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# Notebook from Majoburo/spaxlet
Path: docs/tutorials/deconvolution.ipynb
# Deconvolution Tutorial
## Introduction
There are several problems with the standard initialization performed in the [Quickstart Guide](../0-quickstart.ipynb):
1. The models exist in a frame with a narrow model PSF while the the observed sce... | {
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# Notebook from Messicka/LSSTC-DSFP-Sessions
Path: Sessions/Session13/Day2/02-Fast-GPs.ipynb
# Fast GP implementations_____no_output_____
<code>
%matplotlib inline_____no_output_____%config InlineBackend.figure_format = 'retina'_____no_output_____from matplotlib import rcParams
rcParams["figure.dpi"] = 100
rcParams["... | {
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# Notebook from KarrLab/bcforms
Path: examples/1. Introductory tutorial.ipynb
`BcForms` is a toolkit for concretely describing the primary structure of macromolecular complexes, including non-canonical monomeric forms and intra and inter-subunit crosslinks. `BcForms` includes a textual grammar for describing complexes... | {
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# Notebook from sanchobarriga/course-content
Path: tutorials/W3D2_DynamicNetworks/W3D2_Tutorial1.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_DynamicNetworks/W3D2_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/... | {
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# Notebook from shrikumarp/ADSSpring18
Path: ADS_Project1.ipynb
<code>
import numpy as np
import pandas as pd_____no_output_____yelp = pd.read_csv('https://raw.githubusercontent.com/shrikumarp/shrikumarpp1/master/yelp.csv')_____no_output_____yelp.head()_____no_output_____yelp.info()<class 'pandas.core.frame.DataFrame'... | {
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# Notebook from stjordanis/CS7641
Path: Assignmet_4/CS7641_Assignment4_MDP_1.ipynb
<code>
%%html
<style>
body {
font-family: "Cambria", cursive, sans-serif;
}
</style> _____no_output_____import random, time
import numpy as np
from collections import defaultdict
import operator
import matplotlib.pyplot as plt_____n... | {
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# Notebook from kozo2/pyWikiPathways
Path: docs/pywikipathways-and-bridgedbpy.ipynb
# pywikipathways and bridgedbpy
[](https://colab.research.google.com/github/kozo2/pywikipathways/blob/main/docs/pywikipathways-and-bridgedbpy.ipynb)
by Kozo Nis... | {
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# Notebook from TotoArt/arnheim
Path: arnheim_1.ipynb
Copyright 2021 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
... | {
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# Notebook from jjc2718/generic-expression-patterns
Path: LV_analysis/1_get_multiplier_LV_coverage.ipynb
# Coverage of MultiPLIER LV
The goal of this notebook is to examine why genes were found to be generic. Specifically, this notebook is trying to answer the question: Are generic genes found in more multiplier late... | {
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# Notebook from PythonOT/pythonot.github.io
Path: master/_downloads/cc2d1e2d3ec6e3b42bceea0b50c4db77/plot_wass1d_torch.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
# Wasserstein 1D with PyTorch
In this small example, we consider the following minization problem:
\begin{align}\mu^* = \min_\mu W(\mu,\n... | {
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# Notebook from letsgoexploring/econ126
Path: Lecture Notebooks/Econ126_Class_14.ipynb
<code>
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline_____no_output_____
</code>
# Class 14: Prescott's Real Business Cycle Model I
In th... | {
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# Notebook from tfburns/deep-learning-specialization
Path: notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb
# Character level language model - Dinosaurus Island
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a... | {
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# Notebook from Simonm952/simonm952.github.io
Path: App market.ipynb
## 1. Google Play Store apps and reviews
<p>Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the A... | {
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# Notebook from hemanth22/pythoncode-tutorials
Path: machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.ipynb
<code>
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from mlxtend.frequent_patterns import apriori, association_rul... | {
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# Notebook from charanhu/Amazon-Fine-Food-Reviews-Analysis.
Path: Amazon_Fine_Food_Reviews_Analysis.ipynb
<a href="https://colab.research.google.com/github/charanhu/Amazon-Fine-Food-Reviews-Analysis./blob/main/Amazon_Fine_Food_Reviews_Analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/... | {
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# Notebook from bgoodr/how-to
Path: python/jupyter/basic_jupyter_scipy_tutorial.ipynb
# Introduction _____no_output_____This is a basic tutorial on using Jupyter to use the scipy modules._____no_output_____# Example of plotting sine and cosine functions in the same plot_____no_output_____Install matplotlib through con... | {
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# Notebook from Nop287/game_db
Path: TinyDB.ipynb
<code>
import pandas as pd
import os
import json
import re
from tinydb import TinyDB, Query
import sqlalchemy as db_____no_output_____
</code>
# Building the Database
We use a database in the backend to serve the data over a REST API to our client. The database is be... | {
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"path": "TinyDB.ipynb",
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# Notebook from matteoferla/pyrosetta_scripts
Path: colabs/colabs-pyrosetta.ipynb
<code>
#@title blank template
#@markdown This notebook from [github.com/matteoferla/pyrosetta_help](https://github.com/matteoferla/pyrosetta_help).
