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<a href="https://colab.research.google.com/github/tushar-semwal/fedperf/blob/main/Santiago/Shakespeare/FedAvg.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# FedPerf - Shakespeare + FedAvg algorithm
## Setup & Dependencies Installation
```
%%cap... | github_jupyter |
# Visualizing data using matplotlib and seaborn
```
import pandas as pd, csv, os, re
import numpy as np
#from nltk.stem.porter import PorterStemmer # an approximate method of stemming words
#stemmer = PorterStemmer()
# FOR VISUALIZATIONS
import matplotlib, seaborn as sns
import matplotlib.pyplot as plt
# Visualizati... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
import pickle
data = pd.read_csv("diabetes-pima.csv")
data.head(10)
# to check if any null value is present
data.isnull().values.any()
## c... | github_jupyter |
This notebook can be run on mybinder: [](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Fai-courses%2Fautonomous_systems_ml/HEAD?filepath=notebooks%2F4_discriminant_analysis)
*Taken from scikit-learn example*
```
%matplotlib inline
```
#... | github_jupyter |
# Lesson 2 Demo 3: Creating Fact and Dimension Tables with Star Schema
### Walk through the basics of modeling data using Fact and Dimension tables. In this demo, we will:<br>
<ol><li>Create both Fact and Dimension tables<li>Show how this is a basic element of the Star Schema.
### Import the library
Note: An error ... | github_jupyter |
```
import random
import torch.nn as nn
import torch
import time
import math
import pickle
import pandas as pd
from pandas import Series, DataFrame
from pandarallel import pandarallel
pandarallel.initialize(progress_bar=True)
import sys
import json
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_... | github_jupyter |
# Web Track Overview
```
import pandas as pd
import numpy as np
import seaborn as sb
def cc_15_jsonl(f):
prefix = '/mnt/ceph/storage/data-in-progress/kibi9872/sigir2021/data-13-10-2020/cc15-relevance-transfer/'
threshold = 0.82
df = pd.read_json(prefix + f, lines=True)
df['urlMatches'] = df['urlMatche... | github_jupyter |
# Custom statespace models
The true power of the state space model is to allow the creation and estimation of custom models. This notebook shows various statespace models that subclass `sm.tsa.statespace.MLEModel`.
Remember the general state space model can be written in the following general way:
$$
\begin{aligned}... | github_jupyter |
# VacationPy
----
#### Note
* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think throug... | github_jupyter |
# Parameter Space
To run a DYNAMITE model, one must specify a number of parameters for the gravitational potential. The aim of this notebook is to demonstrate how to specify these parameters and to highlight features that we have implemented in order to help you explore parameter space.
We'll start as before by read... | github_jupyter |
```
%matplotlib inline
```
# Solar Data Processing with Python Part II
Now we have a grasp of the basics of python, but the whole reason for downloading python in the first place was to analyze solar data. Let's take a closer look at examples of solar data analysis.
We will be using SunPy to access solar data. SunP... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib notebook
import numpy as np
import pandas as pd
from scipy import interpolate
import pickle
import xmeos
from xmeos import models
from xmeos import datamod
CONSTS = models.CONSTS
analysis_file = 'data/analysis.pkl'
with open(analysis_file, 'rb') as f:
analysis = pic... | github_jupyter |
# Challenge 4: Convolutional Neural Networks
Create a Convolutional Neural Network (a deep learning architecture) to classify the gear data. The architecture or design should contain a mix of layers such as convolutional and pooling.
