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# Generate data based on BrainWeb images
From brainweb, get:
- Two PET images
- FDG
- Amyloid
- Two MR acquisitions:
- T1
- T2
- A $\mu$-map
We're going to do various things with the images to create some data we can play around with! In image space, this includes:
- adding misalignment to some image... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/4_image_classification_zoo/Classifier%20-%20Cats%20vs%20Dogs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Table of contents
## Install Mon... | github_jupyter |
```
dataset = 'ImageNet16-120' # choose between 'ImageNet16-120', 'cifar10' and 'cifar100'
data_loc = '../datasets/ImageNet16' # choose ImageNet16 for ImageNet16-120 and cifar for cifar10 and cifar100
api_loc = '../datasets/NAS-Bench-201-v1_1-096897.pth'
n_runs = 500
n_init = 100
n_samples = 1000
batch_size = 256
train... | github_jupyter |
### TF-IDF model
```
# load all necessary libraries
import pandas as pd
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
pd.set_option('max_colwidth', 100)
```
#### Let's build a basic bag of words model on three sample documents
`... | github_jupyter |
# CS 410 Final Project: Aspect Based Sentiment Analysis
## Cristian Jansenson
## Peter Tsapatsaris
The application contained in this notebook performs two tasks: (1) parses customer reviews into specified aspects (e.g., "screen", "camera", "value"), such that phrases are extracted that are assocaited with the relev... | github_jupyter |
<!--NAVIGATION-->
< [How to Build Your Own Automated Trading System in Python With OANDA API](00.00 How to Build Your Own Automated Trading System in Python With OANDA API.ipynb) | [Contents](Index.ipynb) | [Rates Information](02.00 Rates Information.ipynb) >
# Setting Up
Before we start, there are a few parts that w... | github_jupyter |
# Interactive Plot
# Description
Allows interactive plotting of multidimensional data
```
%%checkall
# Library Imports
import os
import sys
import math
import colorsys
from dataclasses import dataclass
import unittest
import doctest
import pandas as pd
import numpy as np
from IPython.core.display import display
from... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import toimage
from keras.datasets import cifar10
import keras.utils as Kutils
import tensorflow as tf
from keras.backend import set_session,tensorflow_backend
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True,v... | github_jupyter |
# Comparison of PyBaMM and COMSOL Discharge Curves
In this notebook we compare the discharge curves obtained by solving the DFN model both in PyBaMM and COMSOL. Results are presented for a range of C-rates, and we see an excellent agreement between the two implementations. If you would like to compare internal variabl... | github_jupyter |
# System requirements
**Create virtual environment**
*python3.6 -m venv {Virtual Env Name} or {Absolute Path for Virtual Env}*
Ex: python3.6 -m venv azar or python3.6 -m venv Users/azar-0000/python/azar
**cd to venv directory**
*cd azar* or *Users/azar-0000/python/azar*
**Activate virtual environment**
*source ... | github_jupyter |
```
import calibrimbore as cal
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib notebook
filt = '/Users/rridden/Documents/work/code/source_synphot/source_synphot/passbands/Kepler/Kepler_k.dat'
filt = '/Users/rridden/Documents/work/code/source_synphot/source_synphot/passbands/TESS/tess.dat'
filt = '/Use... | github_jupyter |
```
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T
impo... | github_jupyter |
```
import dataclasses
from pathlib import Path
import nlp
import torch
import numpy as np
from transformers import BertTokenizerFast
from transformers import BertForSequenceClassification
from torch.optim.lr_scheduler import CosineAnnealingLR
try:
from apex import amp
APEX_AVAILABLE = True
except ModuleNotFo... | github_jupyter |
<img src='./img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='50%'></img>
<br>
<a href="./341_ltpy_Ozone_hole_case_study.ipynb"><< 341 - 2019 Antarctic ozone hole case study </a><span style="float:right;"><a href="./index_ltpy.ipynb">Index >></a></span>
# 3.4.2 Ozone hole case... | github_jupyter |
```
from datetime import datetime
import logging
import os
import random
import math
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
import torchvision
from torchvision import transforms
from mylogger import create_logger
from net.mob... | github_jupyter |
```
from IPython.core.display import HTML
from IPython.display import Image
HTML("""
<style>
.output_png {
display: table-cell;
text-align: center;
vertical-align: middle;
}
</style>
""")
```
# *Circuitos Elétricos I*
## Semana 1 - Convenções para aplicação das Leis de Kirchhoff na análise de circuitos
... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pylab as plt
from psi import application as app
from psi.controller.calibration import tone
from psi.controller.calibration.api import FlatCalibration, PointCalibration
from psi.controller.calibration.util import load_calibration, psd, psd_df, db, dbi, dbtopa, rms
from ... | github_jupyter |
# Complex Arithmetic
This is a tutorial designed to introduce you to complex arithmetic.