#@markdown It can be opened in Colabs via [https://colab.research.google.com/github/matt... | {
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# Notebook from ithakker/CISC367-Projects
Path: Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb
# Regular Expression Exercises
* Debugger: When debugging regular expressions, the best tool is [Regex101](https://regex101.com/). This is an interactive tool that let's you visualize a regular expression in acti... | {
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"path": "Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb",
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# Notebook from maximiliense/GLC19
Path: notebooks/Getting started - Baselines and submission.ipynb
This notebook presents some code to compute some basic baselines.
In particular, it shows how to:
1. Use the provided validation set
2. Compute the top-30 metric
3. Save the predictions on the test in the right format ... | {
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"path": "notebooks/Getting started - Baselines and submission.ipynb",
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# Notebook from eugenesiow/practical-ml
Path: notebooks/OCR_from_Images_with_Transformers.ipynb
# OCR (Optical Character Recognition) from Images with Transformers
---
[Github](https://github.com/eugenesiow/practical-ml/) | More Notebooks @ [eugenesiow/practical-ml](https://github.com/eugenesiow/practical-ml)
---__... | {
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# Notebook from drammock/mne-tools.github.io
Path: 0.16/_downloads/plot_brainstorm_auditory.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
# Brainstorm auditory tutorial dataset
Here we compute the evoked from raw for the auditory Brainstorm
tutorial dataset. For comparison, see [1]_ and the associated... | {
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# Notebook from unibw-patch/fuzzingbook
Path: notebooks/WhenToStopFuzzing.ipynb
# When To Stop Fuzzing
In the past chapters, we have discussed several fuzzing techniques. Knowing _what_ to do is important, but it is also important to know when to _stop_ doing things. In this chapter, we will learn when to _stop fuz... | {
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"ecology"
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# Notebook from tAndreani/scATAC-benchmarking
Path: Extra/Buenrostro_2018/test_peaks/Control/run_clustering_frequency.ipynb
<code>
import pandas as pd
import numpy as np
import scanpy as sc
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.cluster imp... | {
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# Notebook from adam-dziedzic/time-series-ml
Path: pytorch_tutorials/pytorch_tutorial_NLP3_word_embeddings_tutorial.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
Word Embeddings: Encoding Lexical Semantics
===========================================
Word embeddings are dense vectors of real numbers, on... | {
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# Notebook from ebayandelger/MSDS600
Path: Reddit_Sentiment_Assignment.ipynb
<code>
# This handy piece of code changes Jupyter Notebooks margins to fit your screen.
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))_____no_output_____
</code>
## Be sure... | {
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# Notebook from trquinn/tangos
Path: docs/Data exploration with python.ipynb
Interactive analysis with python
--------------------------------
Before starting this tutorial, ensure that you have set up _tangos_ [as described here](https://pynbody.github.io/tangos/) and the data sources [as described here](https://pyn... | {
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# Notebook from DerwenAI/conda_sux
Path: spaCy_tuTorial.ipynb
# An Introduction to Natural Language in Python using spaCy_____no_output_____## Introduction
This tutorial provides a brief introduction to working with natural language (sometimes called "text analytics") in Pytho, using [spaCy](https://spacy.io/) and re... | {
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# Notebook from biqar/Fall-2020-ITCS-8010-MLGraph
Path: assignments/assignment_0/gnp_simulation_experiment.ipynb
$\newcommand{\xv}{\mathbf{x}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\Chi}{\mathcal{X}}
\newcommand{\R}{\rm I\!R}
\newcommand{\sign}{\text{sign}}
\newcommand{\Tm}{\mathbf{T}}
\newcommand{\Xm}{\mathb... | {
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# Notebook from maximcondon/Project_BabyNames
Path: Project_1_Babynames.ipynb
# Project 1: Babynames_____no_output_____## I. Characterise One File
### 1. Read the data
- Read the file yob2000.txt
- Name the columns
- Print the first 10 entries_____no_output_____
<code>
import pandas as pd
from matplotli... | {
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# Notebook from markjdugger/arcgis-python-api
Path: guide/14-deep-learning/how_deeplabv3_works.ipynb
# How DeepLabV3 Works
## Semantic segmentation_____no_output_____Semantic segmentation, also known as pixel-based classification, is an important task in which we classify each pixel of an image as belonging to a part... | {
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# Notebook from csavur/biosignalsnotebooks
Path: biosignalsnotebooks_notebooks/Categories/Train_And_Classify/classification_game_volume_2.ipynb
<link rel="stylesheet" href="../../styles/theme_style.css">
<!--link rel="stylesheet" href="../../styles/header_style.css"-->
<link rel="stylesheet" href="https://cdnjs.cloudf... | {
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# Notebook from hossainlab/dsnotes
Path: book/pandas/08-Filtering Rows.ipynb
# Filtering Rows _____no_output_____
<code>
# import pandas
import pandas as pd _____no_output_____# read movie data
movies = pd.read_csv("http://bit.ly/imdbratings")_____no_output_____# examine first few rows
movies.head() _____no_output... | {
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# Notebook from PacktPublishing/Hands-On-Artificial-Intelligence-for-IoT
