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# Learning Scikit-learn: Machine Learning in Python
## IPython Notebook for Chapter 2: Supervised Learning - Estimating Boston house pricing using Linear Regression
_In every example we have seen so far, we have faced what in Chapter 1, Machine Learning – A Gentle Introduction, we called classification problems: the ... | github_jupyter |
```
!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
-O /tmp/horse-or-human.zip
!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \
-O /tmp/validation-horse-or-hu... | github_jupyter |
```
%reset
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Lambda, MaxPool2D, BatchNormalization
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers ... | github_jupyter |
```
import json
import pathlib
import traceback
import cf_xarray
import fsspec
import matplotlib.pyplot as plt
import pandas as pd
import proplot as pplt
import pydantic
import xarray as xr
import xstac
S3_URL = 'https://stratus.ucar.edu'
fs = fsspec.filesystem(
's3', profile='stratus-cesm', anon=False, client_kwa... | github_jupyter |
<a href="https://colab.research.google.com/github/institutohumai/cursos-python/blob/auto/Scraping/3_Selenium_y_xpath/scraping_por_automatizacion_solucion.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/... | github_jupyter |
<a href="https://colab.research.google.com/github/sidharth178/The-Battle-of-Neighborhoods-Capstone-Project/blob/master/The_Battle_Of_Neighborhoods.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# <center>**===========The Battle Of Neighborhoods====... | github_jupyter |
## From HyTcWaves: Obtain TC parameters associated with maximum TWL
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# common
import os
import os.path as op
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import gridspec
# pip
import xarray as xr
import numpy as np
# DEV: override installed tes... | github_jupyter |
### **TODO:** Also extract additional links from the `links` keys
```
import pandas as pd
import os
import ijson
import json
import gzip
import itertools
import sys
sys.path.append("../../src/data")
from content_api_extract import extract_link_types
from extract_text_utils import get_text
from datetime import datetime... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=Health/CALM/CALM-moving-out.ipynb&depth... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import os
import cv2
os.getcwd()
os.chdir('D:')
os.getcwd()
root_directory = os.path.join(os.getcwd(), 'naruto_anime')
os.chdir(... | github_jupyter |
# Structured data prediction using Cloud ML Engine
This notebook illustrates:
1. Exploring a BigQuery dataset using JupyterLab
2. Creating datasets for Machine Learning using Dataflow
3. Creating a model using the feature columns and Keras API
4. Training on Cloud AI Platform
5. Deploying model
6. Predicting with mod... | github_jupyter |
# Patchify and plot images
Refer to [the installation section in README](https://github.com/Living-with-machines/MapReader#installation) to install `mapreader`.
```
# solve issue with autocomplete
%config Completer.use_jedi = False
%load_ext autoreload
%autoreload 2
%matplotlib inline
```
## Load images
Example im... | github_jupyter |
# Imputing Missing Values in Data
## Load Data
```
import sys,tempfile, urllib, os
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
BASE_DIR = '/tmp'
OUTPUT_FILE = os.path.join(BASE_DIR, 'churn_data.csv')
```
... | github_jupyter |
# Demo 5: Creating Denormalized Tables
<img src="images/postgresql-logo.png" width="250" height="250">
### Walk through the basics of modeling data from normalized from to denormalized form. In this demo, we will: <br>
<ol><li>Create tables in PostgreSQL<li>Insert rows of data<li>Do simple JOIN SQL queries to show ho... | github_jupyter |
# HEX algorithm **Kopuru Vespa Velutina Competition**
**XGBoost model**
Purpose: Predict the number of Nests in each of Biscay's 112 municipalities for the year 2020.
Output: *(WaspBusters_20210624_months_SKLearnAVGBaseHybrid.csv)*
@authors:
* mario.bejar@student.ie.edu
* pedro.geirinhas@student.ie.edu
* a.berrizbe... | github_jupyter |
```
import os
import pandas as pd
path = './fashion_mnist'
tr = pd.read_csv(os.path.join(path, 'fashion-mnist_train.csv'))
test = pd.read_csv(os.path.join(path, 'fashion-mnist_test.csv'))
print('train data: ', tr.shape)
print('test data: ', test.shape)
tr.head()
print('label 종류: ', set(tr['label']))
for i in range(10)... | github_jupyter |
```
# Importing necessary packages TEST test
from datetime import date, timedelta
import os
import requests
import shutil
import pandas as pd
import numpy as np
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import tqdm
import logging
# Set start date and end date, with 7 days interval
start = date(... | github_jupyter |
# ORION Orientation Estimation Using Commodity Wi-Fi
With MIMO, Wi-Fi led the way to the adoption of antenna array signal processing techniques for fine-grained localization using commodity hardware. MIMO techniques, previously exclusive to specific domains of applications for instance radar systems, allow to consi... | github_jupyter |
# Evaluating Model Performance
In this demo, we'll be using the Red Wine Quality dataset. The datset can be used in both regression and classification models. The purpose of this notebook is to build different models, classifiers and regressors, and compare their performance to see which one performs the best on our d... | github_jupyter |
<a id='start'></a>
# Reti neurali con Tensor Flow
In questo notebook vengono presentati degli esercizi sulle reti neurali con Tensor Flow.