This topic isn't particularly expansive, but it's important to understand it to be able to work with quantum computing.
This tutorial covers the following topics:
* Imaginary and complex numbers
* Basic complex arithmetic
* Comp... | github_jupyter |
```
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import json
import numpy as np
import random
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import pearsonr
from sklearn.metrics import f1... | github_jupyter |
# Lunar Rock Classfication
```
# Installing latest Tensor-flow
!pip install -U tensorflow-gpu
# Mount for Google Colab
from google.colab import drive
drive.mount('/content/drive',force_remount=True)
# Dependencies
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from... | github_jupyter |
<a href="https://colab.research.google.com/github/BachiLi/redner/blob/master/tutorials/hello_redner_tensorflow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
This is the first tutorial of redner. In this tutorial we will load a 3D object and render... | github_jupyter |
# 🏷️ 🔫 Faster data annotation with a zero-shot text classifier
## TL;DR
1. A simple example for data annotation with Rubrix is shown: **using a zero-shot classification model to pre-annotate and hand-label data more efficiently**.
2. We use the new **SELECTRA zero-shot classifier** and the Spanish part of the **MLS... | github_jupyter |
# An Introduction to PCA with MNIST
_**Investigating Eigendigits from Principal Components Analysis on Handwritten Digits**_
1. [Introduction](#Introduction)
2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)
1. [Permissions and environment variables](#Permissions-and-environment-variables)
2. [D... | github_jupyter |
# Data Preparation
[Original Notebook source from *Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)
## Exploring `DataFrame` inform... | github_jupyter |
# Download Daymet
Daymet provides gridded meteorological data for North American at 1km spatial resolution with daily timestep from 1980 ~ present. [website](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1328) and [user guide](https://daac.ornl.gov/DAYMET/guides/Daymet_V3_CFMosaics.html)
Available variables:
| Var... | github_jupyter |
<figure>
<center>
<img src='https://raw.githubusercontent.com/Alex-Snow-School-Lab/Python-Basics/master/cover%20ppt.png' width = '800px'/>
</center>
</figure>
#**Assignment 3 (Mini project)>=5
**Assigned Date - 18 July 2020 (9:00 PM)**
**Self-Interactive Due Date - 24 July 2020 (11:59:59 AM)**
**Self-Paced Due Date ... | github_jupyter |
# TEXT MINING for PRACTICE
- 본 자료는 텍스트 마이닝을 활용한 연구 및 강의를 위한 목적으로 제작되었습니다.
- 본 자료를 강의 목적으로 활용하고자 하시는 경우 꼭 아래 메일주소로 연락주세요.
- 본 자료에 대한 허가되지 않은 배포를 금지합니다.
- 강의, 저작권, 출판, 특허, 공동저자에 관련해서는 문의 바랍니다.
- **Contact : ADMIN(admin@teanaps.com)**
---
## WEEK 03-2. 정적 페이지 수집하기: 영화댓글
- Python을 활용해 단순한 웹페이지에서 데이터를 크롤링하는 방법에 대해 다룹니다.
... | github_jupyter |
An algorithm is any well-defined computational procedure that takes a set of values as input and produces a set of values as output.
Tool to solve a well-specified computational problem.
Eg: sorting problem:
Input: sequence n numbers $a_1, a_2$, ..., $a_n$.
