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## Классная работа
Является ли процесс ($X_n$) мартингалом по отношению к фильтрации $\mathcal{F}_n$?
1. $z_1,z_2,\ldots,z_n$ — независимы и $z_i\sim N(0,49)$, $X_n=\sum_{i=1}^n z_i$. Фильтрация: $\mathcal{F}_n=\sigma(z_1,z_2,\ldots,z_n);$
2. $z_1,z_2,\ldots,z_n$ — независимы и $z_i\sim U[0,1]$, $X_n=\sum_{i=1}^n z_... | github_jupyter |
# Sistema de Recomendação com a biblioteca Surprise
Aqui nós vamos implementar um simples **Sistema de Recomendação** com a biblioteca **[Surprise](http://surpriselib.com/)**. O objetivo principal é testar alguns dos recursos básico da biblioteca.
## 01 - Preparando o Ambiente & Conjunto de dados
### 01.1 - Baixando ... | github_jupyter |
<a href="https://colab.research.google.com/github/sortsammcdonald/edx-python_and_data_science/blob/master/final_project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Determining Iris species based on simple metrics
This report will evaluate if ... | github_jupyter |
# Initialization
Welcome to the first assignment of "Improving Deep Neural Networks".
Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.
If you completed the previous course of this specialization, you probably followed our ins... | github_jupyter |
# Deep Crossentropy method
In this section we'll extend your CEM implementation with neural networks! You will train a multi-layer neural network to solve simple continuous state space games. __Please make sure you're done with tabular crossentropy method from the previous notebook.__
^T, \textbf{I})$ for <span style="color: blue">BLUE</span>
- $N((0, 1)^T, \textbf{I})$ for <span style="color: orange">ORANGE<... | github_jupyter |
<a href="https://colab.research.google.com/github/JerKeller/2022_ML_Earth_Env_Sci/blob/main/S4_3_THOR.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img src='https://unils-my.sharepoint.com/:i:/g/personal/tom_beucler_unil_ch/ESLP1e1BfUxKu-hchh7wZK... | github_jupyter |
```
import re
import json
import matplotlib.pylab as plt
import numpy as np
import glob
%matplotlib inline
all_test_acc = []
all_test_err = []
all_train_loss = []
all_test_loss = []
all_cardinalities = []
all_depths = []
all_widths = []
for file in glob.glob('logs_cardinality/Cifar2/*.txt'):
with open(file) as logs... | github_jupyter |
```
import numpy as np
import spectral_embedding as se
import matplotlib as mpl
import matplotlib.pyplot as plt
```
In this example we demostrate unfolded adjacency spectral embedding for a series of stochastic block models and investigate the stability of the embedding compared to two other possible approaches; omnib... | github_jupyter |
# Using an external master clock for hardware control of a stage-scanning high NA oblique plane microscope
Tutorial provided by [qi2lab](https://www.shepherdlaboratory.org).
This tutorial uses Pycro-Manager to rapidly acquire terabyte-scale volumetric images using external hardware triggering of a stage scan optimiz... | github_jupyter |
# Distribution Plots
Let's discuss some plots that allow us to visualize the distribution of a data set. These plots are:
* distplot
* jointplot
* pairplot
* rugplot
* kdeplot
___
## Imports
```
import seaborn as sns
%matplotlib inline
```
## Data
Seaborn comes with built-in data sets!