Path: Chapter05/GuessTheWord.ipynb
<code>
import string
import random
from deap import base, creator, tools_____no_output_____## Create a Finess base class which is to be minimized
# weights is a tuple -sign tells to minimize, +1 to maximize
cr... | {
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"path": "Chapter05/GuessTheWord.ipynb",
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# Notebook from cumc/xqtl-pipeline
Path: code/commands_generator/eQTL_analysis_commands.ipynb
# Bulk RNA-seq eQTL analysis
This notebook provide a command generator on the XQTL workflow so it can automate the work for data preprocessing and association testing on multiple data collection as proposed._____no_output___... | {
"repository": "cumc/xqtl-pipeline",
"path": "code/commands_generator/eQTL_analysis_commands.ipynb",
"matched_keywords": [
"RNA-seq"
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"stars": 2,
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"max_line_length": 347365,
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# Notebook from JulioLarrea/course-content
Path: tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/ass... | {
"repository": "JulioLarrea/course-content",
"path": "tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb",
"matched_keywords": [
"neuroscience"
],
"stars": 1,
"size": 48152,
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"max_line_length": 588,
"avg_line_length": 35.5890613452,
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# Notebook from soumitrahazra/platypos
Path: examples/Evolve_one_planet.ipynb
<code>
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from astropy import constants as const
# remove this line if you installed platypos with pip
sys.path.append('/work2/lketzer/work/gitlab/plat... | {
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"path": "examples/Evolve_one_planet.ipynb",
"matched_keywords": [
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# Notebook from smasoka/python-introduction
Path: notebooks/exercises/7 - NumPy.ipynb
# Exercises_____no_output_____## Simple array manipulation
Investigate the behavior of the statements below by looking
at the values of the arrays a and b after assignments:
```
a = np.arange(5)
b = a
b[2] = -1
b = a[:]
b[1] = -1
b ... | {
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# Notebook from fiji/Kappa
Path: Analysis/Notebooks/Spiral Dataset/2_Measure_Curvature.ipynb
**Important**: This notebook is different from the other as it directly calls **ImageJ Kappa plugin** using the [`scyjava` ImageJ brige](https://github.com/scijava/scyjava).
Since Kappa uses ImageJ1 features, you might not be... | {
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"path": "Analysis/Notebooks/Spiral Dataset/2_Measure_Curvature.ipynb",
"matched_keywords": [
"ImageJ"
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"stars": null,
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"hexsha": "cb1b7195fb98eac6b381963274799542d9d8230b",
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# Notebook from dschwen/chimad-phase-field
Path: hackathon1/problems.ipynb
<code>
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
$('div.prompt').hide();
} else {
$('div.input').show();
$('div.prompt').show();
}
code_show = !cod... | {
"repository": "dschwen/chimad-phase-field",
"path": "hackathon1/problems.ipynb",
"matched_keywords": [
"evolution"
],
"stars": null,
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# Notebook from UWashington-Astro300/Astro300-W17
Path: FirstLast_HW4.ipynb
# First Last - Homework 4_____no_output_____* Use the `Astropy` units and constants packages to solve the following problems.
* Do not hardcode any constants!
* Unless asked, your units should be in the simplest SI units possible_____no_output... | {
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"path": "FirstLast_HW4.ipynb",
"matched_keywords": [
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"stars": null,
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# Notebook from yaoyongxin/qucochemistry
Path: examples/Tutorial_Disassociation_curve_end_to_end.ipynb
# End-to-end quantum chemistry VQE using Qu & Co Chemistry
In this tutorial we show how to solve the groundstate energy of a hydrogen molecule using VQE, as a function of the spacing between the atoms of the molecule... | {
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"path": "examples/Tutorial_Disassociation_curve_end_to_end.ipynb",
"matched_keywords": [
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"stars": null,
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# Notebook from LearnableLoopAI/blog2
Path: _notebooks/2022-01-27-MCControl_OffPolicy_BlackJack.ipynb
# "Monte Carlo 6: Off-Policy Control with Importance Sampling in Reinforcement Learning"
> Find the optimal policy using Weighted Importance Sampling
- toc: true
- branch: master
- badges: false
- comments: true
- hi... | {
"repository": "LearnableLoopAI/blog2",
"path": "_notebooks/2022-01-27-MCControl_OffPolicy_BlackJack.ipynb",
"matched_keywords": [
"evolution"
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"stars": null,
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"hexsha": "cb2071004dc3925bb86c89288d76e4dfeab21ff7",
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"a... |
# Notebook from JulienPeloton/fink_grandma_kn
Path: KN-Mangrove_filter.ipynb
# GRANDMA/Kilonova-catcher --- KN-Mangrove
The purpose of this notebook is to inspect the ZTF alerts that were selected by the Fink KN-Mangrove filter as potential Kilonova candidates in the period 2021/04/01 to 2021/08/31, and forwarded to ... | {
"repository": "JulienPeloton/fink_grandma_kn",
"path": "KN-Mangrove_filter.ipynb",
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"evolution"
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# Notebook from BioFreak95/schnetpack
Path: docs/tutorials/tutorial_01_preparing_data.ipynb
# Preparing and loading your data
This tutorial introduces how SchNetPack stores and loads data.