Provate a svolgere il seguente esercizio:<br>
1) [pp -> H -> ZZ -> 4lepton](#section1)<br>
<a id='section1'></a>
## pp -> H -> ZZ -> 4lepton
Creare una rete neurale per analizza... | github_jupyter |
# 2. Exploring the relationship between gender and policing
**Does the gender of a driver have an impact on police behavior during a traffic stop? In this chapter, you will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!**
```
import pandas as pd
ri ... | github_jupyter |
# Topic Modeling on London court cases
We're going to be using a topic model to explore transcripts from court cases in London from 1820-1830. A topic model is similar to a document clustering algorithm, but instead of grouping together documents we're going to group together word *tokens*. A document can thus "belong... | github_jupyter |
# Loading data into StellarGraph from NumPy
> This demo explains how to load data from NumPy into a form that can be used by the StellarGraph library. [See all other demos](../README.md).
<table><tr><td>Run the latest release of this notebook:</td><td><a href="https://mybinder.org/v2/gh/stellargraph/stellargraph/mast... | github_jupyter |
# Tables & Figures Generation
[](https://notebooks.gesis.org/binder/v2/gh/AyrtonB/Merit-Order-Effect/main?filepath=nbs%2Fdev-09-tables-and-figures.ipynb)
This notebook provides a programmatic workflow for generating the tables used in the MOE paper, as well ... | github_jupyter |
# TensorNetworks in Neural Networks.
Here, we have a small toy example of how to use a TN inside of a fully connected neural network.
First off, let's install tensornetwork
```
!pip install tensornetwork
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
tf.enable_v2_behavior()
# Import tens... | github_jupyter |
### Notebook for Understanding Basics of Conjugate Priors
```
import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy.stats as sstat
import seaborn as sns # if necessary
```
#### Check Beta Distribution which is Conjugate to Bernoulli Likelihood
Check Beta Distribution defin... | github_jupyter |
```
import math
class Geo:
#limited to taking in decimal lat and long
def __init__(self, lat, long):
self.lat = lat
self.long = long
@staticmethod
def deg_to_dms(deg, type='lat'):
decimals, number = math.modf(deg)
d = deg
m = decimals * 60
s = (de... | github_jupyter |
```
import re
import readwrite.write_snippets as ws
```
# Regular VSCode snippets
Include multi-line snippets?
```
multiline = True
```
Include single-line snippets?
```
singleline = True
```
Convert `$...$` to `\(...\)`?
```
dollarfix = False
```
Use text/math modes for snippets where available?
```
textmaths... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
class Market:
def __init__(self, a_d, b_d, a_z, b_z, tax):
self.a_d = a_d
self.b_d = b_d
self.a_z = a_z
self.b_z = b_z
self.tax = tax
self.eprice = 0
self.equantity = 0
def price(self):
... | github_jupyter |
# Desafio 3
Neste desafio, iremos praticar nossos conhecimentos sobre distribuições de probabilidade. Para isso,
dividiremos este desafio em duas partes:
1. A primeira parte contará com 3 questões sobre um *data set* artificial com dados de uma amostra normal e
uma binomial.
