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# ## Well near a straight river
# %matplo... | notebooks/line_sink_well_sol.ipynb |
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# ## Exercise 1.1
# In this exercise we will use the ... | labs/notebooks/linear_classifiers/exercises.ipynb |
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# # Reducing variance by removing between categories v... | notebooks/removing_variance_between_categories.ipynb |
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# # Praca domowa 2
# ### <NAME>
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import numpy as n... | src/PracaDomowa2.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Map.ipynb |
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# <a href="https://colab... | sine_wave.ipynb |
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# # Ensemble Methods Applied
#
# Agenda:
# - Review co... | Phase_3/Ensemble_Methods_Applied.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | ML/Assignment4/.ipynb_checkpoints/Assignment4-checkpoint.ipynb |
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# ### 2018/2019 - Task List 4
#
# [Book](https://raw.g... | l4/Task List 4.ipynb |
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# default_exp models.ResNet
# -
# # ResNet
#
# > ... | nbs/101_models.ResNet.ipynb |
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# + [markdown] pycharm={"name": "#%% md\n"}
# # Hello ... | hello_world.ipynb |
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import numpy as np
import pandas as pd
import matplotl... | NaiveBayes.ipynb |
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import numpy as np
import pandas as pd
#creating a d... | pandas_and_seaborn.ipynb |
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# Import Libraries
import requests
from requests.a... | Code/.ipynb_checkpoints/nj-housing-scrape-checkpoint.ipynb |
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# %matplotlib inline
from matplotlib import style
styl... | climate_analysis.ipynb |
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import import_ipynb
import epirecipes
# ## SIR model ... | sir-r.ipynb |
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# + [markdown] id="3CF2edFAI4Uj"
# <... | t81_558_class_11_01_huggingface.ipynb |
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# # SpMAX4_Bh(s) Categorization Analysis of 60 Experim... | SpMAX4Bhs_discrimination_performance_analysis/SpMAX4Bhs_classification_performance.ipynb |
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# + [markdown] slideshow={"slide_type": "slide"}
# # B... | .ipynb_checkpoints/0_course_introduction-checkpoint.ipynb |
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import pandas as pd
import numpy as np
# %matplotlib i... | indoorLocalizationModel/.ipynb_checkpoints/NN_18pts_smoothOnly-checkpoint.ipynb |
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# ## Sample 4.6 Run a Gibbs sampler using ... | sample4.6_gibbs_sampler.ipynb |
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import pandas as pd
from matplotlib import pyplot as p... | pluralsight/data_visualization_YK_Sugi/Module 5 - Time Series/Practice Problem 2 - Compare Population Growth in the US and China.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | pytorch/FeedForwards_PyTorch.ipynb |
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import cv2
import mediapipe as mp
import matplotli... | 01_mediapipe/06_hand_area tracking_recording_and_representation_shared_version.ipynb |
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import math
# import pandas_datareader as web
import t... | jupyternb/Machine Learning Foundations.ipynb |
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# <h1>Parameters in VCCA:</h1>
# <ul>
# <li><b>x_view<... | Groups/Group_ID_40/VCCA/VCCA Documentation.ipynb |
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# Recurrent Neural networks
# =====
# These lessons a... | Deep_Learning/RNNs/7.1 RNN and LSTM.ipynb |
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# # Ejercicio 1 - Heladería
#
# Se quiere calcular la ... | tp2/Ejercicio1.ipynb |
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# + [markdown] azdata_cell_guid="4f4ff8a6-8c23-47d4-9088-bf170e6716e4"
... | ActiveDirectoryModuleNotebook/Active Directory Module Notebook.ipynb |
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# # 3.a xarray
#
# ![xarray logo](images/xarray_logo.p... | Tutorial/03a_xarray_introduction.ipynb |
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import numpy as np
from sympy import Matrix
import cop... | docs/simulation/Interferometer.ipynb |
# -*- coding: utf-8 -*-
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# + tags=["context"] dc={"key": "4"} run_control... | Degrees That Pay You Back/notebook.ipynb |
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# + [markdown] colab_type="text" id="view-in-github"
# <a href="https://colab... | tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial1.ipynb |
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# # Imports
from bs4 import BeautifulSoup... | notebooks/.ipynb_checkpoints/ETL_verification-checkpoint.ipynb |
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# + [markdown] id="X4cRE8IbIrIV"