Before we can start training neural networks with SchNetPack, we need to prepare our data.
This is because SchNetPack has to strea... | {
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"path": "docs/tutorials/tutorial_01_preparing_data.ipynb",
"matched_keywords": [
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# Notebook from Skharwa1/Applied-Data-Science-with-Python-Specialization
Path: Course - 4: Applied Text Mining in Python/Module+2+(Python+3).ipynb
# Module 2 (Python 3)_____no_output_____## Basic NLP Tasks with NLTK_____no_output_____
<code>
import nltk
nltk.download()NLTK Downloader
---------------------------------... | {
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"path": "Course - 4: Applied Text Mining in Python/Module+2+(Python+3).ipynb",
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"stars": 9,
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# Notebook from vasudev-sharma/course-content
Path: tutorials/W2D4_DynamicNetworks/W2D4_Tutorial1.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D4_DynamicNetworks/W2D4_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets... | {
"repository": "vasudev-sharma/course-content",
"path": "tutorials/W2D4_DynamicNetworks/W2D4_Tutorial1.ipynb",
"matched_keywords": [
"evolution"
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"stars": null,
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# Notebook from bitcoffe/bofin
Path: examples/example-1d-Spin-bath-model-ohmic-fitting.ipynb
# Example 1d: Spin-Bath model, fitting of spectrum and correlation functions
### Introduction_____no_output_____The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is enco... | {
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"path": "examples/example-1d-Spin-bath-model-ohmic-fitting.ipynb",
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# Notebook from annacuomo/TenK10K_analyses_HPC
Path: notebooks/estimate_betas_CellRegMap_Bcells_noplasma.ipynb
<code>
import scanpy as sc
import pandas as pd
import xarray as xr
from numpy import ones
from pandas_plink import read_plink1_bin
from numpy.linalg import cholesky
import matplotlib.pyplot as plt
import time... | {
"repository": "annacuomo/TenK10K_analyses_HPC",
"path": "notebooks/estimate_betas_CellRegMap_Bcells_noplasma.ipynb",
"matched_keywords": [
"Scanpy"
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"stars": null,
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"hexsha": "cb2265400b63110153d3d1b22922d06dd3b56233",
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... |
# Notebook from liaison/python
Path: tensorflow/tensorflow_simple_linear_regression.ipynb
### Abstract
This is an example to show to use use the basic API of TensorFlow, to construct a linear regression model.
This notebook is an exercise adapted from [the Medium.com blog](https://medium.com/@saxenarohan97/intro-to... | {
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"stars": 3,
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# Notebook from edgarriba/lightning-flash
Path: flash_notebooks/image_classification.ipynb
<a href="https://colab.research.google.com/github/PyTorchLightning/lightning-flash/blob/master/flash_notebooks/image_classification.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg"... | {
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"path": "flash_notebooks/image_classification.ipynb",
"matched_keywords": [
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"stars": 1,
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# Notebook from patrick-kidger/diffrax
Path: examples/symbolic_regression.ipynb
# Symbolic Regression_____no_output_____This example combines neural differential equations with regularised evolution to discover the equations
$\frac{\mathrm{d} x}{\mathrm{d} t}(t) = \frac{y(t)}{1 + y(t)}$
$\frac{\mathrm{d} y}{\mathrm{... | {
"repository": "patrick-kidger/diffrax",
"path": "examples/symbolic_regression.ipynb",
"matched_keywords": [
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"stars": 377,
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# Notebook from ShepherdCode/Soars2021
Path: Notebooks/ORF_MLP_117.ipynb
# ORF MLP
Trying to fix bugs.
NEURONS=128 and K={1,2,3}.