Train a model on the training dataset using the deided architecture. You may have to i... | github_jupyter |
```
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Embedding, LSTM
from keras.layers import Conv1D, Flatten, MaxPooling1D, GlobalMaxPooling1D
from keras.preprocessing import sequence, text
import numpy as np
import os
import json
```
# Workarou... | github_jupyter |
<a href="https://colab.research.google.com/github/predatorx7/borrows/blob/master/pyai/5_A_Water_Jug.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
![waterjugg.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICA... | github_jupyter |
<a href="https://colab.research.google.com/github/tjwei/NCTU_DeepLearning/blob/master/tf2_tutorial/02_tf2_Basics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install -U tensorflow-gpu
import tensorflow as tf
tf.__version__
matrix1 = tf.c... | github_jupyter |
# Clean-Label Feature Collision Attacks on a PyTorch Classifier
In this notebook, we will learn how to use ART to run a clean-label feature collision poisoning attack on a neural network trained with PyTorch. We will be training our data on a subset of the CIFAR-10 dataset. The methods described are derived from [this... | github_jupyter |
```
import joblib
import pandas as pd
from sklearn.datasets import load_breast_cancer, load_iris, load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.model_selection import GridSearchCV
```
# Process Dataset Breast Ca... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# !git pull
import tensorflow as tf
import malaya_speech
import malaya_speech.train
from malaya_speech.train.model import fastspeech2
import numpy as np
_pad = 'pad'
_start = 'start'
_eos = 'eos'
_punctuation = "!'(),.:;? "
_special = '-'
_letters = 'ABCDEFGHIJKLMN... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
# Pooled Classification
A common workflow with longitudinal spatial data is to apply the same classification scheme to an attribute over different time periods. More specifically, one would like to keep the class breaks the same over each period and examine how the mass of the distribution changes over these classes i... | github_jupyter |
# Determining the proton content with a quantum computer
Code at: https://github.com/qiboteam/qibo/tree/master/examples/qPDF.
In this tutorial we show how to use the `qPDF` model implemented in Qibo to create a set of Parton Distribution Functions (PDFs), parameterized by a variational quantum circuit. In the context... | github_jupyter |
> Copyright 2020 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
>
> Unless required by applicable law or agre... | github_jupyter |
# Visualization
## Matplotlib
<div style="clear:both"></div>
</div>
<hr style="height:2px;">
<div style="float:right; width:250 px"><img src="https://matplotlib.org/_static/logo2.png" alt="NumPy Logo" style="height: 150px;"></div>
## Objectives
1. Create a basic line plot.
1. Add labels and grid lines to the plo... | github_jupyter |
# Load and Process models
This script will load the M models in the collection using cobrapy, and convert them to a normalized format. They will also be exported to the "mat" format used by the COBRA toolbox.
This requires [cobrapy](https://opencobra.github.io/cobrapy) version 0.4.0b1 or later.
```
import os
import ... | github_jupyter |
# Credential Scan on Azure Log Analytics
__Notebook Version:__ 1.0<br>
__Python Version:__ Python 3.8 - AzureML<br>
__Required Packages:__ No<br>
__Platforms Supported:__ Azure Machine Learning Notebooks
__Data Source Required:__ Log Analytics tables
### Description
This notebook provides step-by-step ins... | github_jupyter |
# Mask R-CNN - Train on Shapes Dataset
This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Neura... | github_jupyter |
# Print in python
```
import os
from IPython.core.display import HTML
def load_style(directory = '../', name='customMac.css'):
styles = open(os.path.join(directory, name), 'r').read()
return HTML(styles)
load_style()
```
## Print Statement
The **print** statement can be used in the following differen... | github_jupyter |
```
import numpy as np
import pandas as pd
import os
import torch
import torchvision
import torchsample
import psycopg2
import random
import re
import time
import csv
import copy
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.dat... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df1 = pd.read_csv(r"E:\EYE DATASET\Training_Labels.csv")
df1
df1.columns
DR = DIABETIC RETINOPATHY
ARMD = AGE RELATED MACULAR DEGENRATION
import os
import random
import cv2
import matplotlib.pyplot as plt
df = pd.read_csv("full_df.csv")
df
df1
c... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# ADM Quantities in terms of BSSN Quantities
## Author: Zac... | github_jupyter |
# MNIST Example
This demo is an adaption of our [first `MNIST` themed demo](mnist_example.ipynb), which computes saliency maps for the models' actual prediction.