```
tips = sns.load_dataset... | github_jupyter |
<table align="center">
<td align="center"><a target="_blank" href="http://introtodeeplearning.com">
<img src="http://introtodeeplearning.com/images/colab/mit.png" style="padding-bottom:5px;" />
Visit MIT Deep Learning</a></td>
<td align="center"><a target="_blank" href="https://colab.research.google.c... | github_jupyter |
# EUC Calibration Experiment from David Halpern
For the vertical spacing, what are the ECCOv4r4 vertical layers from the surface to 400 m depth? At the equator (0°), the vertical profile of the zonal velocity component is: 0.1 m s*-1 towards the west from the sea surface at 0 m to 20 m depth; 0.5 m s*-1 towards the... | github_jupyter |
Cross-shelf transport (total) of CNTDIFF experiments
==
This notebook explores the similarities and differences between the 2 tracer transports for case CNTDIFF as well as canyon and no canyon cases. It looks at the transport normal to a shelf break wall<sup>1</sup>. Total Tracer Transport (TracTrans) is understood he... | github_jupyter |
```
import pandas as pd
import numpy as np
from datetime import datetime
from sqlalchemy import create_engine
import requests
from time import sleep
import warnings
warnings.filterwarnings('ignore')
df = pd.read_excel("HistoricoCobranca.xlsx")
df["doc"] = df.apply(lambda x : x["CNPJ"].replace(".", "").replace("-", "").... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os
import sys
from pathlib import Path
ROOT_DIR = os.path.abspath(os.path.join(Path().absolute(), os.pardir))
sys.path.insert(1, ROOT_DIR)
import numpy as np
import scipy
import matplotlib.pyplot as plt
from frequency_response import FrequencyResponse
from biquad import pea... | github_jupyter |
```
!pip install -q blackjax
!pip install -q distrax
import jax
import jax.numpy as jnp
import jax.scipy.stats as stats
from jax.random import PRNGKey, split
try:
import distrax
except ModuleNotFoundError:
%pip install -qq distrax
import distrax
try:
from tensorflow_probability.substrates.jax.distribut... | github_jupyter |
# Direct Grib Read
If you have installed more recent versions of pygrib, you can ingest grib mosaics directly without conversion to netCDF. This speeds up the ingest by ~15-20 seconds. This notebook will also demonstrate how to use MMM-Py with cartopy, and how to download near-realtime data from NCEP.
```
from __futu... | github_jupyter |
# Lab 01 : MLP -- demo
# Understanding the training loop
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from random import randint
import utils
```
### Download the data
```
from utils import check_mnist_dataset_exists
data_path=check_mnist_dataset_exists()
train... | github_jupyter |
```
# header files
import torch
import torch.nn as nn
import torchvision
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from google.colab import drive
drive.mount('/content/drive')
np.random.seed(1234)
torch.manual_seed(1234)
torch.cuda.manual_seed(1234)
# define transforms
train_transforms = torc... | github_jupyter |
Deep Learning
=============
Assignment 1
------------
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used... | github_jupyter |
# MLB's Biggest All-Star Injustices
```
# Import dependencies
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 100)
pd.options.mode.chained_assignment = None
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.m... | github_jupyter |
```
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
np.random.seed(0)
from statistics import mean
```
今回はアルゴリズムの評価が中心の章なので,学習アルゴリズム実装は後に回し、sklearnを学習アルゴリズムとして使用する。
```
import sklearn
```
今回、学習に使うデータはsin関数に正規分布$N(\varepsilon|0,0.05)$ノイズ項を加えたデータを使う
```
size = 100
max_degree = 11
x_data = np.rand... | github_jupyter |
___
<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>
___
<center><em>Content Copyright by Pierian Data</em></center>
# Warmup Project Exercise
## Simple War Game
Before we launch in to the OOP Milestone 2 Project, let's walk through together on using OOP for a more robust... | github_jupyter |
```
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
The raw code for this IPython notebook is by default hidden for easier rea... | github_jupyter |
# H2O Tutorial: EEG Eye State Classification
Author: Erin LeDell
Contact: erin@h2o.ai
This tutorial steps through a quick introduction to H2O's R API. The goal of this tutorial is to introduce through a complete example H2O's capabilities from R.
