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# Statistics review 3: Hypothesis testing and P values
R code accompanying [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC137449/pdf/cc1493.pdf)
## Key learning points
- A P value is the probability that an observed effect is simply due to chance; it therefore provides a measure of the strength of an associati... | 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 |
# California housing dataset with linear and polynomial regression
In this notebook, we'll use [linear regression](https://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares), [regularized linear regression](https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression), and [polyno... | github_jupyter |
# Preparing a computer vision training set for labeling photos
This notebook covers the steps involved in preparing a labeled dataset of images derived from the Newspaper Navigator dataset. Specifically this training set is primarily intended for use in a Programming Historian lesson on computer vision.
## Aims
Th... | github_jupyter |
# Xarray-spatial
### User Guide: Focal
#### Use datashader to render our images...
To get started, we'll import numpy and xarray-spatial, along with datashader and a set of its functions to help us quickly render images.
```
import numpy as np
import datashader as ds
from datashader.transfer_functions import shade
fr... | github_jupyter |
# Astronomy 8824 - Numerical and Statistical Methods in Astrophysics
## Introduction to Clustering
These notes are for the course Astronomy 8824: Numerical and Statistical Methods in Astrophysics and were written by Paul Martini.
#### Background reading:
- Statistics, Data Mining, and Machine Learning in Astronomy... | github_jupyter |
# Bagging元估计器
`Bagging`是`Bootstrap Aggregating`的简称,意思就是再取样(`Bootstrap`)然后在每个样本上训练出来的模型进行集成.
通常如果目标是分类,则集成的方式是投票;如果目标是回归,则集成方式是取平均.
在集成算法中,`bagging`方法会在原始训练集的随机子集上构建一类黑盒估计器的多个实例,然后把这些估计器的预测结果结合起来形成最终的预测结果.
该方法通过在训练模型的过程中引入随机性,来减少基估计器的方差(例如，决策树).在多数情况下,`bagging`方法提供了一种非常简单的方式来对单一模型进行改进,而无需修改背后的算法.因为`bagging`方法可以减小过拟... | github_jupyter |
<a href="https://www.bigdatauniversity.com"><img src="https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png" width="400" align="center"></a>
<h1 align=center><font size="5"> SVM (Support Vector Machines)</font></h1>
In this notebook, you will use SVM (Support Vector Machines) to build and train a mod... | github_jupyter |
##### Copyright 2020 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 |
<h1> Training on Cloud ML Engine </h1>
This notebook illustrates distributed training and hyperparameter tuning on Cloud ML Engine.
```
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ[... | github_jupyter |
# Explore UK Crime Data with Pandas and GeoPandas
## Table of Contents
1. [London boroughs](#boroughs)<br>
2. [Crime data](#crime)<br>
2.1. [Load data](#load2)<br>
2.2. [Explore data](#explore2)<br>
<div class="alert alert-danger" style="font-size:100%">
When you are using <b>Watson Studio</b> to run the wo... | github_jupyter |
# Hybrid Monte Carlo
## Payoff Scripting
In this notebook we demonstrate the setup and use of *Payoff* objects. This is structured along the following steps:
1. Specifying and using basic payoffs
2. Combining basic payoffs to form complex payoff structures
3. Simulate future payoffs with Monte Carlo
4. S... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
## Visual Comparison Between Different Classification Methods in Shogun
Notebook by Youssef Emad El-Din (Github ID: <a href="https://github.com/youssef-emad/">youssef-emad</a>)
This notebook demonstrates different classification methods in Shogun. The point is to compare and visualize the decision boundaries of diffe... | github_jupyter |
# RESULTS OF THE SET OF SIMULATIONS
## Loading results
```
%matplotlib notebook
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from thermalspin.data_analysis import *
# Insert here the name of the simulation set
setname = "heisenberg_2D"
final_state_lst, L_lst, t_lst, J_lst, h_lst, T... | github_jupyter |
# 1. Imports
```
import sqlite3
```
# 2. Connect to the database
```
# connect
conn = sqlite3.connect('../../data/minority-state-owned-ases/sqlite/minority_state_owned_ases.sqlite')
# create a cursos
cur = conn.cursor()
```
# 3. Get insights of the dataset
## 3.1 Example of organizations table
Table schema
- ```t... | github_jupyter |
```
import keras
keras.__version__
```
# Advanced usage of recurrent neural networks
>#### This notebook contains the code samples found in Chapter 6, Section 3 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far ... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#Name" data-toc-modified-id="Name-1"><span class="toc-item-num">1 </span>Name</a></span></li><li><span><a href="#Search" data-toc-modified-id="Search-2"><span class="toc-i... | github_jupyter |
# Imports
```
import os
import h5py
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from IPython import display
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from sklearn import metrics, manifold... | github_jupyter |
# Keras mnist LeNet-5 v2
**此项目为测试修改版的LeNet-5，并且使用图像增强，调节学习率, 使用BatchNormal**
- 目前能在测试集上达到$0.9952$的准确率
```
%matplotlib inline
import os
import PIL
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from IPython import display
from functools import... | github_jupyter |
# Notes
## selection of variable
### Principal Variables
iterative search of variables that covariates more with Y response vector. After the first PV is found, the matrix is reduced to find the next one.