Here, we only analyze one input sample, but compute saliency maps for all of the model's output neurons, one at a time.
# Imports
```
import warnings
warn... | github_jupyter |
# কোয়ান্টাম কম্পিউটারে ক্লাসিক্যাল কম্পিউটেশন
## বিষয়বস্তু
1. [Introduction](#intro)
2. [Consulting and Oracle](#oracle)
3. [Taking Out the Garbage](#garbage)
## 1। ভূমিকা<a id="intro"></a>
কোয়ান্টাম গেটগুলির একটি সর্বজনীন সেট থাকার একটি পরিণতি হল যে কোনও ক্লাসিক্যাল গণনা পুনরুত্পাদন করার ক্ষমতা। আমাদের কেবল বুল... | github_jupyter |
# Padding Oracle
- When a decrypted CBC ciphertext ends in an invalid pad the web server returns a 403 error code (forbidden request). When the CBC padding is valid, but the message is malformed, the web server returns a 404 error code (URL not found).
```
http://crypto-class.appspot.com/po?er="your ciphertext here"
`... | github_jupyter |
# Widget Events
## Special events
```
from __future__ import print_function
```
The `Button` is not used to represent a data type. Instead the button widget is used to handle mouse clicks. The `on_click` method of the `Button` can be used to register function to be called when the button is clicked. The doc strin... | github_jupyter |
# Hi, Are you in Google Colab?
In Google colab you can easily run Optimus. If you not you may want to go here
https://colab.research.google.com/github/ironmussa/Optimus/blob/master/examples/10_min_from_spark_to_pandas_with_optimus.ipynb
Install Optimus all the dependencies.
```
import sys
if 'google.colab' in sys.mod... | github_jupyter |
### Outlier Detection using autoencoders-First version
### Using the whole data
#### Edgar Acuna
#### Abril 2021
```
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
import keras
from keras.models import Model, load_model
from keras.layers import Input, Dense
from keras.callbacks import Model... | github_jupyter |
# Intrusion detection on NSL-KDD
This is my try with [NSL-KDD](http://www.unb.ca/research/iscx/dataset/iscx-NSL-KDD-dataset.html) dataset, which is an improved version of well-known [KDD'99](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html) dataset. I've used Python, Scikit-learn and PySpark via [ready-to-run J... | github_jupyter |
# Lecture 2b: Introduction to Qiskit
**By Adam Fattal**
Welcome to the first practical lecture! In this lecture, we will be introducing qiskit, a package developed by IBM Quantum that allows one to simulate and run quantum circuits and much more! This lecture covers only the surface of Qiskit's functionality. For more... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
Jac_type = {1:'Sacado ', 0:'Analytic ', 2:'Numerical '}
format_line={'names': ('computation type', 'total time', 'time per sample'), 'formats': ('S30', 'f16', 'f16')}
vector= [16, 16, 16, 32, 32, 32, 32 ]
team=[2, 4, 8, 1, 2, 4, 8 ]
sacado_team_vector = {0:'2x16',... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D2_ModelingPractice/W1D2_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week1, Day 2, Tutorial 2
#Tutorial o... | github_jupyter |
# 1.Quickstart Tutorial on Spark NLP - 1 hr
This is the 1 hr workshop version of the entire training notebooks : https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/tutorials/Certification_Trainings/Public
an intro article for Spark NLP:... | github_jupyter |
# Week 11 - Regression and Classification
In previous weeks we have looked at the steps needed in preparing different types of data for use by machine learning algorithms.
```
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from sklearn import datasets
diabetes = datasets.load_diabetes()
# De... | github_jupyter |
```
import numpy as nmp
import pandas as pnd
import matplotlib.pyplot as plt
import pymc3 as pmc
import clonosGP as cln
%load_ext autoreload
%autoreload 2
%matplotlib inline
DATA = pnd.read_csv('data/cll_Rincon_2019_patient1.csv')
METRICS = pnd.read_csv('results/cll_Rincon_2019_patient1.csv')
nmp.random.seed(42)
pmc... | github_jupyter |
# Leverage
### Stupidity or genius?
Updated 2020-August-28.
