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# -*- coding: utf-8 -*-
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# # Superposition Kata
#
# **Superpo... | Superposition/Superposition.ipynb |
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import openpnm as op
import openpnm.models.geometry as... | Simulations/mixed_continuum_and_pore_network.ipynb |
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# # Pandana network accessibility simple demo
#
# This... | accessibility/pandana-accessibility-demo-simple.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | yolov5.ipynb |
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# %matplotlib inline
import openpathsampling as paths
... | examples/alanine_dipeptide_tps/AD_tps_4_advanced.ipynb |
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from seqeval.metrics import accuracy_score
f... | notebooks/002_scipyNER.ipynb |
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# title: "Deep Dream"
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# ## Config
import os
os.environ['CUDA_VISIBLE_DEVICES']... | experiments/celeba-cef-joint.ipynb |
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# # 4.2. OpenAI Gym Environments
# ## Gym basics and a s... | Section 4/Video 4.2.ipynb |
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# Tutorial 3: Realism and Complexity
# ===============... | howtolens/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.ipynb |
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p = [2, 4, 10, 6, 8, 4]
max0, min0 = max(p), min(p... | ue7/ue7.ipynb |
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import datetime, time
import simpy
import shapely... | notebooks/17_cutters and barges (BUG IN RESERVATIONS).ipynb |
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import pandas as pd
from sklearn.linear_model i... | unit_4/ml_4.7.ipynb |
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from math import *
import numpy as np
... | Lab-02/lab02-1.ipynb |
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# ## LAMBDA FUNCTION
#
# anonymous funtion,
#
# a funt... | pypractise11.ipynb |
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# Imports
import random
import sys
import numpy as... | notebooks/inpainting_v4_wgan_complex.ipynb |
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# # Compile and Deploy a Tens... | sagemaker_neo_compilation_jobs/deploy_tensorflow_model_on_Inf1_instance/tensorflow_distributed_mnist_neo_inf1.ipynb |
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# # Constructing ... | Python_Stock/Portfolio_Strategies/Optimal_Portfolio.ipynb |
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import nn
import importlib
importlib.reload(nn)
fr... | 1d_nn.ipynb |
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# # SciPy
# The SciPy library is one of the core pack... | tests/ipython-notebooks/SciPy.ipynb |
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# # Interactive Plotting and Monte Carlo Simulations o... | portfolio-construction-and-analysis-with-python/monte-carlo-simulations-of-CPPI-and-diversification.ipynb |
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import cv2
import time
import numpy as np
## Prep... | invisible cloak.ipynb |
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# # Tutorial: optimal binning with binary target - Loc... | doc/source/tutorials/tutorial_binary_localsolver.ipynb |
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# import modules
import pandas as pd
import nu... | .ipynb_checkpoints/cleaning data-checkpoint.ipynb |
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# ### Imports
import pandas as pd
import numpy as np
... | Machine Learning/Machine Learning.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | 09_SkimLit_nlp_milestone_project_2.ipynb |
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import matplotlib.pyplot as plt
import numpy as np
imp... | notebooks/Notebook-13 - LogLog Exceedance Probability.ipynb |
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# # Prob2b.py
def gcd(a, b):
""" finds gcd/lcm of... | ds/Prob2b.ipynb |
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# ## Observational Write-Up (Results)
# Four mice tum... | Pymaceuticals/pymaceuticals.ipynb |
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# Load pickled data
import pickle
import numpy as ... | Traffic_Sign_Classifier.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb |
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# # OUTLINE
# * Introduce DALBAR study, criticisms
# *... | content-draft/XX_ETF_timing.ipynb |
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# # Face Detection using a Pytorch CNN - Using LFW and... | Face Detection using a Pytorch CNN - Using LFW and AID Datasets At. 2.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | secao_6/aula_68.ipynb |
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# # Cvxpylayers tutorial
# +
import cvxpy as cp
impor... | examples/torch/tutorial.ipynb |
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import numpy as np
import pandas as pd
# %matplotlib ... | final lab code and report/decision tree/decision tree 0001 (1).ipynb |
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import tensorfl... | check_if_gpu.ipynb |
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import matplotlib.pyplot as plt
import matplotlib
... | main_train_and_test_brushing.ipynb |