Most of the functionality for R's `data.frame` is exactly the same s... | github_jupyter |
<a href="https://colab.research.google.com/github/issdl/from-data-to-solution-2021/blob/main/4_metrics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Metrics
## Imports
```
import numpy as np
np.random.seed(2021)
import random
random.seed(2021)... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, roc_auc_score, make_scorer, accuracy_score
from xgboost import XGBClassifi... | github_jupyter |
<hr style="margin-bottom: 40px;">
<img src="https://user-images.githubusercontent.com/7065401/39117440-24199c72-46e7-11e8-8ffc-25c6e27e07d4.jpg"
style="width:300px; float: right; margin: 0 40px 40px 40px;"></img>
# Handling Missing Data with Pandas
pandas borrows all the... | github_jupyter |
<a href="https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/00_tensorflow_fundamentals.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 00. Getting started with TensorFlow: A guide to the fundamentals
## What is ... | github_jupyter |
# Libraries + DATA
```
from visualizations import *
import numpy as np
import pandas as pd
import warnings
from math import tau
import matplotlib.pyplot as plt
from scipy.integrate import quad
warnings.filterwarnings('ignore')
data = np.loadtxt("./../DATA/digits2k_pixels.data.gz", ndmin=2)/255.0
data.shape = (data.sha... | github_jupyter |
# Predicitng Stock/Weather prices using neural networks
## import relevant libraries
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from keras.models import Sequential
import matplotlib.patches as mpatches
from keras.layer... | github_jupyter |
```
import re
from robobrowser import RoboBrowser
import urllib
import os
class ProgressBar(object):
"""
链接：https://www.zhihu.com/question/41132103/answer/93438156
来源：知乎
"""
def __init__(self, title, count=0.0, run_status=None, fin_status=None, total=100.0, unit='', sep='/', chunk_size=1.0):
... | github_jupyter |
# Character-based LSTM
## Grab all Chesterton texts from Gutenberg
```
from nltk.corpus import gutenberg
gutenberg.fileids()
text = ''
for txt in gutenberg.fileids():
if 'chesterton' in txt:
text += gutenberg.raw(txt).lower()
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c i... | github_jupyter |
**Run the following two cells before you begin.**
```
%autosave 10
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
```
______________________________________________________________________
**First, import your data set and define t... | github_jupyter |
<a href="https://colab.research.google.com/github/ahammedshaneebnk/ML_Support_Vector_Machines_Exercises/blob/main/soft_margin_svm_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#**Question:**
```
import cv2
import matplotlib.pyplot as plt
import numpy as np
import time
import pandas as pd
#wd
%cd /content/drive/MyDrive/American_University/2021_Fall/DATA-793-001_Data Science Practicum/data
!pwd
```
# Exploratory Data Analysis
##Read the dat... | github_jupyter |
Check coefficients for integration schemes - they should all line up nicely for values in the middle and vary smoothly
```
from bokeh import plotting, io, models, palettes
io.output_notebook()
import numpy
from maxr.integrator import history
nmax = 5
figures = []
palette = palettes.Category10[3]
for n in range(1, nm... | github_jupyter |
# Operations on Word Vectors
Welcome to your first assignment of Week 2, Course 5 of the Deep Learning Specialization!
Because word embeddings are very computationally expensive to train, most ML practitioners will load a pre-trained set of embeddings. In this notebook you'll try your hand at loading, measuring simi... | github_jupyter |
### Notebook for the Udacity Project "Write A Data Science Blog Post"
#### Dataset used: "TripAdvisor Restaurants Info for 31 Euro-Cities"
https://www.kaggle.com/damienbeneschi/krakow-ta-restaurans-data-raw
https://www.kaggle.com/damienbeneschi/krakow-ta-restaurans-data-raw/downloads/krakow-ta-restaurans-data-raw.zip/... | github_jupyter |
```
import os, sys, time, copy
import random
import numpy as np
import matplotlib.pyplot as plt
import multiprocessing
from functools import partial
from tqdm import tqdm
import myokit
sys.path.append('../')
sys.path.append('../Protocols')
sys.path.append('../Models')
sys.path.append('../Lib')
import protocol_lib
im... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
plt.rcParams["savefig.dpi"] = 300
plt.rcParams["savefig.bbox"] = "tight"
np.set_printoptions(precision=3, suppress=True)
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from skl... | github_jupyter |
```
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import os
from scipy.stats import linregress
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import LabelEncoder, LabelBinarizer
from sklearn.model_selection import train_test_split, GridSearchCV, R... | github_jupyter |
# How to setup Seven Bridges Public API python library
## Overview
Here you will learn the three possible ways to setup Seven Bridges Public API Python library.