_____no_output_____
<code>
import time
def show_time():
t = time.time()
print(time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)))
show_time()2021-07-25 20:40:39 UTC
PC_... | {
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"path": "Notebooks/ORF_MLP_117.ipynb",
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"RNA"
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"stars": 1,
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# Notebook from tech4germany/bam-inclusify
Path: data/vienna_catalog/main.ipynb
This notebook downloads and processes the gender data from the Vienna catalog. The data comes from a [gendering add-in for Microsoft Word 2010](https://web.archive.org/web/20210629142645/https:/archive.codeplex.com/?p=gendering) that has b... | {
"repository": "tech4germany/bam-inclusify",
"path": "data/vienna_catalog/main.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 11,
"size": 8118,
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# Notebook from docinfosci/canvasxpress-python
Path: tutorials/notebook/cx_site_chart_examples/bubble_4.ipynb
# Example: CanvasXpress bubble Chart No. 4
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.or... | {
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# Notebook from naxvm/ML4all
Path: C3.Classification_LogReg/RegresionLogistica_student.ipynb
# Logistic Regression
Notebook version: 2.0 (Nov 21, 2017)
2.1 (Oct 19, 2018)
Author: Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Changes: v... | {
"repository": "naxvm/ML4all",
"path": "C3.Classification_LogReg/RegresionLogistica_student.ipynb",
"matched_keywords": [
"evolution"
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"stars": null,
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# Notebook from VCMason/PyGenToolbox
Path: notebooks/Lyna/SplitFastqBySeqLength_WT_E_50Mill.ipynb
<code>
%load_ext autoreload
%autoreload 2
import datetime
import os
print(datetime.datetime.now())
from pygentoolbox import SplitFastqFileBySeqLength
# from pygentoolbox.Tools import read_interleaved_fasta_as_noninterlea... | {
"repository": "VCMason/PyGenToolbox",
"path": "notebooks/Lyna/SplitFastqBySeqLength_WT_E_50Mill.ipynb",
"matched_keywords": [
"RNA"
],
"stars": null,
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# Notebook from MaayanLab/jupyter-template-catalog
Path: appyters/SigCom_LINCS_Consensus_Appyter/SigCom LINCS Consensus Appyter.ipynb
<code>
#%%appyter init
from appyter import magic
magic.init(lambda _=globals: _())_____no_output_____%%appyter hide_code
{% do SectionField(
name='PRIMARY',
title='1. Upload you... | {
"repository": "MaayanLab/jupyter-template-catalog",
"path": "appyters/SigCom_LINCS_Consensus_Appyter/SigCom LINCS Consensus Appyter.ipynb",
"matched_keywords": [
"CRISPR"
],
"stars": null,
"size": 64586,
"hexsha": "cb2b802957fc599bef9d9cbb9600c2c1196beacd",
"max_line_length": 927,
"avg_line_leng... |
# Notebook from LungCellAtlas/HLCA_reproducibility
Path: notebooks/1_building_and_annotating_the_atlas_core/03_hvg_selection_log_transf.ipynb
## Highly variable gene selection and log-transformation_____no_output_____In this notebook we select highly variable genes and perform log-transformation of normalized counts f... | {
"repository": "LungCellAtlas/HLCA_reproducibility",
"path": "notebooks/1_building_and_annotating_the_atlas_core/03_hvg_selection_log_transf.ipynb",
"matched_keywords": [
"Scanpy"
],
"stars": null,
"size": 70182,
"hexsha": "cb2bee4291d3b8afa3bcc49dd56963a94bf8c2bf",
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"avg... |
# Notebook from aggle/jwst_validation_notebooks
Path: jwst_validation_notebooks/ami3/run_ami_pipeline.ipynb
<a id="title_ID"></a>
# JWST Pipeline Validation Notebook: AMI3, AMI3 Pipeline
<span style="color:red"> **Instruments Affected**</span>: NIRISS
### Table of Contents
<div style="text-align: left">
<br>... | {
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"path": "jwst_validation_notebooks/ami3/run_ami_pipeline.ipynb",
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# Notebook from Grarck/ML4all
Path: C3.Classification_LogReg/RegresionLogistica_student.ipynb
# Logistic Regression
Notebook version: 2.0 (Nov 21, 2017)
2.1 (Oct 19, 2018)
Author: Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Changes: ... | {
"repository": "Grarck/ML4all",
"path": "C3.Classification_LogReg/RegresionLogistica_student.ipynb",
"matched_keywords": [
"evolution"
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"stars": 1,
"size": 388266,
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# Notebook from tawabshakeel/DataCamp-Projects
