code stringlengths 38 801k | repo_path stringlengths 6 263 |
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# # Predicting Closing Price
import yfinance as yf
#... | Predict Closing Price.ipynb |
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# # Homework Plotting
# ## Problem 1
# Creat a plot o... | python/HW09.ipynb |
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# Discretization is an essential preprocessing method ... | notebooks/Solutions/DATAPREP_06_Discretization_Lab_Solution.ipynb |
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import category_encoders as ce
import pandas as pd
imp... | Sinclair_permutation_importances_partial_dependence_plots_assignment.ipynb |
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# + [markdown] toc=true
# <h1>Table of Contents<span c... | notebooks/EDA.ipynb |
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import pandas as pd
import seaborn as sns
... | baseline and initial data/BoxPlot.ipynb |
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# + [markdown] colab_type="text" id="skBHazUjfEwt"
# #... | qutip-notebooks-master/examples/tomography-resonator-MLE.ipynb |
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# + [markdown] id="yUFqTJut2hqm" colab_type="text"
# #data make
# + id="gsUZ... | Decrease_sensors.ipynb |
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# <div class="alert alert-success">
# <b>Author</b... | CSE_313_Numerical Methods/Lecture_11_19.08.2020.ipynb |
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# # Convert Jpeg to png
from glob import glob
impor... | script/2_convert_jpeg2png.ipynb |
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# # Lab 03 : LeNet5 architecture - exercise
# For Goo... | codes/labs_lecture08/lab03_lenet5/lenet5_exercise.ipynb |
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// ## Clustering
// In this exercise, you... | DataAnalyticsWithSpark/Unsupervised/Scala Clustering.ipynb |
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# # More Resources
#
# "Wait, I feel like the prior pa... | content/03/01d_NumpyResources.ipynb |
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# # Day 13
#
# First day I had to cheat and look for h... | 2020/Day-13.ipynb |
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# # Style transfer using VGG16 network
#
# * `A Neural... | week05/02_style_transfer.ipynb |
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# # [실습] Multilayer Perceptron(MLP)로 숫자 분류기 구현하기
# - M... | Lecture_Note/03. CNN Application/01. Numeric classifier using Multilayer Perceptron(MLP) .ipynb |
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# # Assignmnent
#
# 1. Create a random arr... | assignments/essential/assignment_09.ipynb |
# +
"""
Generate Traffic Light
"""
# import python randomint package
import random
# generates a random number from 1 to 3
randn = random.randint(1, 3)
if randn == 1:
print('red')
if randn == 2:
print('green')
if randn == 3:
print('yellow')
# if 1, print 'red'
# if 2, print 'green',
# if 3, print 'yellow... | pset_conditionals/random_nums/solution/nb/p1.ipynb |
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# # OpenVINO example with Squeezenet Model
#
# This no... | examples/models/openvino_imagenet_ensemble/openvino_imagenet_ensemble.ipynb |
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# ## Prediction
# +
import numpy as np
import pandas ... | drug_set2/.ipynb_checkpoints/drug2_prediction_separate01_23_4_quadratic-checkpoint.ipynb |
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# %ls
# %cd Chapter_1/
# %ls
from sklearn import da... | Mathine_Learning/Learning_in_Action/.ipynb_checkpoints/Chapter_1-checkpoint.ipynb |
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# 
#
# #... | YoungCodersNotebook.ipynb |
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# ## 9. Minimum-maximum módszer, MinMaxMethod
#
# A mi... | doc/hu_minmax.ipynb |
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# # Rainbow with Quantile Regression
# ## Imports
# ... | src/model/pytorch/21-RL/DeepRL-Tutorials/10.Quantile-Rainbow.ipynb |
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# %matplotlib inline
from matplotlib import pyplot as ... | fig_4/high-e-stats.ipynb |
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# # Chapter 1
import random
import time
import gym
... | Chapter01/Chapter 1.ipynb |
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# 데이터셋을 좀 더 쉽게 다룰 수 있도록 유용한 도구로서 torch.utils.data.Data... | 02DNN/04Custom_Dataset_sol.ipynb |
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import tensorflow as tf
print(tf.__version__)
from ... | aug_tiny_imagenet.ipynb |
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# %pylab inline
import sacc
import lmfit
import emcee... | doc/notebooks/LSSLike_tests.ipynb |
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# ## Expanding DRS Bundles - SRA Example
# Aside from... | notebooks/drs/Bundle expansion.ipynb |
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# + [markdown] slideshow={"slide_type": "-"}
# # Recur... | iRONS/Notebooks/A - Knowledge transfer/3.a. Recursive decisions and multi-objective optimisation.ipynb |
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""" Implementing decoder"""
"""Imports"""
import ... | Translate.ipynb |
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# # The... | notebooks/bytopic/python-basics/00_introduction.ipynb |
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# # Demonstration of QAD variants in 2D pa... | plot_QAD_landscapes.ipynb |
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# Helpful packages for working with images and fac... | introductory-tutorials/intro-to-julia/compressing_an_image.ipynb |
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# ## Preprocessing - No Lemmatization and No stemming ... | CountVectorizer with SVM, ADA Boost and Random Forest/CountVectorizer- No Lemma-No stem- Removing SW.ipynb |
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# # Deep Markov Model
#
# ## Introduction
#
# We're g... | tutorial/source/dmm.ipynb |
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import random
import matplotlib.pyplot as plt
if __nam... | random_number_generator.ipynb |
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# + [markdown] origin_pos=0
# # 梯度下降
# :label:`sec_gd`
#
# 尽管*梯度下降*（gradient descent）很少直接用于深度学习，
# 但了解它是理解下一节随机梯度下降算法的关键。
# 例如，由于学习率过大，最优化问题中可... | d2l/chapter_optimization/gd.ipynb |
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# # Announcements