## Prerequisites
1. You need to install _sevenbridges-python_ library. Library details are available [here](http://sevenbridges-python.readthedocs.io/en/late... | github_jupyter |
Manipulating numbers in Python
================
**_Disclaimer_: Much of this section has been transcribed from <a href="https://pymotw.com/2/math/">https://pymotw.com/2/math/</a>**
Every computer represents numbers using the <a href="https://en.wikipedia.org/wiki/IEEE_floating_point">IEEE floating point standard</a>... | github_jupyter |
# Seldon-Core Component Demo
If you are reading this then you are about to take Seldon-Core, a model serving framework, for a test drive.
Seldon-Core has been packaged as a [combinator component](https://combinator.ml/components/introduction/), which makes it easy to spin up a combination of MLOps components to make ... | github_jupyter |
```
import numpy as np
import random
twopi = 2.*np.pi
oneOver2Pi = 1./twopi
import time
def time_usage(func):
def wrapper(*args, **kwargs):
beg_ts = time.time()
retval = func(*args, **kwargs)
end_ts = time.time()
print("elapsed time: %f" % (end_ts - beg_ts))
return retval
... | 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 a... | github_jupyter |
# Génération de données synthétiques
```
import numpy as np
import pandas as pd
from math import exp, log, log10, sqrt
from scipy.integrate import odeint
from scipy.stats import norm, lognorm
# The Complete model
def deriv(y, t, phiS, phiL, deltaS, deltaL, deltaAb):
dydt = phiS * exp(-deltaS * t) + phiL * exp(-de... | github_jupyter |
# Discretization
---
In this notebook, you will deal with continuous state and action spaces by discretizing them. This will enable you to apply reinforcement learning algorithms that are only designed to work with discrete spaces.
### 1. Import the Necessary Packages
```
import sys
import gym
import numpy as np
i... | github_jupyter |
## Energy Generation Analysis
- This script analyzes energy generation and fuel stock data published by the U.S Energy Information Administration
- The data used includes energy generation data from across the country
- Also included are stock levels of fuels used, including oil, coal, petcoke, and boiler fuels
- Data ... | github_jupyter |
```
pip install pyspark
pip install sklearn
pip install pandas
pip install seaborn
pip install matplotlib
import pandas as pd
import numpy as np
import os
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from ... | github_jupyter |
# GRIP_JULY - 2021 (TASK 5)
# Task Name:- Traffic sign classification/Recognition
# Domain:- Computer Vision and IOT
# Name:- Akash Singh
```
import cv2
import numpy as np
from scipy.stats import itemfreq
def get_dominant_color(image, n_colors):
pixels = np.float32(image).... | github_jupyter |
# Making Predictions with the Standardized Coefficients
## Import the relevant libraries
```
# For these lessons we will need NumPy, pandas, matplotlib and seaborn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# and of course the actual regression (machine learning) module
from sklearn.linea... | github_jupyter |
## Synthetic spectra generator
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
max_features = 15
n_points = 640
nu = np.linspace(0,1,n_points)
def random_chi3():
"""
generates a random spectrum, without NRB.
output:
params = matrix of parameters. each row corresponds to... | github_jupyter |
# NLP-Cube local installation
To be able to use NLP-Cube to the fullest (train models, export them, change the network structure, etc.) we need a clone of the NLP-Cube repository.