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# # Import
# ## Packages
# +
# General
import iterto... | inst/model_development/notebooks/direction_classification_model_verification.ipynb |
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# name: python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east... | Chapter09/03 - Hosting multiple models with multi-model endpoints.ipynb |
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impo... | code/Baseline Scratch Files And Analysis Source File/GraphGeneration.ipynb |
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# + colab={"base_uri": "https://localhost:8080/", "height": 349} id="9OsVp1dW... | j_components_assignment2.ipynb |
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# # The operating system
# ![](ghtop_images/header2.p... | _notebooks/2021-10-04-OS.ipynb |
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# # COVID-19 OPEN RESEARCH DATASET
import os
import j... | covidModel-2.ipynb |
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# +
# Imports here
import torch
# from torch import nn... | TransferLearning/2_FlowerClassifier/image_utils.ipynb |
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# + pycharm={"name": "#%%\n"}
import time
import copy... | playground/pipeline/mesh_slicing.ipynb |
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# ## Universal Approximation Theorem:
#
# Let $\phi(\... | Lecture17_Backpropagation/Lecture 17 - Backpropagation.ipynb |
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# # Data Visualization
#
# The RAPIDS AI ec... | intro_notebooks/data_visualization.ipynb |
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import numpy as np
import pandas as pd
from scipy.lina... | data + Solutions/1 - Scenario DA1/.ipynb_checkpoints/da1-checkpoint.ipynb |
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#Taken from https://www.kaggle.com/rikdifos/nba-pl... | Final Test Models/nba/nba-players-salary-prediction.ipynb |
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# Change directory to VSCode workspace root so that re... | docs/x_to_a_summary.ipynb |
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# # Create Training Job (Hyperparamter Injection)
#
#... | sagemaker-fundamentals/create-training-job/create_training_job_hyperparameter_injection.ipynb |
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# ## Advanced Lane Finding Project
#
# The goals / ste... | Lane_line.ipynb |
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# First set of Problems
1. True and True # True
2... | E28.ipynb |
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# ## Function |
#1 it will print 5
def a():
retur... | function_1.ipynb |
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from qiskit.optimization import QuadraticProgram
f... | comparison/Ocean/LinearProgramming/DWave_Qiskit_Plugin_Test/External/Binary_LP_BURKARDT.ipynb |
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# # Chapter 1: Arrays and Strings
### Two Sum ###
def... | test_prep.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | VAE_Torch.ipynb |
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import numpy as np
import os
import... | Notebooks/pre-processing.ipynb |
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# # Command Line
#
# This is a reference for all the `--help` message from al... | guide/cli_help.ipynb |
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import numpy as np
all_aufgaben = []
solved_aufgaben =... | Lernphase/Aufgaben_zu_loesen.ipynb |
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import os
import pandas as pd
import numpy as np
i... | performance_test_std_preempt_rt_rpi_3B_3Bp.ipynb |
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import numpy as np
import pandas as pd
import matp... | Module 9/Module_9-Case+study+2.ipynb |
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# # Docker-compose
#
# ![docker compose!](./assets/doc... | Bonus_resources/deployment/2.Docker/5.docker-compose.ipynb |
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# # Homework 0
# In this homework, we will go through ... | hw0_release/.ipynb_checkpoints/hw0-checkpoint.ipynb |
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# # Bot Check Check the followers count using bot scor... | BotScoresDoc.ipynb |
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# ## ewf-wfp-02-01-04 - Land Surface Temperature Anoma... | src/main/app-resources/notebook/libexec/input.ipynb |
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# # Classifier drift detector on CIFAR-10
#
# ### Meth... | examples/cd_clf_cifar10.ipynb |
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# %matplotlib inline
import matplotlib.pyplot ... | preprocessing/population_processing.ipynb |
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# +
# Plots Pareto distribution... | notebooks/book2/02/pareto_dist_plot.ipynb |
# +
"""
Play RPS w/Input
"""
p1 = input('player 1 choice: ') # from user input
p2 = input('player 2 choice: ') # from user input
if p1 == p2:
print(0)
elif p1 == 'r' and p2 == 's':
print(1)
elif p1 == 'r' and p2 == 'p':
print(2)
elif p1 == 'p' and p2 == 's':
print(2)
elif p1 == 'p' and p2 == 'r':
... | pset_conditionals/rps/solution/nb/p3.ipynb |
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# ### Note
# * In... | HeroesOfPymoli/Pandas_Challenge_Final.ipynb |
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# # What’s new in Python 3.8?
