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from pynvml import *
nvmlInit()
vram = nvmlDevice... | 决赛代码/测试固定图像大小的模型_346_split2_4_全集训练150代.ipynb |
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# <h1><center>SSP -- Assignment 4</center></h1>
# # 1... | SSP Assignment 4.ipynb |
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# + [markdown] colab_type="text" id="5eeje4O8fviH" pyc... | scripts/parking_her.ipynb |
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# # Preprocess
#
# This notebook preproces... | src/preprocess.ipynb |
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import pandas as pd
import numpy as np
df = pd.read_ex... | metabolic_models.ipynb |
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# + [markdown] colab_type="text" id="view-in-github"
#... | Machine Learning Summer School 2019 (Moscow, Russia)/tutorials/reinforcement_learning2/day2_solutions_new.ipynb |
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import panel as pn
pn.extension('terminal')
# When developing applications that are to be used by multiple users and which may process a lot ... | examples/user_guide/Performance_and_Debugging.ipynb |
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# # !pip install imgaug
# # !pip install --ignore-ins... | deeppixel/instance_segmen/Instance_segment_camera/Instance_segment_camera.ipynb |
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import numpy as np
import pandas as pd
import matplotl... | csv.ipynb |
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# ### Task 1.1. Prepare basis state
#
# **Input:**
# 1. Arr... | graded-task-template/GradedTask.ipynb |
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#import modules
import numpy as np
import tensorflo... | main.ipynb |
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# name: python361364bitbcimusicalcondae85380e674b24bdb93e8f1c1... | public/ipynb/Model_PCA_5956733.ipynb |
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# HIDDEN
from datascience import *
from pro... | miscellaneous_notebooks/Continuous_Distributions/Calculus_in_SymPy.ipynb |
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# name: python37664bitvelapipenvde09592071074... | ds/practice/daily_practice/20-08/20-08-19-232-wed.ipynb |
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# Importar los módulos de los paquetes que instalaste ... | notebooks/defunciones_fecha.ipynb |
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# # Employees performance and delivered value
# ![Emp... | Programming for data analysis Project.ipynb |
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# # Json tree representation
# > M... | nbs/03_json_tree.ipynb |
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import pandas as pd
import random
import itertools... | metis/Metis Challenge1.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | OOP_CONRAD.ipynb |
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# # By <NAME> with refrence from W3 schools... | Lab1/scatterplot.ipynb |
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# + id="PUNeUN_1JbAQ" cellView="form"
#@title Kaggle API
from IPython.displa... | User_Anime_Rating_Predictions.ipynb |
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# # Prophet Baseline
#
# *<NAME> 23-03-2020*
#
#
# I'l... | notebooks/apoirel-prophet-01.ipynb |
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import cesiumpy
options = dict(animation=True, baseLa... | examples/03_imageryproviders.ipynb |
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##################################################... | ies8-hm2-sga-vs-cga-ftrap5.ipynb |
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# ! ... | adaptive-avg-pool2d-output-1x1/adap.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="e90Uv_x5_iRe" outputId=... | Untitled7.ipynb |
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import numpy as np
import xarray as xr
import hvplot.x... | makefig.ipynb |
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# # Code for Chapter 1.