# If you're opening t... | examples/text_classification.ipynb |
# # A full training
# Install the Transformers and Datasets libraries to run this notebook.
# !pip install datasets transformers[sentencepiece]
# !pip install accelerate
# To run the training on TPU, you will need to uncomment the followin line:
# # !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.goo... | course/chapter3/section4.ipynb |
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// # P1_2 sandbox
// + dotnet_interacti... | samples/notebooks/P1_2 interactive/interactive P1_2.ipynb |
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# ## TensorFlow 2 - The Mandelbrot set
# In this exam... | examples/TensorFlow 2 - Mandelbrot.ipynb |
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# # Model clinical trajectories from longitudinal data... | notebooks/model_trajectories.ipynb |
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# # What is Python?
# * Python is an interpreted, high... | 0. INTRO OF PYTHON.ipynb |
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# # Nigerian Music ... | 5-Clustering/2-K-Means/notebook.ipynb |
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# # Checkpoint
# You could directly run cells below wi... | Traffic_Sign_Classifier_Test.ipynb |
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# # File,Character Encoding and Table Data File
#
#
# ... | notebook/Unit1-5-Files.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | SNLP.ipynb |
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# + [markdown] slideshow={"slide_type": "-"}
# # Explo... | notebooks/dense-dnn/dense-convolution.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Hilmi_BERTopic.ipynb |
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# # Predicting Titanic Survival
#
# From the descripti... | container_files/demos/Predicting Titanic Survival.ipynb |
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import matplo... | Adelaide-Temperature.ipynb |
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# # Nonnegative matrix factorization
#
# A derivative ... | examples/notebooks/WWW/nonneg_matrix_fact.ipynb |
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# # Part 2 of 2D Data Tracks: Histograms
#
# This page is primarily based on the following page at the Circos documentation site:
#
#
#
# - [3... | notebooks/2d_data_tracks_Part3.ipynb |
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# %matplotlib inline
morange = u... | notebooks/v4_color_evolution_snapshots.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Pavee/CRR_Model.ipynb |
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# ## AWS AI Service :
# Forecast Open API Service Fea... | Cloudmesh-OpenAPI/AWSForecast/AWS_Forecast.ipynb |
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# # Navigating the Web with Selenium
# *<NAME>*
#
# Se... | Section 5/NavigatingWebSelenium.ipynb |
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# + [markdown] slideshow={"slide_type": "s... | source/part1/Formulation.ipynb |
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# # Proposed model over the full dataset
#
# The full ... | 3.3.4-Full-dataset-model.ipynb |
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# # LSLGA Galaxy Pairs
#
# Identify galaxies *not* in ... | doc/lslga-galaxy-pairs.ipynb |
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# # 행렬의 고유치<br>Eigenvalues of a Matrix
#
#
# 임의의 $n \... | 60_linear_algebra_2/200_Eigenvalues_of_a_Matrix_PowerMethod.ipynb |
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from __future__ import pr... | training/Training_grey_r.ipynb |
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class FInput:
def __init__(self, name, x, y, ... | .ipynb_checkpoints/Fuzzy_logic-checkpoint.ipynb |
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# # Demo: Matrix Conditioning and Uncertai... | demos/analysis/ConditioningTest.ipynb |
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# # 【图像分类】使用飞桨全流程开发工具P... | 2282380.ipynb |
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# ## Serving of Tensorflow2 model with Konduit-serving... | 2_NewsGroupClassification/tf2-simple-model/serving/client.ipynb |
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# Modelo de McCulloch-Pitts
# ===
#
# **<NAME>**
# <... | 02-McCullochPitts.ipynb |
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# # Web-scraping Canadian Postal Codes
# We use Beauti... | Postal Codes of Canada.ipynb |
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# +
import tensorflow as tf
from tensorflow.examples.t... | 21-project/.ipynb_checkpoints/softmax_regression.py-checkpoint.ipynb |
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# # 07 - Serving predictions