KW: supervised methods
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L., & Engelsen, S. B. (2... | github_jupyter |
<a href="https://colab.research.google.com/github/mrdbourke/pytorch-resnet-twitch/blob/main/resnet50_twitch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!nvidia-smi
!pip install torchinfo
import torchinfo
import os
import torch
import torchv... | github_jupyter |
```
from matplotlib import pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, TimeDistributed, Bidirecti... | github_jupyter |
# Robustness in Regulatory Networks
## A tutorial for BoolNet and BoolNetPerturb
* [Introduction](#Introduction)
* [Robustness and plasticity in biological systems](#Robustness-and-plasticity-in-biological-systems)
* [Regulatory Networks](#Regulatory Networks)
* [Biological system: Th17/iTreg network](#Bio... | github_jupyter |
# Load/save and structure data
Let's first take a quick survey on the Inaugural assignment process, found [here](https://forms.office.com/Pages/ResponsePage.aspx?id=kX-So6HNlkaviYyfHO_6kckJrnVYqJlJgGf8Jm3FvY9UMEZTODYyVjJWSFBPNTVRMzBMQzFYOE5JQiQlQCN0PWcu).
You will learn to **load and save data** both to and from offl... | github_jupyter |
# Introduction to Programming with Python
# Unit 5: Nested Loops
Let us start with revisiting the exercise from the last unit. We needed to:
1. Write a function `fact` that will calculate a factorial $n!=1\cdot2\cdot\dots\cdot n$
2. Print a table of factorials from 1 to 7
Let's start with a function:
```
def fact(... | github_jupyter |
Водопьян А.О. Хабибуллин Р.А. 2019 г.
## Газосодержание
<a id="Rs"></a>
### Газосодержание, корреляция Стендинга
<a id="Rs_Standing"></a>
Для расчета газосодержания используется корреляция, обратная корреляции Стендинга для давления насыщения нефти газом.
$$ R_s = \gamma_g \left( \frac{1.92 p}{\ 10^{y_g}}\right)... | github_jupyter |
# What permutation tests to do?
I need to work out what permutation tests to do. There are several ways we can compare X:A or Y:A or 4:A.
These data are challenging because at the individual gene cell level the data are very sparse. One solution to this problem is to aggregate data to the cell type level. Unfortunat... | github_jupyter |
# Import Libraries
```
import numpy as np
import pandas as pd
```
# Import Data
```
# Import data.
loan_data_preprocessed_backup = pd.read_csv('loan_data_2007_2014_preprocessed.csv')
```
# Explore Data
```
loan_data_preprocessed = loan_data_preprocessed_backup.copy()
loan_data_preprocessed.columns.values
# Display... | github_jupyter |
```
#练习 1：求n个随机整数均值的平方根，整数范围在m与k之间。
import random,math
m = int(input('please input a smaller number '))
k = int(input('please input a bigger number '))
n = int(input('please input a number for times '))
i = 1
total = 0
avg = 0
num = random.randint(m,k)
print ('num0:',num)
total += num
while i < n :
num ... | github_jupyter |
# Copying Task
Inspired on the task described in the following paper: [https://arxiv.org/pdf/1511.06464.pdf](https://arxiv.org/pdf/1511.06464.pdf)
## Introduction
The copying task is one of the simplest benchmark tasks for recurrent neural networks.