NLP-Cube requires a number of dependencies installed. This tutorial will show each step in detail. We'll install on a fresh Ubuntu 18.04 w... | github_jupyter |
```
#DATA TAKEN ON 5/1
import pandas as pd
import numpy as np
from sodapy import Socrata
client = Socrata("data.sfgov.org", None)
# results = client.get("cuks-n6tp", limit = 2191368)
data = client.get("cuks-n6tp", limit = 3000000)
data_df = pd.DataFrame.from_records(data)#here
print(data_df.shape)
data_df.columns
data_... | github_jupyter |
# Distribution function for the NFW profiles
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
from scipy.integrate import quad, cumtrapz
from tqdm import tqdm
import matplotlib as mpl
mpl.rcParams['font.size'] = 18.0
G_N = 4.302e-3
c = 100
def f_NFW(x):
retu... | github_jupyter |
# Markov Random Fields for Collaborative Filtering (Memory Efficient)
This notebook provides a **memory efficient version** in Python 3.7 of the algorithm outlined in the paper
"[Markov Random Fields for Collaborative Filtering](https://arxiv.org/abs/1910.09645)"
at the 33rd Conference on Neural Information Processi... | github_jupyter |
<a href="https://colab.research.google.com/github/HenriqueCCdA/bootCampAluraDataScience/blob/master/modulo1/desafios/Desafio_aula5_modulo1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import matplotlib.pyplot as plt
import... | github_jupyter |
<a href="https://colab.research.google.com/github/emadphysics/Amsterdam_Airbnb_predictive_models/blob/main/airbnb_pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import numpy as np
from datetime import date
import ... | github_jupyter |
```
import panel as pn
pn.extension()
```
One of the main design goals for Panel was that it should make it possible to seamlessly transition back and forth between interactively prototyping a dashboard in the notebook or on the commandline to deploying it as a standalone server app. This section shows how to display ... | github_jupyter |
```
import os
import numpy as np
import scipy.stats as sps
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random
import sys, os
sys.path += [os.path.abspath(os.pardir + '/code')]
print(sys.path)
from experiment import init_random_state, BanditLoopExperiment, get_ts_model
sns.set(f... | github_jupyter |
# Emulators: First example
This example illustrates Bayesian inference on a time series, using [Adaptive Covariance MCMC](http://pints.readthedocs.io/en/latest/mcmc_samplers/adaptive_covariance_mcmc.html) with emulator neural networks .
It follows on from [Sampling: First example](../sampling/first-example.ipynb)
Li... | github_jupyter |
# Bias Reduction
Climate models can have biases towards different references. Commonly, biases are reduced by postprocessing before verification of forecasting skill. `climpred` provides convenience functions to do so.
```
import climpred
import xarray as xr
import matplotlib.pyplot as plt
from climpred import Hindca... | github_jupyter |
```
import os
import sys
import random
import math
import re
import time
import numpy as np
from keras import backend as K
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.abspath("../..")
# Import Mask RCNN
sys.path.append(ROOT_DIR)
from mrcnn import utils
impor... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import gin
import numpy as np
from matplotlib import pyplot as plt
from torch.autograd import Variable
from tqdm.auto import tqdm
import torch
from causal_util.helpers import lstdct2dctlst
from sparse_causal_model_learner_rl.sacre... | github_jupyter |
```
import os
import threading
import gym
import multiprocessing
import numpy as np
from queue import Queue
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, optimizers
class ActorCriticModel(keras.Model):
def __init__(self... | github_jupyter |
```
class DF2Paths():
def __init__(self, path, fps=24):
self.path, self.fps = path, fps
def __call__(self, item:pd.Series):
def fr(t): return int(float(t)*self.fps)
Id, start, end = item['id'], item['start'], item['end']
start, end = fr(start), fr(end)
step ... | github_jupyter |
# Using `bw2landbalancer`
Notebook showing typical usage of `bw2landbalancer`
## Generating the samples
`bw2landbalancer` works with Brightway2. You only need set as current a project in which the database for which you want to balance land transformation exchanges is imported.
```
import brightway2 as bw
import nu... | github_jupyter |
# Analyzing volumes for word frequencies
This notebook will demonstrate some of basic functionality of the Hathi Trust FeatureReader object. We will look at a few examples of easily replicable text analysis techniques — namely word frequency and visualization.
```
%%capture
!pip install nltk
from htrc_features import ... | github_jupyter |
```
import json
from matplotlib import pyplot as plt
import numpy as np
import statistics
n_gen = 200 # number of generatons
n = 10 # number of generations to group
```
Read files
```
with open("Results_vehicle_ga.json", "r") as f:
ga = json.load(f)
with open("Results_vehicle_mo.json", "r") as f:
mo = js... | github_jupyter |
# Correlation and Causation
It is hard to over-emphasize the point that **correlation is not causation**!. Variables can be highly correlated for any number of reasons, none of which imply a causal relationship.