* This notebook looks at what the last 92 years of daily S&P 500 data has to say about the now well-known intra-day leverage.
* Automatic reinvestment of dividends is assumed.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
... | github_jupyter |
## Exercise 5.03: Visually comparing different tile providers
Geoplotlib offers the possibility to switch between several providers of map tiles.
This means we can try out different map tile styles that fit our visualization.
In this exercise we'll take a look at how easily tile providers can be swapped.
#### ... | github_jupyter |
```
import keras
keras.__version__
```
# 透過二元分類訓練 IMDB 評論資料
二元分類或稱兩類分類可能是在機器學習中應用最廣泛問題。只要處理的問題只有兩個結果，就可以適用。在這個例子中，我們將根據 IMDB 評論的文本內容將電影評論分為「正面」評論和「負面」評論。
## 關於 IMDB Dataset 資料集
IMDB Dataset 是來自 Internet 電影數據庫 50,000 條評論文字。他們分為 25,000 條訓練數據和 25,000 條測試數據，每組皆包含包括 50％ 的負面評論和 50％ 的正面評論。
我們可以直接透過 Keras Datasets 函式庫載入已經... | github_jupyter |
```
%matplotlib inline
```
# Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
This is an example of applying :class:`sklearn.decomposition.NMF` and
:class:`sklearn.decomposition.LatentDirichletAllocation` on a corpus
of documents and extract additive models of the topic struct... | github_jupyter |
# Amazon SageMaker와 병렬로 SageMaker 분산 모델을 사용하여 모델 병렬화로 MNIST 훈련 작업 시작
SageMaker 분산 모델 병렬 (SageMaker Distributed Model Parallel, SMP)은 GPU 메모리 제한으로 인해 이전에 학습하기 어려웠던 대규모 딥러닝 모델을 훈련하기 위한 모델 병렬 처리 라이브러리입니다. SageMaker Distributed Model Parallel은 여러 GPU 및 인스턴스에서 모델을 자동으로 효율적으로 분할하고 모델 훈련을 조정하므로 더 많은 매개 변수로 더 큰 모델을 생성하여 예측 정확... | github_jupyter |
# The Fuzzing Book
## Sitemap
While the chapters of this book can be read one after the other, there are many possible paths through the book. In this graph, an arrow _A_ → _B_ means that chapter _A_ is a prerequisite for chapter _B_. You can pick arbitrary paths in this graph to get to the topics that interest you mo... | github_jupyter |
# Random Forest Classifier (RFC)
```
#Importing necessary libraries
import numpy as np
import pandas as pd
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.pipelin... | github_jupyter |
# Introduction to climlab and 1D grey radiation models
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import netCDF4 as nc
import climlab
```
# Validate climlab against analytical solution for 2-layer atmosphere
```
# Test in a 2-layer atmosphere
col = climlab.GreyRadiationModel(num_lev=2... | github_jupyter |
```
import math
import time
import util
import torch
import logging
import numpy as np
from torch import nn
import torch.optim as optim
from util import DataLoaderS
from model import *
from model_time_shift import A2GCN
logging.basicConfig(level=logging.INFO,#控制台打印的日志级别
filename='logging_ablatio... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
# Title Generation using Recurrent Neural Networks
I never know what I should title most things I have written. I hope that by using a corpus of titles, recurrent neural networks (RNNs) can write my titles for me.
I thought a fitting title to generate would be something within Machine Learning, so I used [Publish or P... | github_jupyter |
# DeepDreaming with TensorFlow
>[Loading and displaying the model graph](#loading)
>[Naive feature visualization](#naive)
>[Multiscale image generation](#multiscale)
>[Laplacian Pyramid Gradient Normalization](#laplacian)
>[Playing with feature visualzations](#playing)
>[DeepDream](#deepdream)
This notebook demo... | github_jupyter |
```
import os
import sys
import glob
import itertools
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from matplotlib.colors import ListedColormap
import numpy as np
import pandas as pd
np.random.seed(1234)
%matplotlib inline
```
# Load AML data
... | github_jupyter |
# BERT based NER experiment
> Tutorial author: 徐欣（<xxucs@zju.edu.cn>）
On this demo, we use `BERT` to recognize named entities. We hope this demo can help you understand the process of named entity recognition.