Output: Reordering of the sequence $a_1', a_2'$, ..., $... | github_jupyter |
```
import os
import sys
import itertools
import time
import multiprocessing as mp
```
# Test the limits:
Using tmpfiles named in local tempfiles (one for each paralell process)
## Attention Candidate Object - tempfile
## Attention Candidate adjacency-matrix | priority-forces-network
```python
"""
Extracted from:
... | github_jupyter |
<small><small><i>
All the IPython Notebooks in this lecture series by Dr. Milan Parmar are available @ **[GitHub](https://github.com/milaan9/01_Python_Introduction)**
</i></small></small>
# Python Variables and Constants
In this class, you will learn about Python variables, constants, literals and their use cases.
#... | github_jupyter |
Viscoplastic thermal convection in a 2-D square box: Tosi et al. 2015
=====
This series of notebooks generates benchmark models from the Tosi et al. (2015) in Underworld2. The Underworld2 results are then directly compared to the results from Table 2 of Tosi et al. (2015) the paper for each case. The notebooks are bro... | github_jupyter |
##### Copyright 2019 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 |
The concepts to be introduced in this notebook, such as the Ising model, simulated annealing, and the transverse Ising model, play an important role in today's quantum algorithms and quantum computing paradigms, including quantum annealing, the quantum approximate optimization algorithm, and quantum-enhanced sampling. ... | github_jupyter |
# Random Forest Model
The previous discussion on Decision Tree Model will give us intuition about a Random Forest Model. Random Forest focuses on feature selection and is a supervised learning algorithm. It can be used for both classification and regression. Random Forest helps us reduce the variance in decision tre... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
fro... | 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 |
# Descriptor evaluation on the RDNIM dataset
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
import brewer2mpl
from lisrd.evaluation.descriptor_evaluation import run_descriptor_evaluation
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
## Day reference
```
config = {
'name': 'rdnim',... | github_jupyter |
# Implementation of pytorch network
# Imports
```
import os
import torch
import mlflow
import numpy as np
import pandas as pd
import plotly.express as px
from itertools import product
from torch import nn, cuda, optim, no_grad
import torch.nn.functional as F
from torchvision import transforms
from torchvision.dataset... | github_jupyter |
# Quantum Kernel Training for Machine Learning Applications
In this tutorial, we will train a quantum kernel on a labeled dataset for a machine learning application. To illustrate the basic steps, we will use Quantum Kernel Alignment (QKA) for a binary classification task. QKA is a technique that iteratively adapts a ... | github_jupyter |
# GeoEnrichment
GeoEnrichment provides the ability to
* get facts about a location or area.
* information about the people, places, and businesses
* in a specific area or
* within a certain distance or drive time from a location.
* large collection of data sets including population, income, housing, consumer beh... | github_jupyter |
## Dependencies
```
import os
import sys
import cv2
import shutil
import random
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import multiprocessing as mp
import matplotlib.pyplot as plt
from tensorflow import set_random_seed
from sklearn.utils import class_weight
from sklearn.model_sele... | github_jupyter |
```
%load_ext watermark
%watermark -i -h -m -v -p numpy,torch,POLO -r -g -b
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
mode='nearest'
width = 32