2. A segunda parte será sobre a an... | github_jupyter |
- data downloaded from: http://hmp2-data.stanford.edu/index.php#
- reference study design: https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(14)00306-0?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312814003060%3Fshowall%3Dtrue
- shannon diversity: protein (take exponential to ensure ... | github_jupyter |
```
#!/usr/bin/env python3
# coding=utf-8
import spotipy
import spotipy.util as util
from spotipy.oauth2 import SpotifyClientCredentials
from pprint import pprint
from dotenv import load_dotenv
import os
```
import sys
if len(sys.argv) > 3:<br>
username = sys.argv[1]<br>
playlist_id = sys.argv[2]<br>
trac... | github_jupyter |
# Googleドライブから学習データをロード
```
from google.colab import drive
drive.mount('./gdrive')
!cp '/content/gdrive/MyDrive/ReversiTrainData/X_dataset2.npy' ./
!cp '/content/gdrive/MyDrive/ReversiTrainData/y_dataset2.npy' ./
import numpy as np
X_dataset = np.load('X_dataset2.npy')
y_dataset = np.load('y_dataset2.npy')
print(... | github_jupyter |
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
import sys
import numpy as np
import astropy.units as u
from astropy import wcs
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.visualization import make_lupton_rgb
from astropy.utils.data import download_file, clea... | github_jupyter |
Title: RP- Spatial Accessibility of COVID-19 Healthcare Resources in Illinois Pre-Processing Script
---
This is a script that automates as much of the data gathering and pre-processing as possible for reproduction of Kang et al. (2020).
**Reproduction of**: Rapidly measuring spatial accessibility of COVID-19 healthca... | github_jupyter |
# Multi-Layer Perceptron, MNIST
---
In this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.
The process will be broken down into the following steps:
>1. Load and visualize the data
2. Define a neural network
3. Train the model... | github_jupyter |
```
from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np
batch_size = 64 # Batch size for training.
epochs = 100 # Number of epochs to train for.
latent_dim = 256 # Latent dimensionality of the encoding space.
num_samples = 10000 # Number of samples to train on.
# Path to th... | github_jupyter |
# Machine Learning con Python
## Índice
1. **[¿Qué es Machine Learning?](#1.-¿Qué-es-Machine-Learning?)**
2. **[Tipos de Machine Learning](#2.-Tipos-de-Machine-Learning)**
* [2.1 Aprendizaje supervisado](#2.1-Aprendizaje-supervisado)
* [2.2 Aprendizaje no sup... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
dtrain = xgb.DMatrix('./resources/agaricus.txt.train')
dtest = xgb.DMatrix('./resources/agaricus.txt.test')
# specify training parameters
params = {
'objective':... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp callback.tensorboard
#all_slow
#export
from fastai.basics import *
```
# Tensorboard
> Integration with [tensorboard](https://www.tensorflow.org/tensorboard)
First thing first, you need to install tensorboard with
``... | github_jupyter |
**Chapter 4 – Huấn luyện Mô hình Tuyến tính**
_Notebook này chứa toàn bộ mã nguồn mẫu và lời giải bài tập Chương 4 - tập 1._
<table align="left">
<td>
<a href="https://colab.research.google.com/github/mlbvn/handson-ml2-vn/blob/main/04_training_linear_models.ipynb" target="_parent"><img src="https://colab.resear... | github_jupyter |
# 4.3 アルゴリズム選択
```
# 日本語化ライブラリ導入
!pip install japanize-matplotlib | tail -n 1
# 共通事前処理
# 余分なワーニングを非表示にする
import warnings
warnings.filterwarnings('ignore')
# 必要ライブラリのimport
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# matplotlib日本語化対応
import japanize_matplotlib
# データフレーム表示用関数
from IPytho... | github_jupyter |
# Homework: Mad Libs!
## The Problem
Write a Python program which creates your own unique Mad-Libs! story. This is very similar to the example we did in large group only now you will devise and program your own unique story.
If you are not familar with a Mad-Libs! stories, check out:
http://www.madlibs.com and http... | github_jupyter |
```
import tensorflow as tf
tf.config.experimental.list_physical_devices()
tf.test.is_built_with_cuda()
```
# Importing Libraries
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os.path as op
import pickle
import tensorflow as tf
from tensorflow import keras
from keras.models im... | github_jupyter |
# Capsule Networks (CapsNets)
# 胶囊网络（CapsNets）
Based on the paper: [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829), by Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton (NIPS 2017).
基于这篇论文：[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)，作者Sara Sabour, Nicholas Frosst 和 Geof... | github_jupyter |
```
import os
import ase
from ase import Atoms
import numpy as np
import tqdm
import ase.io
from nice.blocks import *
from nice.utilities import *
from matplotlib import pyplot as plt
from sklearn.linear_model import BayesianRidge
PROPERTIES_NAMES = [
'tag', 'index', 'A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'g... | github_jupyter |
# Running Code
First and foremost, the IPython Notebook is an interactive environment for writing and running code. IPython is capable of running code in a wide range of languages. However, this notebook, and the default kernel in IPython 2.0, runs Python code.