Path: A Network Analysis of Game of Thrones/notebook.ipynb
## 1. Winter is Coming. Let's load the dataset ASAP!
<p>If you haven't heard of <em>Game of Thrones</em>, then you must be really good at hiding. Game of Thrones is the hugely popular television series by HBO base... | {
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"path": "A Network Analysis of Game of Thrones/notebook.ipynb",
"matched_keywords": [
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"stars": 5,
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# Notebook from BNN-UPC/ignnition
Path: notebooks/shortest_path.ipynb
<a href="https://colab.research.google.com/github/BNN-UPC/ignnition/blob/ignnition-nightly/notebooks/shortest_path.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_output___... | {
"repository": "BNN-UPC/ignnition",
"path": "notebooks/shortest_path.ipynb",
"matched_keywords": [
"evolution"
],
"stars": 18,
"size": 206741,
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# Notebook from daniel-koehn/Differential-equations-earth-system
Path: 03_Lorenz_equations/03_LorenzEquations_fdsolve.ipynb
###### Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Daniel Koehn based on Jupyter notebooks by Marc Spiege... | {
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"path": "03_Lorenz_equations/03_LorenzEquations_fdsolve.ipynb",
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"stars": 30,
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"hexsha": "cb3448d410839139690980c2784f852304c60f8c",
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# Notebook from ranstotz/data-eng-nanodegree
Path: course_materials/project_03_data_warehouses/L3 Exercise 4 - Table Design - Solution.ipynb
# Exercise 4: Optimizing Redshift Table Design_____no_output_____
<code>
%load_ext sql_____no_output_____from time import time
import configparser
import matplotlib.pyplot as pl... | {
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# Notebook from fakecoinbase/sweetpandslashAlgorithms
Path: NLP Data Prep.ipynb
<code>
from fastai.text import *_____no_output_____from fastai.tabular import *_____no_output_____path = Path('')_____no_output_____data = pd.read_csv('good_small_dataset.csv', engine='python')_____no_output_____data.head()_____no_output__... | {
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# Notebook from EDSEL-skoltech/maxvol_sampling
Path: Boxplots_Interpolation.ipynb
## Boxplot plots
_______
tg: @misha_grol and anna.petrovskaia@skoltech.ru
Boxplots for features based on DEM and NDVI_____no_output_____
<code>
# Uncomment for Google colab
# !pip install maxvolpy
# !pip install clhs
# !git clone ht... | {
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# Notebook from RuishanLiu/TrialPathfinder
Path: tutorial/tutorial.ipynb
<code>
import pandas as pd
import numpy as np
import TrialPathfinder as tp_____no_output_____
</code>
# Trial PathFinder_____no_output_____## Load Data Tables
TrialPathfinder reads tables in Pandas dataframe structure (pd.dataframe) as default.... | {
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"path": "tutorial/tutorial.ipynb",
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# Notebook from DeloitteHux/tensor-house
Path: time-series/lstm-forecasting.ipynb
# Enterprise Time Series Forecasting and Decomposition Using LSTM
This notebook is a tutorial on time series forecasting and decomposition using LSTM.
* First, we generate a signal (time series) that includes several components that are... | {
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# Notebook from CambridgeUniversityPress/Interstellar-and-Intergalactic-Medium
Path: Chapter6/Fig6_2_Trumpler1930.ipynb
# Trumpler 1930 Dust Extinction
Figure 6.2 from Chapter 6 of *Interstellar and Intergalactic Medium* by Ryden & Pogge, 2021,
Cambridge University Press.
Data are from [Trumpler, R. 1930, Lick Obse... | {
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# Notebook from labs15-rv-life/data-science
Path: app_reviews/app_reviews_scattertext.ipynb
<code>
import pandas as pd_____no_output_____ios = pd.read_csv('app_reviews/rv_ios_app_reviews.csv')
ios['Content'] = ios['label_title']+ios['review']
ios = ios.drop(['app_name','app_version','label_title','review'], axis=1)
pr... | {
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# Notebook from seungkyoon/SimpleITK-Notebooks
Path: Python/10_matplotlib's_imshow.ipynb
# Using `matplotlib` to display inline images
In this notebook we will explore using `matplotlib` to display images in our notebooks, and work towards developing a reusable function to display 2D,3D, color, and label overlays for... | {
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# Notebook from fomightez/cl_demo-binder
Path: notebooks/Using Biopython PDB Header Parser to get missing residues.ipynb
# Using Biopython's PDB Header parser to get missing residues
Previously this worked out and had to be run at that time with a development version of Biopython that I got working [here](https://gi... | {
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# Notebook from ArunKara/web_scraping_hw
Path: mission_to_mars.ipynb
<code>
from bs4 import BeautifulSoup
from splinter import Browser
from pprint import pprint
import pymongo
import pandas as pd
import requests_____no_output_____!which chromedriver/usr/local/bin/chromedriver
executable_path = {'executable_path': 'ch... | {
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"path": "mission_to_mars.ipynb",
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# Notebook from Laurans/hmm-tagger
Path: HMM Tagger.ipynb
# Project: Part of Speech Tagging with Hidden Markov Models
---
### Introduction
Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural lan... | {
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# Notebook from moekay/course-content-dl
Path: tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial2.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial2.ipynb" target="_parent"><img src="https://colab.research... | {
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"path": "tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial2.ipynb",
"matched_keywords": [
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# Notebook from caganze/popsims