#
# In Python 3.8, the s... | docs/workspace/jupyter/kernels/python38.ipynb |
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import pandas as pd
import matplotlib.pyplot as pl... | dev/Fingerprint-dummy.ipynb |
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# If you run on colab uncomment the... | examples/notebooks/models_training/vamp_training.ipynb |
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# %matplotlib inline
import skimage.io as io
img ... | Chapter 10/Section16_01_Getting_Started.ipynb |
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# ## JanataHack Cross Sell Prediction
# Cross-selling... | JanataHack-Cross-Sell-Prediction (1).ipynb |
# + ein.hycell=false ein.tags="worksheet-0" slideshow={"slide_type": "-"}
# %matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_context("talk")
sns.set_style("white")
# + ein.hycell=false ein.tags="worksheet-0" slideshow={"slide_type": "-"}
import numpy as np
import torch
import torch.nn a... | notebook/plot-toy-correlated-posterior.ipynb |
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# # Hamiltonian Monte Carlo
#
# As seen in previous no... | notebooks/3j.2 Hamiltonian Monte-Carlo.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | code/Migration_Data_Prediction_V2.ipynb |
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import argparse
import pickle
import os
import ... | experiments/2_artificial_data.ipynb |
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# + [markdown] button=false deletable=true new_sheet=f... | OCW/[Stanford]CS143_Compilers/Notes/Week01.ipynb |
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# + [markdown] slideshow={"slide_type": "slide"}
# # D... | day 02 PyTORCH and PyCUDA/PyCUDA/03 PyCUDA sigmoid.ipynb |
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import os
import requests
os.chdir("C:/Users/abewo/Do... | .ipynb_checkpoints/Exploratory Notebook-checkpoint.ipynb |
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# # **PROGETTO**
# # FEATURES NUMERICHE, CATEGORIALI,... | src/.ipynb_checkpoints/Progetto_BanNumCatData_COPIA-checkpoint.ipynb |
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# # Implementation of Word2Vec and FastText Word Embed... | Word2Vect_yoruba.ipynb |
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# # AKARI-NEP master catalogue
#... | dmu1/dmu1_ml_AKARI-NEP/1.3_NEP-Spitzer.ipynb |
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import pandas as pd
import numpy as np
import matplotl... | Untitled1.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Assignment_no_8.ipynb |
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# # Big data analysis homework 2
# ## Name: liusheng
#... | homework2/Big_Data_Homework2.ipynb |
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# # Exercise 1
# +
amount = 1000
interest_rate = 0.1
... | Week0/Week0-Exercise.ipynb |
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import numpy as np
import pandas as pd
import logging
... | visualization/.ipynb_checkpoints/lineplot-checkpoint.ipynb |
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# ## Objective and Outline
#
# Main goal is to have so... | advanced-tutorials/prefect_PPP_demo.ipynb |
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# # BootstrapOutOfBag
# An implementation of the out-... | mlxtend/docs/sources/user_guide/evaluate/BootstrapOutOfBag.ipynb |
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# + [markdown] id="s9c-vMyjq7-c"
# ____
# __Universidad Tecnológica Nacional,... | 07_programacion_matematica/casos_codigo/ioperativ_clase25_multiproducto_restriccion.ipynb |
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# <br><br><br><br><br>
#
# # Jailbreaking Python
#
# <... | 09-jailbreaking-python.ipynb |
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# <br>
# <hr>
# **“You don't have to be great to start... | 2 digit recognizer/beginners-guide-to-mnist-with-fast-ai.ipynb |
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# # Introduction
# Models are trained in googl... | notebooks/09_nam_convert_models_for_gcp.ipynb |
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#데이터 로드
sales = pd.read_csv("sales_rate.csv", encoding... | dayoung_trial1/11_add_variables_modified .ipynb |
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from __future__ import print_function
... | training/Training_rgb-r_r-corrected-full.ipynb |