base_levels = 1.61803
base_levels = 2
n_sublevel = 2
n_azimuth = 18
n_theta = 8
n_phase = 2
N_batch = 1024
minibatch = 64
#N_batch = 128
#N_batch ... | github_jupyter |
# Basic SoS workflows
* **Difficulty level**: easy
* **Time need to lean**: 10 minutes or less
* **Key points**:
* A forward-style workflow consists of numerically numbered steps
* Multiple workflows can be defined in a single SoS script or notebook
* Optional input and output statements can be added to change h... | github_jupyter |
# Decorator
---
## Introduction
在進階的python程式碼中，可以透過裝飾器對函式進行裝飾，讓函式達到特定的變化。為了要讓各位邁向更高的程式設計的境界，在本節將會介紹裝飾器的使用。
不過在正式進入裝飾器的介紹前，我們要先了解幾個概念:
1. 在python中，函式也是一個可被賦值給變數，可作為參數傳遞，也可作為其他函數的回傳值。
2. 函式裡面可以再定義函式，我們稱其為子函式，而包含此子函式的函式為父函式。
### 1.裝飾器介紹
---
裝飾器可以用來修飾(或包裝)函式，可以增加額外的程式碼在被包裝的目標函式的之前或之後。
想像裝飾器是燈罩，加上燈罩之後，可以修改電燈照明範圍；你也... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import ta
dataset_train = pd.read_csv('train.csv')
dataset_train = dataset_train.drop(["Date","Turnover (Lacs)"], axis=1)
RSI=ta.momentum.rsi(close=dataset_train['Close'])
MFI=ta.volume.money_flow_index(dataset_train['High'], dataset_train['Low'... | github_jupyter |
# Searching For Simple Patterns
Being able to match letters and metacharacters is the simplest task that regular expressions can do. In this section we will see how we can use regular expressions to perform more complex pattern matching. We can form any pattern we want by using the metacharacters mentioned in the prev... | github_jupyter |
# Activity Feed Details - Web Scraping
>- It can be used free account to extract the information
>- For this script you can use either a list of logins, or setup a unique login through the **Function** `change_user ()`, with `number_logins=1` and any login of your choice
>- For a web scraping process is important to h... | github_jupyter |
# Symbolic Math: Differentiation Program
This notebook describes how to write a program that will manipulate mathematical expressions, with an emphasis on differentiation. The program can compute $\frac{d}{dx} x^2 + \sin(x)$ to get the result $2x + \cos(x)$. Usually in a programming language, when we say `sin(x)` w... | github_jupyter |
# Linear Regression in Python
There are two main packages you can use to run basic linear regression models in Python: [statsmodels](http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/ols.html) and [scikit-learn](http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares). I'll ... | github_jupyter |
<h1>Training Keras model on Cloud AI Platform</h1>
This notebook illustrates distributed training on Cloud AI Platform. This uses Keras and requires TensorFlow 2.1
```
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
```
## Set up environment variables and load necessary libr... | github_jupyter |
# Performance Attribution
In this exercise, we're going to use artificial data and a PCA risk model to show you the nuts and bolts of performance attribution. We use artificial data here so that you can focus on the calculations without having to worry about extra packages and data details. Let's get started!
```
impo... | github_jupyter |
```
import pandas as pd
# データフレームを読み込む
df = pd.read_csv('USDJPY_TrailingStop2.csv', index_col='Date')
df.index = pd.to_datetime(df.index)
df.head()
import time
import numpy as np
from numba import jit
from keras.utils.np_utils import to_categorical
# 特徴量を[Close[20], ATR(20)[20]]、目的変数をTrailingStop損益結果の4パターンとする
@jit
de... | github_jupyter |
# Logistic Regression
Logistic regression is an <b>algorithm of binary classification</b> used in a supervised learning problem when the output y are all either zero or one. (*For Example*: Cat vs No-cat).
<br>
The <b>goal</b> of logistic regression is to minimize the error between its predictions and training data.