## Code cells allow you to enter and run Python code
Ru... | github_jupyter |
```
%matplotlib inline
import argparse
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import scipy.spatial.distance
import cpc.feature_loader as fl
import cpc.train as tr
from cpc.dataset import AudioBatchData, findAllSeqs, filterSeqs, parseSeqLabels
sys.path.append(os.path.dirname(tr.__file_... | github_jupyter |
# Tarea N°02
## Instrucciones
1.- Completa tus datos personales (nombre y rol USM) en siguiente celda.
**Nombre**:Andrés Montecinos López
**Rol**:201204515-0
2.- Debes pushear este archivo con tus cambios a tu repositorio personal del curso, incluyendo datos, imágenes, scripts, etc.
3.- Se evaluará:
- Soluciones
... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good model.
This does have the potential to make the project quite messy! Before subm... | github_jupyter |
```
from __future__ import print_function
# to be able to see plots
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append("../tools")
from tools import collage
# just to use a fraction of GPU memory
# This is not needed on dedicated machines.
# Allows you to share the ... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | github_jupyter |
# Notes for Unit 1
This is where I put my notes for the very first unit's video:
**Video Link:**
https://www.freecodecamp.org/learn/machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals
**Artificial Intelligence vs Neural Networks vs Machine Learning**:
* **Artificial Intelligence**
* ... | github_jupyter |
```
0 << 2
from typing import List
class TrieNode:
def __init__(self):
self.next = [None, None] # self.next[0] 表示 0， self.next[1] 表示1
class Solution:
def maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:
nums.sort()
# 按照 queries[1] 的大小进行排序
# 按照... | github_jupyter |
# Identifying country names from incomplete house addresses
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc">
<ul class="toc-item">
<li><span><a href="#Introduction" data-toc-modified-id="Introduction-1">Introduction</a></span></li>
<li><span><a href="#Prerequisites" data-toc-modified-id="Prere... | github_jupyter |
# Learning Objectives
- Understand HPC concepts: automating data-mining process through the Palmetto Supercomputer
** Python cell magic: **
- Specify at top of cell
- Prefixed by %%
- Enable functionality on cell' contents
** writefile: **
- %%writefile *path-to-filename*
## Where am I?
```
!ls
%%writefile mine_i... | github_jupyter |
# ipyvuetify Tutorial 08 - Custom Components
This is number 8 in a series of ipyvuetify app development tutorials. If you're just getting started with ipyvuetify and haven't checked out the first tutorial "01 Installation and First Steps.ipynb", be sure to check that one out first.
First of all, we'll load the requir... | github_jupyter |
# Classification -- Images & Hands-On
## Table of Contents
<ol>
<li>Processing of complicated data like images</li>
<li>Thinking about models to use for image classification</li>
<li>Implementation of common models</li>
<li>Convolutional neural networks -- an ML greatest hit</li>
</ol>
## 1. Processin... | github_jupyter |
# ALERCE Client ToO Access
Demonstrate access to the ALERCE data stream.
This notebook demonstrates searches for Type Ia SNe.
```
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import astropy.units as u
from astropy import coordinates
from astropy.time import Time
from astropy.table imp... | github_jupyter |
# Predicting drug-target interaction
In this tuorial, we will go through how to run a MolTrans model for compound-protein affinity prediction. In particular, we will demonstrate how to train, validate and test of classification and regression tasks within folder `/apps/drug_target_interaction/moltrans_dti/`.
# MolTra... | github_jupyter |
# Application of Vertical Federated Learning: Constructing a Credit Score System for the Unlabelled Party
Authors: Zhu Xiaochen, Xu Yunfei
## Set up the environment
```
import pandas as pd
pd.set_option("display.max_rows", None, "display.max_columns", None)
import numpy as np
np.set_printoptions(precision=3, suppres... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D2_LinearDeepLearning/W1D2_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 3: Deep linear neural networks
**Week 1, Day ... | github_jupyter |
# Train VAE for task2...
Finally trying more silly attempt; no reconstruction loss.
Loss function is now: $loss = 0 L_{Reconstruction} + L_{KLD} = L_{KLD}$
```
# public modules
from dlcliche.notebook import *
from dlcliche.utils import (
sys, random, Path, np, plt, EasyDict,
ensure_folder, deterministic_ever... | github_jupyter |
<a href="https://colab.research.google.com/github/alexmascension/ANMI/blob/main/notebook/T5.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tema 5: Interpolación de funciones
```
!pip install -r https://raw.githubusercontent.com/alexmascension/AN... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('derlemler/filtrelenmis_temizlenmis_derlem.csv.gz')
# grafik çizimi için yardımcı metot
def window_average(x,N):
low_index = 0
high_index = low_index + N
w_avg = []
while(high_index<len(x)):
temp = sum(x[low_index:high_inde... | github_jupyter |
# GCB535 - Debugging Code
## Instructions
In this adventure, you will practice looking at, identifying, and correcting code that is *buggy*.