# - __Please familiarize yourself wi... | Lectures/Lecture 21/Lecture21_LA_stability_eigen.ipynb |
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# # ETL
# ### Cargar Librerias
import numpy as np
im... | 2_Train_Model/1_ETL.ipynb |
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# + id="urkaWe-kSPNy" colab_type="code" outputId="980681fa-eacd-407b-9daa-5a9... | music_genre_classification.ipynb |
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# # 第5章 深層学習に基づく統計的パラメトリック音声合成
#
# [
baseball2<-read.table("ba... | Code/Chpter4.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
#... | homework01/homework_modules.ipynb |
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# ... | pset_linear_regression.ipynb |
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# ### 观察新闻语料数据特征
fname = 'F:\\NLP_CV\\lec\\7lesson\\s... | nlp-backyard/assignment07.ipynb |
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import pandas as pd
import numpy as np
import sys
impo... | MultiskillConverter/program/multiskill_converter.ipynb |
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# # RMSProp算法
#
# 我们在[“AdaGrad算法”](adagrad.ipynb)一节中提到，因为调整学习率时分母上的变量$\boldsymbol{s}_t$一直在累加按元素平方的小批量随机梯度，所以目标函数自变量每个元素的学习率在迭代过程中一直在降低（或不变）。因此... | chapter_optimization/rmsprop.ipynb |
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# # Getting started with Azure Cosmos DB's API for MongoDB and... | Notebooks/PySpark/Synapse Link for Cosmos DB samples/E-Commerce/spark-notebooks/pyspark/01-CosmosDBSynapseMongoDB.ipynb |
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import requests,re
url="https://study-ccna.com/classes... | DAY 16 ASSIGNMENT.ipynb |
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# # Inference pipeline
#
# Created by: <NA... | notebooks/processing/inference.ipynb |
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# # Goals
#
#
# ### Learn how to change number of ... | study_roadmaps/1_getting_started_roadmap/5_update_hyperparams/3_training_params/2) Change display params.ipynb |
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# + deletable=true editable=true
# %matplotlib noteboo... | examples/EnvironmentGeneration.ipynb |
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# + [markdown] slideshow={"slide_type": "skip"... | uci-pharmsci/lectures/docking_scoring_pose/docking.ipynb |
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# # Welcome to Provis!
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#
# This is an ex... | examples/dynamic_example.ipynb |
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from attacksplitnn.splitnn import Client, Server, Spli... | Attack_SplitNN/examples/Shredder.ipynb |
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# %ma... | climate_starter.ipynb |
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# + [markdown] tags=[]
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# # Advanced Feature Engineering in Keras
#
# **Learn... | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/keras_adv_feat_eng-lab.ipynb |
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# [Source](https://www.dataquest.io/blog/pandas-python... | lectures/Week 05 - Data Processing and Visualization Part 2/02.a - Pandas Data analysis Part 1.ipynb |
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-- # Chapter 05 - Statistics
--
-- +
imp... | notebooks/05_statistics.ipynb |
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# ## Advanced Lane Finding Project
#
# The goals / ste... | examples/example.ipynb |
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# # 深度概率编程
#
# ## 概述
#
# 深度学习模型具有强大的拟合能... | tutorials/notebook/apply_deep_probability_programming/apply_deep_probability_programming_bnnlenet5.ipynb |
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# ### Task: Explore the Datasets and Seggregate the fo... | Filtering_playersByPosition.ipynb |
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# URL: http://bokeh.pydata.org/en/latest/docs/gallery/step_chart.html
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# Most examples work across multiple plotting backends, this example i... | examples/gallery/demos/matplotlib/step_chart.ipynb |
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# # Softmax 回归的简洁实现
#
# 我们在[“线性回归的简洁实现”](linear-regression-gluon.md)一节中已经了解了使用 Gluon 实现模型的便利。下面，让我们再次使用 Gluon 来实现一个 softmax 回归模型。首先导入本节实现所需的包或... | chapter_deep-learning-basics/softmax-regression-gluon.ipynb |
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# **Technical Note**:
# - Install <code>deap</code> in... | weighting/GA_deap_02.ipynb |
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# # Machine Learning Engineer Nanodegree
# ## Unsuperv... | customer_segments/customer_segments.ipynb |
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from scrapy import Selector
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# ## Are comments using profanity doomed t... | Analysis of a Data Set using API.ipynb |
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# Problem statement: predicting turbine energy yield (... | Assign_ANN_gas_turbines.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
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# # Example of creating an SVG diagram
# The ProgramA... | Documentation/Examples/html/.ipynb_checkpoints/ProgramAnalysis_Example-checkpoint.ipynb |
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# ## Code used to train the RapidEye regular models
#... | Landslide_Segmentation_with_U-Net_Landslide_Segmentation_with_U-Net:_Evaluating_Different_Sampling_Methods_and_Patch_Sizes/notebooks/Training_regular_RapidEye.ipynb |
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# ### Author: <NAME>
# ### Reg No: 20MAI0044
# ### Dee... | Lab_Assignment_2/Assignment2_2.ipynb |
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# # Basic linear algebra in Julia
# Author: <NAME>... | 10 - Basic linear algebra.ipynb |
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# imports
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# # Adaptive PDE discretizations on cartesian grids
# ... | TP7/NonlinearMonotoneSecond2D_Exo.ipynb |
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# ## 加载模型
#
import os
GPUID='0'##调用GPU序号
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# # Rolling Windows
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# ## Pandas.DataFrame.rolling
#
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# ### Logistic discrimination
load("PCA.rda")
load("DP.rda")
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