... | github_jupyter |
```
import pandas as pd
from tqdm.auto import tqdm
import numpy as np
from slugify import slugify
import ast
from IPython.display import display, HTML
import matplotlib.pyplot as plt
import booleanize
artists_df = pd.read_csv("data/artists.csv", keep_default_na=False)
tracks_df = pd.read_csv("data/tracks.csv")
playlist... | github_jupyter |
```
%matplotlib inline
import sys
import numpy as np
import math
import matplotlib.pyplot as plt
#Mathematical constants
pi = np.pi
tpi = 2.0*pi
fpi = 4.0*pi
zI = 1.0j
#Physical constants
#https://ja.wikipedia.org/wiki/%E5%8E%9F%E5%AD%90%E5%8D%98%E4%BD%8D%E7%B3%BB
aB = 0.0529177210903 #nanometer
Hartree = 27.2113862459... | github_jupyter |
```
import csv
import json
import uuid
import datetime
import pandas as pd
from lxml import etree
from collections import defaultdict, Counter
from tqdm import tqdm_notebook as tqdm
import os
import matplotlib.pyplot as plt
import statistics
import numpy as np
from eMammal_helpers import clean_species_name, get_total_f... | github_jupyter |
<a href="https://colab.research.google.com/github/dude123studios/AdvancedReinforcementLearning/blob/main/Deep%20Q%20networks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import random
import gym
import numpy as np
from collections import dequ... | github_jupyter |
```
!curl https://topcs.blob.core.windows.net/public/FlightData.csv -o flightdata.csv
import pandas as pd
df = pd.read_csv('flightdata.csv')
df.head()
df.shape
df.isnull().values.any()
df = df.drop('Unnamed: 25', axis=1)
df.isnull().sum()
df = df[["MONTH", "DAY_OF_MONTH", "DAY_OF_WEEK", "ORIGIN", "DEST", "CRS_DEP_TIME... | github_jupyter |
# 10. 다양한 데이터 전처리 기법
**EDA를 통해 도출된 데이터 인사이트를 토대로, 효과적인 Feature Engineering을 위해 사용하는 Encoding, Scaling, Feature Selection 등의 전처리 기법을 실습해 본다.**
## 10-1. 들어가며
```bash
$ mkdir -p ~/aiffel/data_preprocess/
$ ln -s ~/data/ ~/aiffel/data_preprocess/
```
```
import pandas as pd
import numpy as np
import matplotlib.pyplot a... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
# Text generation using a RNN with eager execution
<table class="tfo-notebook-buttons" align="left">
<td>
<a target=\"_blank\" href="https://www.tensorflow.org/tutorials/sequences/text_generation"><img src... | github_jupyter |
```
import pandas as pd
import missingno as msno
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('../../fixtures/raw_data/2019projections.csv')
# see how many rows and columns are in this dataset
shape_info = df.shape # set the dataframe's "shape" to a variable
print('This dat... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.linear_model ... | github_jupyter |
```
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequence... | github_jupyter |
<a href="https://colab.research.google.com/github/SidharthArya/Deep_Learning_Class_Manit/blob/main/Assignments/005_Transfer_Functions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pyplot as plt
from matplot... | github_jupyter |
```
import fnmatch
import os
import re
import numpy as np
import pandas as pd
from matplotlib import cm
import seaborn as sns
def get_val_losses(file_list, col_ref='val_ou', avg_over=50, epochs=None):
'''
Returns a concatenated array with arrays in the list. \n
Concatenation will be performed along axis=1
... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Configuration
_**Setting up your Azure Machine Learning services workspace and configuring your n... | github_jupyter |
# Pandas fast mutate architecture
(Published 27 Oct 2019)
## Problem: series operations are type invariant under grouping
### What is type variance?
In spirit, most pandas operations are one of two functions.
* f_elwise(a, [b]) - takes up to two series, returns a result of the same length.
* f_agg(a, [b]) - takes ... | github_jupyter |
# Step 2.1: Experiment I: Machine Learning
---
## 1. Imports
```
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np #operaciones matriciales y con vectores
import pandas as pd #tratamiento de datos
import random
import matplotlib.pyplot as plt #gráficos
import seaborn as sns
import jobl... | github_jupyter |
```
# !pip install nlp
# !pip install bert_score
# !pip install git+https://github.com/google-research/bleurt.git
from pathlib import Path
import pandas as pd
import asyncio
import aiohttp
import json
from aiohttp import ClientConnectorError, ClientSession
from nlp import load_metric
from sklearn.model_selection impor... | github_jupyter |
# Deep Q Network with Pong
DQN was created by DeepMind researchers, achieving superhuman performance on many Atari games (https://deepmind.com/research/dqn/). What made the accomplishments of DQN even more impressive is that the DQN had nearly the same architecture and hyperparameter settings for each game (ie no game... | github_jupyter |
```
# default_exp tensorflow.tflite_converter
# hide
from nbdev.showdoc import *
# export
import logging
import logging.handlers
import argparse
import sys
from os.path import join
from google.protobuf import text_format
from aiforce.core import OptionalModule
from aiforce.dataset.type import DatasetType, infer_dataset... | github_jupyter |
```
%load_ext watermark
%watermark -d -u -a 'Andreas Mueller, Kyle Kastner, Sebastian Raschka' -v -p numpy,scipy,matplotlib
```
The use of watermark (above) is optional, and we use it to keep track of the changes while developing the tutorial material. (You can install this IPython extension via "pip install watermar... | github_jupyter |
#### Agenda - Given a sequence of previous characters, model the probability distribution of the next character in the sequence.