When trying to understand relationships between variables, it is worth the effort to think carefully a... | github_jupyter |
# Modélisation thématique
Dans ce notebook, nous effectuons de la modélisation thématique de textes à l'aide de modules Python spécialisés.
**IMPORTANT**: ce notebook requiert le module `java`. S'il n'était pas chargé au moment d'ouvrir ce notebook, vous devez le fermer, l'arrêter, charger le module `java` et rouvrir ... | github_jupyter |
# Fraud_Detection_Using_ADASYN_OVERSAMPLING
I am able to achieve the following accuracies in the validation data. These results can be further improved by reducing the
parameter, number of frauds used to create features from category items. I have used a threshold of 100.
* Logistic Regression :
Validation ... | github_jupyter |
# Boston Housing Prices Classification
```
import itertools
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from dataclasses import dataclass
from sklearn import datasets
from sklearn import svm
from sklearn import tree
from sklearn.ensemble import AdaBoostClassifier
f... | github_jupyter |
# ALL/AML Classifier
## Research Description
### Summary of the relative research
- **Index of the papers discussed**
| Paper ID | Title | Published Year |
|----------|-------|----------------|
|1 |_Molecular classification of cancer: class discovery and class prediction by gene expression monitoring_|1999/10/15|
... | github_jupyter |
# Increase Transparency and Accountability in Your Machine Learning Project with Python and H2O
#### Explain your complex models with decision tree surrogates, GBM feature importance, and reason codes
Decision trees and decision tree ensembles are some of the most popular machine learning models used in commercial pra... | github_jupyter |
[](http://rpi.analyticsdojo.com)
<center><h1>Intro to Tensorflow - MINST</h1></center>
<center><h3><a href = 'http://rpi.analyticsdojo.com'>rpi.analyticsdojo.com</a></h3></center>
[](https://colab.research... | github_jupyter |
```
# code reference: https://github.com/jeffheaton/t81_558_deep_learning/blob/dce2306815d4ac7c6443a01c071901822d612c6a/t81_558_class_06_4_keras_images.ipynb
%matplotlib inline
from PIL import Image, ImageFile
from matplotlib.pyplot import imshow
import requests
import numpy as np
from io import BytesIO
from IPython.d... | github_jupyter |
## 3.2 autograd
用Tensor训练网络很方便，但从上一小节最后的线性回归例子来看，反向传播过程需要手动实现。这对于像线性回归等较为简单的模型来说，还可以应付，但实际使用中经常出现非常复杂的网络结构，此时如果手动实现反向传播，不仅费时费力，而且容易出错，难以检查。torch.autograd就是为方便用户使用，而专门开发的一套自动求导引擎，它能够根据输入和前向传播过程自动构建计算图，并执行反向传播。
计算图(Computation Graph)是现代深度学习框架如PyTorch和TensorFlow等的核心，其为高效自动求导算法——反向传播(Back Propogation)提供了理论支持，了解计算图在实际写程序过... | github_jupyter |
# COVID-19 Deaths Per Capita
> Comparing death rates adjusting for population size.
- comments: true
- author: Joao B. Duarte & Hamel Husain
- categories: [growth, compare, interactive]
- hide: false
- image: images/covid-permillion-trajectories.png
- permalink: /covid-compare-permillion/
```
#hide
import numpy as n... | github_jupyter |
```
# Required to load webpages
from IPython.display import IFrame
```
[Table of contents](../toc.ipynb)
<img src="https://github.com/scipy/scipy/raw/master/doc/source/_static/scipyshiny_small.png" alt="Scipy" width="150" align="right">
# SciPy
* Scipy extends numpy with powerful modules in
* optimization,
* ... | github_jupyter |
# ¿Cómo funciona la suspensión de un auto?
<div>
<img style="float: left; margin: 0px 0px 15px 0px;" src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/ce/Packard_wishbone_front_suspension_%28Autocar_Handbook%2C_13th_ed%2C_1935%29.jpg/414px-Packard_wishbone_front_suspension_%28Autocar_Handbook%2C_13th_ed%2C... | github_jupyter |
## RaTG13 de-novo assembly - plots of assembled nucleotide sequences
Multiple preprints have questioned the validity of the metagenomic dataset upon which RaTG13 is based (Zhang, 2020; Rahalkar and Bahulikar, 2020; Singla et al., 2020; Signus, 2020). Here we undertook de-novo assembly of the metagenomic dataset uploa... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# This code creates a virtual display to draw game images on.