#
# In this case we will rev... | 01-Introduction/chapter01.ipynb |
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# # Preparing and loadi... | docs/tutorials/tutorial_01_preparing_data.ipynb |
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from abc import ABC, abstractmethod
from multiproc... | experiments/karla/diplomski-rad/blade/pb/racon-hax-pileups/pileup-generator-bug-fix.ipynb |
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from keras.datasets import imdb
(train_data, trai... | DeepLearningWithPython/08_TensorBoard/TensorBoard_IMDBbinClassification.ipynb |
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import arcpy
import pandas as pd
import os
arcpy.env.... | Create Stores Sample.ipynb |
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# ### Business Understanding
# We are interested in an... | Analysis.ipynb |
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# # Loading data into Elasticsearch
# Welcome! Hopefu... | load_metadata.ipynb |
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# # Clean Data of the results of experiment 2
# - This... | PlanB/Experiment2/.ipynb_checkpoints/Clean_data_ex2-checkpoint.ipynb |
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# + tags=["parameters"]
project_id = 'elife-data-pipel... | notebooks/peerscout/peerscout-disambiguate-editor-papers-details.ipynb |
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# + colab={"base_uri": "https://localhost:8080/", "hei... | src/specific_model_tests_colab/one_particle_lj.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | examples/notebooks/TutorialAlgorithmImplementation.ipynb |
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import xarray as xr
import numpy as np
import matp... | fsspec-reference-maker/generate_goes_jsons.ipynb |
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# # Edge Detection
# This notebook tests... | notebooks/EdgeDetection.ipynb |
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# + [markdown] toc=true
# <h1>Table of Contents<span c... | notebooks/query_log_analysis/query_log_analysis.ipynb |
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# # Exercícios de Python 01
# ## Criar um script Python que execute os seguintes passos
#
# 1. Solicite do usuário seu **nome, idade e profis... | cclhm0069/mod4b/exercicios.ipynb |
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# + [markdown] deletable=false editable=false
# Copyrig... | Regression-trees.ipynb |
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# (DUALIDADLEMAFARKASCONDKKT)=
# # 4.4 Dualidad, lema... | libro_optimizacion/temas/IV.optimizacion_en_redes_y_prog_lineal/4.4/Dualidad_lema_de_Farkas_condiciones_KKT_de_optimalidad.ipynb |
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# # Autoscaling Seldon Deployments
#
# ## Prerequisit... | examples/models/autoscaling/autoscaling_example.ipynb |
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# + [markdown] id="3tHmSlPKjSDR"
# # AU Fundamentals o... | notebooks/AUP110_W11_Exercise.ipynb |
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import numpy as np
import gym
from keras.models im... | balancebot-project/Wall_E.ipynb |
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import cv2
impor... | notebooks/visualize_localization_robotcar.ipynb |
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# %load_ext autoreload
# %autoreload 2
# +
from time import ... | metadata-translation/notebooks/mongo_etl_demo.ipynb |
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# # BNN指标
#
#... | CertifiableBayesianInference/FCN_Experiments/analysis_BBB_2.ipynb |
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# <img alt="QuantRocket logo" src="https://www.quantro... | dead_cat_drop/Part1-Historical-Data-Collection.ipynb |
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# # Computational Experiments on Maass Forms
# +
T... | Experimentation/maass_computational_experiments.ipynb |
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from rescaleforvis import rescale_for_vis
#how to... | use_cases.ipynb |
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# + id="ZVlMmNhkBEaM"
import sys, os
import pandas as ... | other ipynb models/Live_video_detection.ipynb |
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# # Keras Functional API
# ### Callable layers
import tensorf... | notebooks/2.2 callable_layers.ipynb |
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import csv
from datetime import datetime
i... | .ipynb_checkpoints/indeed-web-scraper-checkpoint.ipynb |
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# ## Linking Google Colab to your GitHub page
# **You... | 0 - First_steps.ipynb |
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# + [markdown] _uuid="f2156d1dd26a1243e18512002e10872c... | mnist/kernel.ipynb |
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# # Django based movie recommender system
# Author: Xi... | Algorithm- Item_based & SVD.ipynb |
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import pandas as pd
import numpy as np
df = pd.Da... | pandas_dataFrame_transform.ipynb |
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# # 011 Network Analysis Result Summary
# * Analyze di... | scripts/analysis/011_network_analysis_summary.ipynb |
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import tensorflow as tf
import matplotlib.pyplot a... | Course-2/exercise-3-RockPaperScissors.ipynb |
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# + [markdown] slideshow={"slide_type": "slide"}