Path: notebooks/gizmo_read_tutorial.ipynb
# tutorial for reading a Gizmo snapshot
@author: Andrew Wetzel <arwetzel@gmail.com>_____no_output_____
<code>
# First, move within a simulation directory, or point 'directory' below to a simulation directory.
# This directory should contain ei... | {
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"path": "notebooks/gizmo_read_tutorial.ipynb",
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# Notebook from mmithil/Web-Mining-Repo
Path: .ipynb_checkpoints/Updated_goodreads_script-checkpoint.ipynb
<code>
import urllib2
from bs4 import BeautifulSoup
import csv
import time
import re
import urllib2
import csv
import time
import sys
import xml.etree.ElementTree as ET
import os
import random
import traceback
fr... | {
"repository": "mmithil/Web-Mining-Repo",
"path": ".ipynb_checkpoints/Updated_goodreads_script-checkpoint.ipynb",
"matched_keywords": [
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# Notebook from LucianoPereiraValenzuela/QuantumNeuralNetworks_for_StateDiscrimination
Path: qnn/tests/test_minimum_error_discrimination.ipynb
# Test: Minimum error discrimination
In this notebook we are testing the evolution of the error probability with the number of evaluations._____no_output_____
<code>
import ... | {
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# Notebook from heprom/cvml
Path: corrections/single_layer_neural_network_cor.ipynb
# Single layer Neural Network
In this notebook, we will code a single neuron and use it as a linear classifier with two inputs. The tuning of the neuron parameters is done by backpropagation using gradient descent._____no_output_____
... | {
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"path": "corrections/single_layer_neural_network_cor.ipynb",
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# Notebook from j-berg/explore_colon
Path: explore.ipynb
# Analyzing colon tumor gene expression data
Data source:
- https://dx.doi.org/10.1038%2Fsdata.2018.61
- https://www.ncbi.nlm.nih.gov/gds?term=GSE8671
- https://www.ncbi.nlm.nih.gov/gds?term=GSE20916_____no_output_____### 1. Initialize the environment and varia... | {
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"path": "explore.ipynb",
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"gene expression"
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# Notebook from veeralakrishna/DataCamp-Portofolio-Projects-R
Path: Modeling the Volatility of US Bond Yields/notebook.ipynb
## 1. Volatility changes over time
<p>What is financial risk? </p>
<p>Financial risk has many faces, and we measure it in many ways, but for now, let's agree that it is a measure of the possible... | {
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"path": "Modeling the Volatility of US Bond Yields/notebook.ipynb",
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# Notebook from zealseeker/deepchem
Path: examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb
# Tutorial Part 2: Learning MNIST Digit Classifiers
In the previous tutorial, we learned some basics of how to load data into DeepChem and how to use the basic DeepChem objects to load and manipulate this data. In t... | {
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# Notebook from PabloSoto1995/teachopencadd
Path: teachopencadd/talktorials/T008_query_pdb/talktorial.ipynb
# T008 · Protein data acquisition: Protein Data Bank (PDB)
Authors:
- Anja Georgi, CADD seminar, 2017, Charité/FU Berlin
- Majid Vafadar, CADD seminar, 2018, Charité/FU Berlin
- Jaime Rodríguez-Guerra, Volkam... | {
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"matched_keywords": [
"structural biology",
"genomics",
"bioinformatics",
"biology"
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# Notebook from AngShengJun/dsiCapstone
Path: codes/P5.2 Topic Modeling.ipynb
## P5.2 Topic Modeling_____no_output_____---_____no_output_____### Content
- [Topic Modelling using LDA](#Topic-Modelling-using-LDA)
- [Topic Modeling (Train data)](#Topic-Modeling-(Train-data))
- [Optimal Topic Size](#Optimal-Topic-Size)
- ... | {
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# Notebook from RPGroup-PBoC/chann_cap
Path: src/image_analysis/20190816_O2_long_growth_test/20190816_data_comparison.ipynb
# Comparison of the data taken with a long adaptation time_____no_output_____(c) 2019 Manuel Razo. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativec... | {
"repository": "RPGroup-PBoC/chann_cap",
"path": "src/image_analysis/20190816_O2_long_growth_test/20190816_data_comparison.ipynb",
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# Notebook from QRussia/basics-of-quantum-computing-translate
Path: bronze/.ipynb_checkpoints/B03_One_Bit-checkpoint.ipynb
<table width="100%"> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="35%" align="left"> </a></... | {
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"matched_keywords": [
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# Notebook from lukexyz/insultswordfight
Path: notebooks/03_aoe2_civgen.ipynb
<code>
# default_exp core_____no_output_____
</code>
# Few-shot Learning with GPT-J
> API details._____no_output_____
<code>
# export
import os
import pandas as pd_____no_output_____#hide
from nbdev.showdoc import *
import toml
s = toml.l... | {
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# Notebook from urviyi/artificial-intelligence
Path: Projects/4_HMM Tagger/HMM Tagger.ipynb
# Project: Part of Speech Tagging with Hidden Markov Models
---
### Introduction
Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often use... | {
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"path": "Projects/4_HMM Tagger/HMM Tagger.ipynb",
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# Notebook from MeganStalker/Advanced_Practical_Chemistry_Year_3
Path: Week_3/week_3.ipynb
# Week 3
## Introduction to Solid State _____no_output_____
<code>
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess
from polypy.read import History
from polypy.msd import MSD
from polypy import pl... | {
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"path": "Week_3/week_3.ipynb",
"matched_keywords": [
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# Notebook from gfeiden/MagneticUpperSco
Path: notes/.ipynb_checkpoints/convective_structure-checkpoint.ipynb
# Radiative Cores & Convective Envelopes
Analysis of how magnetic fields influence the extent of radiative cores and convective envelopes in young, pre-main-sequence stars.