Check out the code below. I have devided the code into cells (blocks) for you to dissect sequentially, but the ultimate goal here would be code that is fixed such that it "work... | github_jupyter |
# Predicting Student Admissions with Neural Networks
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: http://www.ats.ucla.edu/
## Loading the data
To load the da... | github_jupyter |
# K-Nearest Neighbor Classification
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## Data Loading
```
PATH = "../../../Classification/K-NN/Python/Social_Network_Ads.csv"
dataset = pd.read_csv(PATH)
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
```
## Train Test Spli... | github_jupyter |
<a href="https://colab.research.google.com/github/jads-nl/WhirlwindTourOfPython/blob/master/07-Control-Flow-Statements.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="https:/... | github_jupyter |
```
# 需要先安裝 gym[atari]
# headless 執行: xvfb-run -a jupyter notebook
import gym
env = gym.make('Pong-ram-v0')
import numpy as np
import ipywidgets as W
from PIL import Image
```
看一下基本資訊
```
env.action_space
env.reward_range
env.reset()
```
抓圖出來
```
Image.fromarray(env.render(mode='rgb_array'))
from io import BytesIO
... | github_jupyter |
## <center>ML Feature Engineering</center>
# <center>Feature Preparation, Selection and Engineering</center>
**Author:** João António - joaoantant@gmail.com \& github.com/JoaoAnt/.
**Based on:** the approach of the Dataquest.
**The ipybn can be found in:** the Github in the WaddlePortfolio/Projects.
# Introduction... | github_jupyter |
## Columbia University
### ECBM E4040 Neural Networks and Deep Learning. Fall 2021.
# ECBM E4040 - Assignment 2 - Task 3: Convolutional Neural Network (CNN)
In this task, you are going to first practice the forward/backward propagation of the convolutional operations with NumPy. After that, we will introduce TensorFl... | github_jupyter |
# Python Primer: The Basics
This is a notebook file. It is an interactive document that you can edit on the fly, and you can use it to write and execute programs. You can learn more about the interface by reading the [online documentation](http://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Notebook%20B... | github_jupyter |
<a href="https://colab.research.google.com/github/Data-Science-and-Data-Analytics-Courses/MITx---Machine-Learning-with-Python-From-Linear-Models-to-Deep-Learning-Jun-11-2019/blob/master/Resources.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Res... | github_jupyter |
% CGRtools Tutorial
% Dr. Ramil Nugmanov; Dr. Timur Madzhidov; Ravil Mukhametgaleev
% Mar 25, 2019
# 1. Data types and operations with them
(c) 2019, Dr. Ramil Nugmanov; Dr. Timur Madzhidov; Ravil Mukhametgaleev
Installation instructions of CGRtools package information and tutorial's files see on `https://github.com... | 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 |
## Multi-label classification
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.conv_learner import *
PATH = 'data/planet-understanding-the-amazon-from-space/'
# Data preparation steps if you are using Crestle:
#os.makedirs('data/planet/models', exist_ok=True)
#os.makedirs('/cache/planet/tmp', e... | github_jupyter |
```
from tools import *
from models import *
import plotly.graph_objects as go
import plotly.figure_factory as ff
from Bio.SeqUtils import GC
import pickle
import warnings
warnings.filterwarnings('ignore')
#CONSTANTS AND HYPERPARAMETERS (add to yaml)
# Device configuration
device = torch.device('cuda:0' if torch.cuda.... | github_jupyter |
```
#all necessary imports to run SAMUROI
%matplotlib qt4
import numpy
import scipy.signal
import matplotlib.pyplot as plt
import samuroi
from samuroi.plugins.baseline import bandstop, power_spectrum
from samuroi.plugins.baseline import linbleeched_deltaF
from samuroi.plugins.baseline import stdv_deltaF
from samu... | github_jupyter |
```
! pip install memory_profiler
%load_ext memory_profiler
```
### INTRODUCTORY EXAMPLE:
#### Total size of folders and files in a path
```
import os
"""
The os functions that we are given are:
os.path.getsize(path)
os.path.isdir(path)
os.listdir(path)
os.path.join(path, filename)
"""
def disk_usage(path):
"""
... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmode... | github_jupyter |
# Vocabulary
Here are some examples and tests with vocabularies in spacy
## Matchers
This is analog to traditional regular expressions but applied to documents.