# # **Demo o... | Visualization/AFW_Display_Demo.ipynb |
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# <h1 align="center">탐색적 분석 : 왕자의 게임</h1>
# <img sr... | Chapter01/game_of_thrones_eda.ipynb |
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# # Отбор признаков и снижение размерности
# %matplot... | Courses/IadMl/IntroToDataAnalysis/seminars/DimensionalityReduction_filled.ipynb |
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# # web
#
# get pages
#
import pandas as pd
import nu... | 3_prg/4_py/84_dat/pan16web.ipynb |
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# + nbsphinx="hidden"
# HIDDEN CELL
import sys, os
# ... | docs/data_sources.ipynb |
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# # Exploratory data analysis on unlabeled data
# We d... | ch_11/1-EDA_unlabeled_data.ipynb |
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# <div class="clearfix" style="padding: 10p... | examples/opencv_movie.ipynb |
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# name: python385jvsc74a57bd08ce15fe1d81c51f8b77db41b1b6a0dbb73a431a1... | ibm-applied-data-science/week3_toronto.ipynb |
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# # Build and Train
#
# Now we're ready to begin building our sen... | course/project_build_tf_sentiment_model/02_build_and_train_lstm_example.ipynb |
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# # Importing librariys
import numpy as np
from sklea... | ML_Basic_LinearRegressionWithSampleData_04.ipynb |
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import numpy
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def neuralnet(m1, m2, w1, w2, b):
... | python/Petal Prediction.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
#... | coding-club.ipynb |
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# # 04 Train Model
# Used the cleaned, scaled, and nor... | 04 Train Model.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="u1T7C-3Q_p9M" outputId=... | eda/jose/Ejemplo_Mapbox.ipynb |
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# ### MRC Knockoffs Primer
# Given a set of $p$ featu... | docs/master/mrcknock.ipynb |
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# # Writing your own component definition file
from t... | ai-pipeline/concepts/04-Components.ipynb |
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import pandas as pd
import warnings
war... | CLTV.ipynb |
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import numpy as np
import pandas as pd
import matplotl... | Statistical_Modeling.ipynb |
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# # Fairness
# > _Lorem ipsum bla bla bla_
from IPyt... | fairness.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="... | making_citydataset_textures.ipynb |
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# + [markdown] colab_type="text"
# # [deplacy](https://koichiyasuoka.github.i... | doc/pl.ipynb |
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# ## Week 3: 3.1 Database
# **by <NAME> (Intern at Chi... | Database/ Week 3- 3.1 Database - Assignment.ipynb |
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# %matplotlib inline
#
# Auto-scheduling a Neural Net... | _downloads/5e4e499c097b16a90c517e630502253a/tune_network_mali.ipynb |
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# # Chapter 11 : Interfacing with External Environment... | Chapter11/ch-11.ipynb |
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# description: Python 3 pro... | 00_Intro_to_Jupyter/00_Intro_to_Jupyter.ipynb |
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import numpy as np
import pandas as pd
import re
# Re... | notebooks/.ipynb_checkpoints/create_all_data-checkpoint.ipynb |
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# # COVID-19 Pandemic Alert In Tirupati
# ## Introduc... | .ipynb_checkpoints/Capstone Project - The Battle of Neighborhoods (Week 2)-checkpoint.ipynb |
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# name: python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east... | pytorch/data_parallel/maskrcnn/pytorch_smdataparallel_maskrcnn_demo.ipynb |
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# # Introduction to Feature Engineering
#
# **Learning... | courses/machine_learning/deepdive/03_model_performance/a_feature_engineering_dnn.ipynb |
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# # QA masks for MODIS NDVI time series from Google Ea... | docs/examples_dev/MODIS_Time_Series.ipynb |
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# # *Unsupervised learning: Latent Dirichlet allocatio... | gensim_pyldavis/LDA_topic_modeling_sansjava.ipynb |
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# # Reading and Writing files
#
# So far, we have type... | Lesson 6 - File access in Python3.ipynb |
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import os
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from videoprops imp... | lab/video-information.ipynb |
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import os
import sys
import time
import n... | Notebooks/parameterized_dyn_systems.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Pet_Image_Classification.ipynb |
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import pandas as pd
import numpy as np
import MySQLdb
... | MeerKAT_LSPs/MHONGOOSE.ipynb |
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# + [markdown] id="Gzr1fe7mRn7a" colab_type="text"
# # Linear Models
#
# I'll... | Linear_models_after_data_exploration.ipynb |
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... | examples/ATL06Retrieval.ipynb |