Here we try harry potter text
We will be using kears for this note
```
from __future__ import print_function
import numpy as np
import random
import sys
from keras.models import Sequenti... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will gen... | 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 |
```
!pip install --upgrade pip
!pip install python-decouple
!pip install geoalchemy2
!pip install shapely
!pip install scipy
!pip install hyperas
from sqlalchemy import create_engine, func, text
from sqlalchemy.orm import sessionmaker
from decouple import config
from shapely import wkb, wkt
from shapely.geometry import... | github_jupyter |
# Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have to use your solutions from this previous assignment and extend th... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
model_name = 'k80'
file_name = 'k80'
DATA_DIR = os.path.abspath("../../../prediction_model/data/raw_data")
SAVE_DIR = os.path.abspath("../../../prediction_model/data/")
```
## Dense Layer
```
dfDense = pd.read_pickle(os.path.join(DATA_DIR,'%s/8/benchmark_dense__201... | github_jupyter |
# Example 14: RVT SRA with multiple motions and simulated profiles
Example with multiple input motions and simulated soil profiles.
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pysra
%matplotlib inline
# Increased figure sizes
plt.rcParams["figure.dpi"] = 120
```
## Create a po... | github_jupyter |
```
import numpy as np
import tensorflow as tf
class HMM(object):
def __init__(self, initial_prob, trans_prob, obs_prob):
self.N = np.size(initial_prob)
self.initial_prob = initial_prob
self.trans_prob = trans_prob
self.emission = obs_prob
assert self.initial_prob.shape == (s... | github_jupyter |
# Tanzania dataset presentation
This notebook presents the [Open AI Tanzania dataset](https://blog.werobotics.org/2018/08/06/welcome-to-the-open-ai-tanzania-challenge/), released after a recent deep learning [challenge](https://competitions.codalab.org/competitions/20100), and used in this semantic segmentation projec... | github_jupyter |
# MoViNet Tutorial
This notebook provides basic example code to create, build, and run [MoViNets (Mobile Video Networks)](https://arxiv.org/pdf/2103.11511.pdf). Models use TF Keras and support inference in TF 1 and TF 2. Pretrained models are provided by [TensorFlow Hub](https://tfhub.dev/google/collections/movinet/),... | github_jupyter |
# 函数定义
本章我们将介绍编程语言中非常重要的一个概念——函数。这里的函数和我们之前在数学中所学习的函数，有一些相似点，也有些不同。
相似点是，它们本质都是一段操作的复用。在数学里，假如我们有一个函数：$f(x)=3x+9$，当我们计算 $f(3)$ 或者 $f(9)$ 的时候，都是在进行 $3x+9$ 这个操作。在程序中呢，也是类似的，我们用几行代码定义了一个函数，每次调用这个函数就是重复那几段代码的操作。
不同的是，在数学的函数中，我们只能定义 $f(x,y) = x^2 + xy + 3$ 这样的函数，却不能定义 $f(x,y) = x^2 + ny + m$ 这样的函数，我们不知道这里面的 $m$ 和 $n$ 是什么... | github_jupyter |
# WELCOME TO THE YOUTUBE SCRAPER USING BS4
Hello All, I will show you all how to scrape youtube and display the video in the Notebook itself. Also, we can store the details in the excel for further use.
Before Scraping any website, go through their terms and conditional.
You can find the code in github - https://gith... | github_jupyter |
```
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification,AdamW,BertForSequenceClassification,DistilBertModel
import torch
from torch.utils.data import DataLoader
import pickle
from sklearn.model_selection import train_test_split
import numpy as np
import tqdm
import sys
from sklearn.fea... | github_jupyter |
[](https://www.pythonista.io)
# *URLs* y redireccionamiento.
## La función ```flask.url_for()```.
La función ```flask.url_for()``` permite utilizar una función de vista como referencia en vez de su *URL* correspondiente con la siguiente sintaxis.