This demo uses `Python3`.
## NER
**Named-entity recognition** (also known as named entity identification, e... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
```
import time
from termcolor import colored
import torch
import torch.autograd.profiler as profiler
from modules.Swc2d import Swc2d
from modules.Dcls2dFull import Dcls2dFull
assert torch.cuda.is_available()
cuda_device = torch.device("cuda") # device object representing GPU
in_channels = 1
out_channels = 1
kerne... | github_jupyter |
# Setup
## Instructions
1. Work on a copy of this notebook: _File_ > _Save a copy in Drive_ (you will need a Google account).
2. (Optional) If you would like to do the deep learning component of this tutorial, turn on the GPU with Edit->Notebook settings->Hardware accelerator->GPU
3. Execute the following cell (click... | github_jupyter |
```
"""
I've never used SQL before, so this is just trial and error for loading things right now.
This is just for helping me think and plan the steps.
"""
print('')
import pandas as pd
# pd.set_option('display.max_columns', 30)
# pd.set_option('display.width', 10000)
# pd.set_option('display.expand_frame_repr', False... | github_jupyter |
# Tutorial
## [How to do Novelty Detection in Keras with Generative Adversarial Network](https://www.dlology.com/blog/how-to-do-novelty-detection-in-keras-with-generative-adversarial-network-part-2/) | DLology
This notebook is for test phase Novelty Detection. To Train the model, run this first.
```bash
python models.... | github_jupyter |
# Lab 07: Stack Applications
## Overview
For this assignment you will build on the stack data structure created in class to develop two distinct stack-driven applications.
Below is the completed stack implementation from class. While you needn't modify it for this assignment — indeed, all tests run on our end will *... | github_jupyter |
### Exercise 1: Create a Numpy array (from a list)
```
import numpy as np
lst1=[1,2,3]
array1 = np.array(lst1)
type(array1)
type(lst1)
```
### Exercise 2: Add two Numpy arrays
```
lst2 = lst1 + lst1
print(lst2)
array2 = array1 + array1
print(array2)
```
### Exercise 3: Mathematical operations on Numpy arrays
```
p... | github_jupyter |
# Convert OpenSN data to name,host,type,x,y,z,t,lum
Data downloaded from The Open Supernova Catalog https://sne.space on Aug. 20, 2019
```
import pandas as pd
import numpy as np
from astropy import units
from astropy.coordinates import SkyCoord, Distance
from astropy.cosmology import WMAP9
import datetime
import mat... | github_jupyter |
```
import random
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
length = 1000
cols = ["Q", "X", "Y", "Z"]
mu = 0
sigma = 5
import pingouin
lst_dct = {col:[] for col in cols }
for i in range(length):
lst_dct["Q"].append(50 + np.random.normal(mu, sigma))
lst_dct["X"].append(5 * ... | github_jupyter |
```
%matplotlib notebook
# test imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
print(f"The version of numpy is: {np.__version__}")
print(f"The version of pandas is: {pd.__version__}")
print(f"The version of scikit-learn is: {sklearn.__version__}")
```
You should see the v... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Goal" data-toc-modified-id="Goal-1"><span class="toc-item-num">1 </span>Goal</a></span></li><li><span><a href="#Var" data-toc-modified-id="Var-2"><span class="toc-item-num">2 </span>Va... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
## Use MLf... | github_jupyter |
<a href="https://colab.research.google.com/github/gyyang/neurogym/blob/master/examples/demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Exploring NeuroGym tasks
NeuroGym is a comprehensive toolkit that allows training any network model on ma... | github_jupyter |
```
from netCDF4 import Dataset
import netCDF4 as netcdf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib as mpl
import cmocean as cmo
#mapping
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io import shapereader
from cartopy.mpl.gridl... | github_jupyter |
# Lecture 1.1: Introduction to NumPy & pandas
This lecture, we are getting to know the two python libraries at the heart of data analysis: [NumPy](https://numpy.org/) and [pandas](https://pandas.pydata.org/).