#
# The purpose of the no... | 07-prediction-serving.ipynb |
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# Configure Jupyter so figures appear in the noteb... | code/hiv_model.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="BJZEwNTJ05hA" outputId=... | projects/Megaman Causal Scene Generation/shademan_vae (1).ipynb |
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# # Inferencing Iris XGBoost PMML Model using AI-Servi... | examples/AIServingIrisXGBoostPMMLModel.ipynb |
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import numpy as np
import matplotlib.pyplot as plt
fro... | measures_and_segmentation.ipynb |
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# # Tracking particles in brightfield
#
# **TL;DR** We... | examples/brightfield - m04 - 06A - ISF from traj.ipynb |
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import itertools
import json
import matplotlib as mpl
... | util/results_plotting_4_task.ipynb |
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from selenium import webdriver
from selenium.webdrive... | .ipynb_checkpoints/FinalScrape(Ihope!)-MorePulls_2-checkpoint.ipynb |
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import pandas as pd
from nltk.corpus import wordnet
im... | Preprocessing the Summaries.ipynb |
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# # XGBoost test
#
# This function handles evaluating ... | xgb_test/xgb_test.ipynb |
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# + id="M84cneEaqtlu" colab_type="code" colab={}
from google.colab import dri... | MNB.ipynb |
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# # THIS IS NOW OBS... | notebooks/Fit mass model.ipynb |
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# # Part 1. Authentication in a service
# ## 1.1. Wha... | [Spring 2021] Advanced Information retrieval/Week 2/HW/2021S-0304 GDrive magic.ipynb |
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# Given an array of size n, find the majority element.... | array_strings/ipynb/majority_element.ipynb |
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# # The median statistic function
import pybryt
# +... | demo/submissions/subm03.ipynb |
# -*- coding: utf-8 -*-
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# This animation illustrat... | SamplePlants.ipynb |
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# First we will initialize our filiBERTo tokenizer like... | course/bert_from_scratch/01b_load_and_train.ipynb |
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# # Starbucks Capstone Challenge
#
# # I. Project Int... | Starbucks_Capstone_notebook.ipynb |
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# # Dark matter electron scattering
# +
import numeri... | notebooks/DM-electron scattering.ipynb |
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import tensorflow as tf
# + pycharm={"name": "#%%\n"}... | demos/tensorflow2/gather.ipynb |
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# + [markdown] tags=[]
# # Configuration
#... | notebooks_binary_model/CREMA_13_normalized.ipynb |
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# # Grama: A Gammar of Model Analysis
#
# *Author*: <... | examples/trajectory_validation/trajectory_validation.ipynb |
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# 9-1
class Restaurant():
def __init__(self, n... | Chapter9.ipynb |
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# # MapMatching Example for enviroCar Tracks
# +
#... | examples/map_matching_example.ipynb |
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# # Exercise 8
# * Due: 3.3. at noon
# * Max points: 1... | Exercise 8.ipynb |
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# # Indexing, Slicing and Sub... | notebooks/02-index-slice-subset.ipynb |
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# ### How Are We Doing?
#
# In the last notebook, you ... | 3_How_Are_We_Doing.ipynb |
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# # Mean Based Blending
#
# <NAME>
#
# <https://xsong.... | uncertainty/averaging.ipynb |
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# %matplotlib inline
#
# Model Freezing in TorchScrip... | docs/_downloads/16b28c991d1ecd15e9c3baccf501a92e/torchscript_freezing.ipynb |
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# # EDA - BASIC Structure
import pandas as pd
import... | notebooks/EDA -Basic-Structure.ipynb |
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import requests
import json
url="https://centralindia.... | ML/SkinDisease.ipynb |
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# # Dataset
import json
import pandas as pd
import py... | git_notebooks/pickle_generator.ipynb |
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fname = path.expanduser('t... | pandas.ipynb |
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import os
import numpy as np
import pandas as pd
# **... | majority-vote.ipynb |