```
url_for(<función>, value... | github_jupyter |
# Validate and prepare YAML policy stubs
```
import copy
import pathlib
import json
import os
import re
import lxml.etree
import networkx
import pandas
from pykwalify.core import (
Rule,
Core as Kwalify,
)
import ruamel.yaml
```
## Configuration
```
schema_path = 'schema.yml'
test_paths = list(map(str, path... | github_jupyter |
```
from sklearn import neighbors, datasets
iris = datasets.load_iris()
X, y = iris.data, iris.target
print ("data=", X, "\ntarget=",y);
from sklearn.decomposition import PCA
'''
Principal component analysis (PCA)
Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower... | github_jupyter |
# Parallel Processing with Metakernel
Metakernel uses the `ipyparallel` system for running code in parallel. This notebook demonstrates the process using the kernel Calysto Scheme. However, other Metakernel-based kernels may also work. The kernel needs to be able to return values, and implement kernel.set_variable() a... | github_jupyter |
##### Copyright 2019 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 |
```
import sys
sys.path.insert(0, '/home/emmanuel/code/py_iasi')
sys.path.insert(0, '/home/emmanuel/papers_code/2019_egp_letter/src')
# standard packages
import numpy as np
import xarray as xr
import pandas as pd
# IASI dataloader
from pyiasi.iasi import IASIDataLoader, IASIData
# GP Models
from models.exact import ... | github_jupyter |
<center><h1> CSS Playground </h1></center>
A notebook that contain most of the things that could be displayed, to test CSS, feel free to add things to it, and send modification
# Title first level
## Title second Level
### Title third level
#### h4
##### h5
###### h6
# h1
## h2
### h3
#### h4
##### h6
This is ... | github_jupyter |
# Results Visualisation
---
```
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from annex_new import import_
from annex_new import get_ellipticity
from annex_new import get_bh_errors
from annex_new import get_sep_errors
from annex_new import get_elli_errors
from annex_new import count_per_... | github_jupyter |
```
import numpy as np
import pandas as pd
from collections import Counter
import operator
df = pd.read_csv("datasets/titanic_data.csv")
df.head()
from sklearn.model_selection import train_test_split
train_data, test_data = train_test_split(df, test_size=0.33, random_state=42)
train_data.info()
```
Okay, the **Age**,... | github_jupyter |
##### Copyright 2019 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 |
# A brief description of the proposed distribution
They calculate different characteristics of the voxel values of images captured by different devices, and using statistical hypothesis testing they reject null hypothesizes by low values of p-values, and they conclude that the statistical distribution generating these... | github_jupyter |
# k-NN Image Classifier
This chapter aims to create a image classifier for a dataset consisting of images of dogs, cats and pandas.
k-NN is the simplest machine learning algorithm.
It works like this:
In the training phase the images are put in a cartesian path, where the axes are the values of the feacture vectors, a... | github_jupyter |
## Imports
```
import os, tarfile, sys
from pathlib import Path
from time import time
from pprint import pprint
from collections import Counter
import numpy as np
from numpy.random import choice
import pandas as pd
import spacy
from gensim.models.word2vec import LineSentence
from gensim.models.phrases import Phrase... | github_jupyter |
```
# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_recognition/conf/SpeakerNet_recognition_3x2x512.yaml
# !cat conf/SpeakerNet_recognition_3x2x512.yaml
# !pip3 install nemo-toolkit[asr]==1.0.0b1
# !pip3 install torchaudio>=0.6.0 -f https://download.pytorch.org/whl/torch_stable.html
# !git p... | github_jupyter |
```
#default_exp cli
```
# CLI
> Run the forecasting pipeline with configuration files
```
#export
import pandas as pd
import typer
from mlforecast.api import (
_paste_dynamic,
_path_as_str,
_prefix_as_path,
_read_dynamic,
fcst_from_config,
parse_config,
perform_backtest,
read_data,
... | github_jupyter |
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