**Learning goals:**
- Explain the difference between NumPy ndarrays, pandas Series, and pandas DataFrames
- ... | github_jupyter |
# MeshCat Animations
MeshCat.jl also provides an animation interface, built on top of the [three.js animation system](https://threejs.org/docs/#manual/introduction/Animation-system). While it is possible to construct animation clips and tracks manually, just as you would in Three.js, it's generally easier to use the M... | github_jupyter |
## Summary
----
## Imports
```
import concurrent.futures
import gzip
import os
import shutil
import subprocess
from collections import Counter
from pathlib import Path
import logomaker
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import proteinsolver
import pyarrow as pa
import pyarrow.par... | github_jupyter |
##### Copyright 2021 The TensorFlow Authors.
```
#@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 obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
**This notebook is an exercise in the [Python](https://www.kaggle.com/learn/python) course. You can reference the tutorial at [this link](https://www.kaggle.com/colinmorris/functions-and-getting-help).**
---
Functions are powerful. Try writing some yourself.
As before, don't forget to run the setup code below befor... | github_jupyter |
# Variational Multi-modal Recurrent Graph AutoEncoder
In this tuorial, we will go through how to run a Variational Multi-modal Recurrent Graph AutoEncoder (VMR-GAE) model for origin-destination (OD) matrix completion. In particular, we will demonstrate how to train the model and evaluate the completion results.
## Par... | github_jupyter |
```
!pip install -q transformers datasets sentencepiece coral_pytorch
import torch
import torch.nn as nn
from torch.functional import F
from datasets import Dataset
import transformers as ts
from transformers import AutoTokenizer , AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer... | github_jupyter |
# HW3: Variational Autoencoders
```
import torch
import torch.optim as optim
import torch.nn as nn
from torch.distributions import Normal
from itertools import chain
from torchlib.generative_model.autoencoder.vae import VAE
from torchlib.dataset.utils import create_data_loader
from torchlib.utils.distributions import ... | github_jupyter |
# Analyzing Real vs. Fake News Article Headlines 📰
Author:<br>[Navraj Narula](http://navierula.github.io)<br><br>
Data Source: <br>[Randomly-Collected Fake News Dataset](https://github.com/BenjaminDHorne/fakenewsdata1)<br><br>
Resources Consulted: <br>[Text Mining with R](http://tidytextmining.com)<br>[R: Text Classi... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@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 obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Questions [40marks]
* Q1 Who did spend most money for renting?
* Q2 Which room does make the most amount of income?
* Q3 How many time Jack Jones rent the room?
* Q4 How many time Claire Taylor rent each room?