```
import spacy
from spacy.matcher import Matcher
nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab) # work with the normal vocabulary
# Sola... | github_jupyter |
# Baesyan Data Analysis Course - Chapter 4 Exercises
https://github.com/avehtari/BDA_course_Aalto/tree/master/exercises
### Exercise 1 - Bioassay Model
In this exercise, you will use a dose-response relation model that is used in Section 3.7 of the course book and in the chapter reading instructions [here](https://gi... | github_jupyter |
# Naive Bayes, Part 3
### Naive Bayes by Example 3
In part 2, you have seen an example of Naive Bayes classifier for rain prediction. It has three input features. Now let's start with a new example. In this example, we want to build an intelligent lighting. The light has two states either 'On' or 'Off' depending on us... | github_jupyter |
## MonteCarlo
Apply Monte-Carlo control to Easy21. Initialise the value function to zero.
Use a time-varying scalar step-size of alpha_t = 1/N(s_t, a_t) and an epsilon-greedy exploration strategy with epsilon_t = N_0 / (N_0 + N(s_t)), where N_0 = 100 is a constant, N(s) is the number of times that state s has been vis... | github_jupyter |
```
import numpy as np
import pandas as pd
import csv
import json
from sklearn import preprocessing
```
#### Read csv data into a pandas dataframe
```
aid = pd.read_csv('aiddata-countries-only.csv', delimiter=',')
aid
aid.count()
aid.columns
```
#### Group by donor and recipient respectively
```
# donor = aid.group... | github_jupyter |
<a href="https://colab.research.google.com/github/sarthakpant772/ML_classification_Hacktoberfest/blob/main/King_Rook_vs_King_Pawn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# K-Nearest-Neighbors (KNN)
## Importing Libraries
```
import tensor... | github_jupyter |
# Environment
```
# setting the random seed for reproducibility
import random
random.seed(493)
# for manipulating dataframes
import pandas as pd
import numpy as np
# for statistical testing
from scipy import stats
from scipy.stats import mannwhitneyu
# natural language processing
import re
import unicodedata
import... | github_jupyter |
**INITIALIZATION:**
- I use these three lines of code on top of my each notebooks because it will help to prevent any problems while reloading the same project. And the third line of code helps to make visualization within the notebook.
```
#@ INITIALIZATION:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
```... | github_jupyter |
# Week 1: Mean/Covariance of a data set and effect of a linear transformation
In this week, we are going to investigate how the mean and (co)variance of a dataset changes
when we apply affine transformation to the dataset.
## Learning objectives
1. Get Farmiliar with basic programming using Python and Numpy/Scipy.
2.... | github_jupyter |
## Variance between Infomap Runs
In this notebook, we check the robustness of our results against the randomness inherent in the `infomap` algorithm
as reported in the SI.
In short, we investigate the question:
How much variance does there exist between infomap runs with different seeds?
### Preparations
```
from c... | github_jupyter |
## PS2-1 Convexity of Generalized Linear Models
#### (a)
Output:
```
==== Training model on data set A ====
Finished 10000 iterations
Finished 20000 iterations
Finished 30000 iterations
Converged in 30395 iterations
==== Training model on data set B ====
Finished 10000 iterations
Finished 20000 iterations
Finished 3... | github_jupyter |
**This code was adapted from [Alexander Held's "Example of a differentiable analysis" repository](https://github.com/alexander-held/differentiable-analysis-example/)**
```
from jax import grad, vmap, jit
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
plt.rcParams.update(
... | github_jupyter |
# codecentric.AI Bootcamp - Random Forests
## Aufgaben
Hier findet ihr eine Reihe von Übungsaufgaben zu Random Forests.
Folge den Aufgaben und ergänze die ___ in den Code-Abschnitten.
Die folgenden Pakete werden geladen:
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
... | github_jupyter |
# Plagiarism Detection, Feature Engineering
In this project, you will be tasked with building a plagiarism detector that examines an answer text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided, source text.
Your first ... | github_jupyter |
# Tensor Transformations
```
from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
```
NOTE on notation
* _x, _y, _z, ...... | github_jupyter |
<a href="https://colab.research.google.com/github/Priyam145/MLprojects/blob/main/notebooks/Statistics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
np.random.seed = 42
n... | github_jupyter |
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