinding/punct_digit/punct_stop_digit> --border_size=-1 --num_epoches=1... | github_jupyter |
# ORF307 Precept 5
# Converting LPs
Convert the following LP into 2 forms
\begin{array}{ll} \mbox{min} & \|Ax - b\|_1 \\
\mbox{subject to} & \|x\|_{\infty} \leq k \\
\end{array}
form (1)
\begin{array}{ll} \mbox{min} & c^T x \\
\mbox{subject to} & Ax \leq b \\
& Cx = d \\
\end{array}
form (2)
\begin{array}{ll} \m... | github_jupyter |
**Tools - pandas**
*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available pro... | github_jupyter |
# Bayesian Temporal Matrix Factorization
**Published**: October 8, 2019
**Author**: Xinyu Chen [[**GitHub homepage**](https://github.com/xinychen)]
**Download**: This Jupyter notebook is at our GitHub repository. If you want to evaluate the code, please download the notebook from the repository of [**tensor-learning... | github_jupyter |
<a href="https://colab.research.google.com/github/ziatdinovmax/gpax/blob/v0.0.3/examples/GP_sGP.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install -q git+https://github.com/ziatdinovmax/gpax@v0.0.3
```
Imports:
```
import gpax
import... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('E:/kaggle/Benz/data/train.csv/train.csv')
```
## First, we have a glance of the our dataset
```
train.head()
train.describe()
train.info()
train[train.isnull().values == True]
```
#### This is a regr... | github_jupyter |
# Training and hosting SageMaker Models using the Apache MXNet Module API
The **SageMaker Python SDK** makes it easy to train and deploy MXNet models. In this example, we train a simple neural network using the Apache MXNet [Module API](https://mxnet.apache.org/api/python/module/module.html) and the MNIST dataset. The... | github_jupyter |
# Deep Q-learning
```
import gym
import tensorflow
from matplotlib import pyplot
import dqn
```
## Atari 2600 Breakout
```
env = gym.make('Breakout-v0')
env.action_space, env.observation_space
# env.observation_space.low, env.observation_space.high
env.env.get_action_meanings()
S = env.reset()
for t in range(250):
... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains material from [CBE40455-2020](https://jckantor.github.io/CBE40455-2020);
content is available [on Github](https://github.com/jckantor/CBE40455-2020.git).*
<!--NAVIGATION-->
< [3.3 Agent Based Models](https://jckantor.github.io/CBE40455-2020/03.03-Agent-Based-Models.html) ... | github_jupyter |
```
import sqlite3
from selenium import webdriver
from selenium.webdriver.remote.webelement import WebElement
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as ... | 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 |
# RUL estimation UNIBO Powertools Dataset
```
import numpy as np
import pandas as pd
import scipy.io
import math
import os
import ntpath
import sys
import logging
import time
import sys
import random
from importlib import reload
import plotly.graph_objects as go
import tensorflow as tf
from tensorflow import keras
f... | github_jupyter |
# Analyze Product Sentiment
```
import turicreate
import os
```
# Read product review data
```
d = os.getcwd() #Gets the current working directory
os.chdir("..")
products = turicreate.SFrame('./data/amazon_baby.sframe/m_bfaa91c17752f745.frame_idx')
```
# Explore data
```
products
products.groupby('name',operations... | github_jupyter |
# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo
```
%matplotlib inline
import pandas as pd
import numpy as np
import numpy.ma as ma
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sb
sb.set()
import pdb
np.random.seed(2009... | github_jupyter |
# Keras tutorial - the Happy House
Welcome to the first assignment of week 2. In this assignment, you will:
1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.
2. See how you c... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/landsat_surface_reflectance.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target... | github_jupyter |
# Example: CanvasXpress violin Chart No. 1
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/violin-1.html
This example is generated using the reproducible JSON obtained from the above page an... | github_jupyter |
<a href="https://colab.research.google.com/github/srimanthtenneti/Deep-Learning-NanoDegree/blob/main/Capsule_Networks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Capsule Networks
```
import torch
import torch.nn as nn
import torch.nn.function... | github_jupyter |
```
import pickle
import matplotlib.pyplot as plt
from scipy.stats.mstats import gmean
import seaborn as sns
from statistics import stdev
from math import log
import numpy as np
from scipy import stats
from statistics import mean
%matplotlib inline
price_100_stan = pickle.load(open("C:\\Users\\ymamo\\Google Drive\\1. P... | github_jupyter |
```
!git clone https://github.com/karpathy/minGPT.git
!pip install snakeviz
from fastai.text.all import *
from minGPT.mingpt.model import GPT, GPTConfig, GPT1Config
with open('/kaggle/input/lyrics-v2/lyrics.txt', encoding="utf8", errors='ignore') as f:
raw_text=f.read()
len(raw_text)
class CharTransform(DisplayedTr... | github_jupyter |
## Tutorial for building a feature vector distribution plot
In this tutorial we will build an interactive widget using bqplot and ipywidgets. bqplot is a powerful interactive plotting library for jupyter. Its main power comes from how well integrated it is into the ipywidgets library. There are a few things you should... | github_jupyter |
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