* Q5 what is the total income of ALL rooms in June? Between 1st June 2018(inclusive) and 30th June 2018(incl... | github_jupyter |
We can use embedding comparison to measure the difference between the representations that neural network models learn. In this notebook, we compare the final-layer embeddings for Imagenet-trained VGG16, VGG19, and InceptionV3 models
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
os.environ["CUDA... | github_jupyter |
# Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are part of a team working to make mobile payments available globally, and are asked to build a deep learning model to detect fraud--whenever someone makes a payment, you w... | github_jupyter |
```
import numpy as np
import subprocess as sub
import SWAT_ReadOut as read
from SWAT_Manipulate import rteManipulator
from SWAT_Manipulate import bsnManipulator
from SWAT_Manipulate import gwManipulator
from SWAT_Manipulate import solManipulator
from SWAT_Manipulate import mgtManipulator
from SWAT_Manipulate import hr... | github_jupyter |
```
import numpy as np
%matplotlib tk
import matplotlib.pyplot as plt
import pickle
from sklearn import cluster
from sklearn import metrics
from sympy.solvers import solve
import sympy as sym
from scipy import optimize
class VelocityPlotter():
def __init__(self):
personNames = ['person1','person2','person... | github_jupyter |
## 一、比较类排序
### A、交换排序
#### a、冒泡排序
```
def bubble_sort(List):
n = len(List)
for i in range(n):
for j in range(0, n-i-1):
if List[j] > List[j+1]:
List[j], List[j+1] = List[j+1], List[j]
return List
arr = [1, 6, 9, 8, 2, 6, 7, 4, 3]
print(bubble_sort(arr))
```
#### b、快速排... | github_jupyter |
```
import numpy as np
import sys
from scipy.special import expit as sigmoid
training_data_path = sys.argv[1]
testing_data_path = sys.argv[2]
output_path = sys.argv[3]
# training_data_path = "../data/devnagri_train.csv"
# testing_data_path = "../data/devnagri_test_public.csv"
# output_path = "../data/nn/b/cs1160328"
... | github_jupyter |
This notebook can be executed in a notebook hosted in KubeFlow.
You can find instructions on how to deploy a KubeFlow cluster and how to access the the KubeFlow UI and the hosted notebooks here: https://www.kubeflow.org/docs/pipelines/pipelines-quickstart/
Please install KubeFlow Pipelines SDK using the following com... | github_jupyter |
Exercise 9 - Advanced Neural Networks
==========
There are many factors that influence how well a neural network might perform. AI practitioners tend to play around with the structure of the hidden layers, the activation functions used, and the optimisation function.
In this exercise we will look at how changing thes... | github_jupyter |
## Convolutional Neural Networks
---
In this notebook, we train an MLP to classify images from the MNIST database.
### 1. Load MNIST Database
```
from keras.datasets import mnist
# use Keras to import pre-shuffled MNIST database
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("The MNIST database ... | github_jupyter |
## EEML2019: ConvNets and Computer Vision Tutorial (PART I)
### Supervised classification, overfitting and inductive biases in convnets, and how to improve models through self-supervision
### by Viorica Patraucean (vpatrauc@gmail.com)
* Exercise 1: Implement and train a Resnet-50 classifier using supervised learning;... | github_jupyter |
```
cd /content/drive/My\ Drive/lane_follower
%tensorflow_version 1.x
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
print(tf.__version__)
import cv2
import time
import os
impor... | github_jupyter |
```
from datetime import date
import pandas as pd
import numpy as np
from datetime import datetime, timedelta, date
import geopandas as gpd
from pathlib import Path
import re
pd.options.display.max_columns = 100
# data from github jhu,import the lastest data from timeseries
df_Counties_confirmed = pd.read_csv(
"htt... | github_jupyter |
```
%matplotlib inline
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_pacf, plot_acf
sns.set_style('darkgrid')
df = pd.read_csv('... | github_jupyter |
# CMFGEN
Database from John Hillier’s CMFGEN, a radiative transfer code designed to solve the radiative transfer and statistical equilibrium equations in spherical geometry.
<div class="alert alert-info">
**Note:**
In this example, the data was downloaded from the [CMFGEN website](http://kookaburra.phyast.pitt.... | github_jupyter |
# Exploring different Symbol options in Magics
This notebook will help you discover lots of posibilities for plotting symbols on your maps in Magics.
Symbol plotting in Magics is the plotting of different types of symbols at selected locations. A symbol in this context is a number (the value at the location), a text ... | github_jupyter |
# Unity ML Agents
## Environment Basics
This notebook contains a walkthrough of the basic functions of the Python API for Unity ML Agents. For instructions on building a Unity environment, see [here](https://github.com/Unity-Technologies/ml-agents/wiki/Getting-Started-with-Balance-Ball).
### 1. Load dependencies
```
... | github_jupyter |
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