# If you are running locally, just ignore it
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY"))==0:
!bash ../xvfb start
%env DISPLAY=:1... | github_jupyter |
# Financial Planning with APIs and Simulations
In this Challenge, you’ll create two financial analysis tools by using a single Jupyter notebook:
Part 1: A financial planner for emergencies. The members will be able to use this tool to visualize their current savings. The members can then determine if they have enough... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def take_and_clean(filename, keyword, print_info=False):
raw = pd.read_json(f'./data/giantsGates/{filename}.json')
ranks = raw[['creatorName', 'totalScore']]
ranks = ranks[ranks['creatorName'].str.contains(keyword)]
ranks = ra... | github_jupyter |
# Visualizing Sorting Algorithm Behavior
```
import numpy as np
import random
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import time
import scipy.signal
def generateRandomList(n):
# Generate list of integers
# Possible alternative: l = [random.randint(0, n) for _ in range(n)]
... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Objectives" data-toc-modified-id="Objectives-1"><span class="toc-item-num">1 </span>Objectives</a></span></li><li><span><a href="#Example-Together" data-toc-modified-id="Example-Together-2"><span... | github_jupyter |
## Dependencies
```
import json, warnings, shutil
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras.models import Model
from tensorflow.keras import optimiz... | github_jupyter |
__This notebook__ trains resnet18 from scratch on CIFAR10 dataset.
```
%load_ext autoreload
%autoreload 2
%env CUDA_VISIBLE_DEVICES=YOURDEVICEHERE
import os, sys, time
sys.path.insert(0, '..')
import lib
import numpy as np
import torch, torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
%m... | github_jupyter |
```
#!pip install tensorflow
import pandas as pd
#import tensorflow as tf
#from tensorflow import keras
#from tensorflow.keras.models import Sequential
#from tensorflow.keras.layers import Activation, Dense
import matplotlib.pyplot as plt
x = [-1, 0, 1, 2, 3, 4]
y = [-3, -1, 1, 3, 5, 7]
df = pd.DataFrame({
"x": [... | github_jupyter |
## Homework-3: MNIST Classification with ConvNet
### **Deadline: 2021.04.06 23:59:00 **
### In this homework, you need to
- #### implement the forward and backward functions for ConvLayer (`layers/conv_layer.py`)
- #### implement the forward and backward functions for PoolingLayer (`layers/pooling_layer.py`)
- #### i... | github_jupyter |
#### Import the libraries and load the data
```
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
from gensim.models import Word2Vec
import matplotlib.pyplot as plt
%matplotlib inline
import warnings;
warnings.filterwarnings('ignore')
onlineData = pd.read_excel('./Data/Online Retail.xlsx')
o... | github_jupyter |
# YOLOv5 Training on Custom Dataset
## Pre-requisite
- Make sure you read the user guide from the [repository](https://github.com/CertifaiAI/classifai-blogs/tree/sum_blogpost01/0_Complete_Guide_To_Custom_Object_Detection_Model_With_Yolov5)
- Upload this to Google Drive to run on Colab.
*This script is written pri... | github_jupyter |
**ZuckFlix**
*A data analysis using Python to give a streaming proposal.*
---
By: Josue Salvador Cano Martinez.
Facebook Data Challenge 2021. California, United States.
Importing libraries.
Analyzing DB in a general way.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn... | github_jupyter |
## Calculations and plots for blog post
## Ice Lake Xeon Platinum 8352Y results ... with old data ...
### TR 3990x vs 3970x vs 3265W Performance and Scaling and rocket lake stuff
These are typical imports I do for almost any data analysis
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
... | github_jupyter |
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