Begin with some preliminaries.____... | {
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"path": "notes/.ipynb_checkpoints/convective_structure-checkpoint.ipynb",
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# Notebook from teo-milea/federated
Path: docs/tutorials/sparse_federated_learning.ipynb
##### Copyright 2021 The TensorFlow Federated Authors._____no_output_____
<code>
#@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... | {
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# Notebook from volfi/CVND---Image-Captioning-Project
Path: 2_Training.ipynb
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good mode... | {
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# Notebook from 0todd0000/fdr1d
Path: Appendix/ipynb/AppendixE.ipynb
# Appendix E: Validation of FDR’s control of false positive node proportion
This appendix contains RFT and FDR results (Fig.E1) from six experimental datasets and a total of eight different analyses (Table E1) that were conducted but were not inclu... | {
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# Notebook from davemcg/scEiaD
Path: colab/cell_type_ML_labelling.ipynb
<a href="https://colab.research.google.com/github/davemcg/scEiaD/blob/master/colab/cell_type_ML_labelling.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_output_____# Aut... | {
"repository": "davemcg/scEiaD",
"path": "colab/cell_type_ML_labelling.ipynb",
"matched_keywords": [
"Scanpy",
"scRNA",
"CellRanger"
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# Notebook from cilsya/coursera
Path: Machine _Learning_and_Reinforcement_Learning_in_Finance/03_Reinforcement_Learning_in_Finance/02_QLBS Model Implementation/dp_qlbs_oneset_m3_ex2_v3.ipynb
## The QLBS model for a European option
Welcome to your 2nd assignment in Reinforcement Learning in Finance. In this exercise y... | {
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"path": "Machine _Learning_and_Reinforcement_Learning_in_Finance/03_Reinforcement_Learning_in_Finance/02_QLBS Model Implementation/dp_qlbs_oneset_m3_ex2_v3.ipynb",
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# Notebook from tAndreani/scATAC-benchmarking
Path: Extra/Buenrostro_2018/test_blacklist/SCRAT_buenrostro2018-blacklist-rm.ipynb
### Installation_____no_output_____`devtools::install_github("zji90/SCRATdatahg19")`
`source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")` _____no_output_____... | {
"repository": "tAndreani/scATAC-benchmarking",
"path": "Extra/Buenrostro_2018/test_blacklist/SCRAT_buenrostro2018-blacklist-rm.ipynb",
"matched_keywords": [
"Bioconductor"
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"stars": 2,
"size": 58030,
"hexsha": "cb799a2365167c75f4080e5729dac08584bd5544",
"max_line_length": 339,
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# Notebook from NCBI-Hackathons/ncbi-cloud-tutorials
Path: BLAST tutorials/notebooks/GSD/GSD Rpb1_orthologs_in_1011_genomes.ipynb
# GSD: Rpb1 orthologs in 1011 genomes collection
This collects Rpb1 gene and protein sequences from a collection of natural isolates of sequenced yeast genomes from [Peter et al 2017](http... | {
"repository": "NCBI-Hackathons/ncbi-cloud-tutorials",
"path": "BLAST tutorials/notebooks/GSD/GSD Rpb1_orthologs_in_1011_genomes.ipynb",
"matched_keywords": [
"BioPython",
"evolution"
],
"stars": 11,
"size": 310780,
"hexsha": "cb7cd48ceada74091147e98b11980254432a9438",
"max_line_length": 87292,... |
# Notebook from devVipin01/Machine-learning-AI-Templates
Path: Regression/RadiusNeighborsRegressor_MinMaxScaler_PolynomialFeatures.ipynb
# RadiusNeighborsRegressor with MinMaxScaler & Polynomial Features
_____no_output_____**This Code template is for the regression analysis using a RadiusNeighbors Regression and the f... | {
"repository": "devVipin01/Machine-learning-AI-Templates",
"path": "Regression/RadiusNeighborsRegressor_MinMaxScaler_PolynomialFeatures.ipynb",
"matched_keywords": [
"evolution"
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"stars": 2,
"size": 133925,
"hexsha": "cb7d47d2ec8fcd64afebd4b209df752259b7bbad",
"max_line_length": 50448,
"avg_lin... |
# Notebook from jouterleys/NSCI801-QuantNeuro
Path: NSCI801_Reproducibility.ipynb
# NSCI 801 - Quantitative Neuroscience
## Reproducibility, reliability, validity
Gunnar Blohm_____no_output_____### Outline
* statistical considerations
* multiple comparisons
* exploratory analyses vs hypothesis testing
* Open S... | {
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"path": "NSCI801_Reproducibility.ipynb",
"matched_keywords": [
"Salmon",
"neuroscience"
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"stars": 102,
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"max_line_length": 8000,
"avg_line_length": 42.119460501,
"alphanum_f... |
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