The general idea of the task is to reproduce a random sequence of s... | github_jupyter |
```
#Necessary libraries
import numpy as np
import pandas as pd
import graphviz
import numexpr
import itertools
from subprocess import call
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.metrics import fbeta_score
from sklearn.... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
data = pd.read_csv('pokemon.csv')
data.info()
data.head(10)
#correlation map
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
```
## Matplotlib... | github_jupyter |
# Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow as tf
import numpy as np
tokenizer = tokenization.FullTokenizer(
vocab_file='albert-base-2020-04-10/sp10m.cased.v10.vocab', do_lower_case=False,
... | github_jupyter |
## K2-24 Fitting & MCMC
Using the K2-24 (EPIC-203771098) dataset, we demonstrate how to use the radvel API to:
- perform a max-likelihood fit
- do an MCMC exploration of the posterior space
- plot the results
### Circular Orbits
Perform some preliminary imports:
```
%matplotlib inline
import os
import matplotlib
... | github_jupyter |
##### Copyright 2021 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Cart-pole Balancing Model with Amazon SageMaker and Ray
---
## Introduction
In this notebook we'll start from the cart-pole balancing problem, where a pole is attached by an un-actuated joint to a cart, moving along a frictionless track. Instead of applying control theory to solve the problem, this example shows ho... | github_jupyter |
```
### This Notebook is intented to build a trainer
### using GPT2 and CNN daily mail
!nvidia-smi
import sys
sys.path.append("/home/USER/TF_NEW/tf-transformers/src/")
import tensorflow as tf
import tqdm
import time
import functools
import os
from hydra import initialize, initialize_config_module, initialize_config_di... | github_jupyter |
##### Copyright 2020 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 |
[source](../../api/alibi_detect.cd.cvm.rst)
# Cramér-von Mises
## Overview
The CVM drift detector is a non-parametric drift detector, which applies feature-wise two-sample [Cramér-von Mises](https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93von_Mises_criterion) (CVM) tests. For two empirical distributions $F(z)$ and... | github_jupyter |
# E(n)-Equivariant Steerable CNNs - Hands-on tutorial
We start by importing the necessary packages. The user typically only needs to interact with the high level functionalities provided in the subpackages `escnn.gspaces` and `escnn.nn`.
```
import torch
from escnn import gspaces
from escnn import nn
import numpy... | github_jupyter |
<img align="centre" src="../../Supplementary_data/dea_logo_wide.jpg" width="100%">
# Scalable Supervised Machine Learning on the Open Data Cube
* **Prerequisites:** This notebook series assumes some familiarity with machine learning, statistical concepts, and python programming. Beginners should consider working thro... | github_jupyter |
<div class="clearfix" style="padding: 10px; padding-left: 0px">
<img src="../resources/img/softbutterfly-logo.png" class="pull-left" style="display: block; height: 40px; margin: 0;"><img src="../resources/img/jupyter-logo.png" class="pull-right" style="display: block; height: 20px; margin-top: 10px;">
</div>
<h1>
Cur... | github_jupyter |
# Machine learning with SPARK in SQL Server 2019 Big Data Cluster
Spark in Unified Big data compute engine that enables big data processing, Machine learning and AI
Key Spark advantages are
1. Distributed compute enging
2. Choice of langauge (Python, R, Scala, Java)
3. Single engine for Batch and Streaming job... | github_jupyter |
# Implementing CEA calculations using Cantera
```
# this line makes figures interactive in Jupyter notebooks
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import cantera as ct
from pint import UnitRegistry
ureg = UnitRegistry()
Q_ = ureg.Quantity
# for convenience:
def to_si(quant):
... | github_jupyter |
# Bayesian Neural Network (VI) for regression
### Zhenwen Dai (2018-8-21)
```
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License i... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
#plt.show() is used when not using Jupyter
import numpy as np
x = np.linspace(0,5,11)
y = x ** 2
x
y
# Functional Method and then Object Oriented Method
# FUNCTIONAL
plt.plot(x, y, 'ro')
#plt.show()
plt.plot(x, y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.tit... | github_jupyter |
# Movie Recommendations
Recommendations are a common machine learning task widely used by many leading companies, such as Netflix, Amazon, and YouTube. If you have used any of these online services, you are familiar with recommendations that are often prefixed with "You might also like.." or "Recommended items other c... | github_jupyter |
# 13-Testing
```
from scipy import *
def bisect(f, a, b, tol=1.e-8):
"""
Implementation of the bisection algorithm
f real valued function
a,b interval boundaries (float) with the property
f(a) * f(b) <= 0
tol tolerance (float)
"""
if f(a) * f(b)> 0:
raise ValueError("Incorrect... | github_jupyter |
# Data exploration
Questions to ask:
1. How do values distribute for the main variable *search_interest*?
1. What are keywords with high search interest?
2. What is the average search interest ...
1. for a keyword?
1. for a keyword that has at least 1 entry > 0?
1. for a keyword that has at least 1 entr... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq mrl-pypi # upgrade mrl on colab
# default_exp train.buffer
```
# Buffer
> Callbacks for buffer
```
#hide
from nbdev.showdoc import *
%load_ext autoreload
%autoreload 2
# export
from mrl.imports import *
from mrl.core import *
from mrl.train.callback import *
fr... | github_jupyter |
# Setup
Note that this notebook was developed with NodePy version 0.7.
```
import numpy as np
import matplotlib.pyplot as plt
from nodepy import rk, stability_function
rk4 = rk.loadRKM('RK44').__num__()
rk4x2 = rk4*rk4
ssp2 = rk.loadRKM('SSP22').__num__()
ssp3 = rk.loadRKM('SSP33').__num__()
ssp104 = rk.loadRKM('SSP... | github_jupyter |
<a href="https://colab.research.google.com/github/facebookresearch/habitat-sim/blob/master/examples/tutorials/colabs/ECCV_2020_Interactivity.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Habitat-sim Interactivity
This use-case driven tutorial co... | 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 |
# Circuit Rewriting using the Transpiler
Previously we have performed basic operations on circuits, and ran those circuits on real quantum devices using the `execute` function. `execute` is a helper function that performs three tasks for the user:
1) Circuits are... | github_jupyter |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing.
## Spectral Shaping of the Quantization Noise
The quantized signal $x_Q[k]$ can be expressed by the continuous amplitude signal $x[k]$ and the quantization error $e[... | github_jupyter |
Code for generating results for the confounder-mediator graph (Figure 2(c) and Figure 3(c)).
```
import numpy
import sympy
import pandas
import numpy as np
import pandas as pd
import sympy as sp
import datetime
import copy
import attr
import time
import logging
import itertools
import pickle
import sys
import os
impor... | github_jupyter |
We will use the [CartPole-v1](https://gym.openai.com/envs/CartPole-v0/) OpenAI Gym environment. For reproducibility, let is fix a random seed.
```
import pytorch_lightning as pl
from reagent.gym.envs.gym import Gym
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
import... | github_jupyter |
# π Estimation with Monte Carlo methods
We demonstrate how to run Monte Carlo simulations with lithops over IBM Cloud Functions. This notebook contains an example of estimation the number π with Monte Carlo. The goal of this notebook is to demonstrate how IBM Cloud Functions can benefit Monte Carlo simulations and not ... | github_jupyter |
# Probabilistic Programming in Python using PyMC
Authors: John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck
## Introduction
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful,... | github_jupyter |
[this doc on github](https://github.com/dotnet/interactive/tree/master/samples/notebooks/csharp/Docs)
# Variable Sharing
.NET Interactive enables you to write code in multiple languages within a single notebook and in order to take advantage of those languages' different strengths, you might find it useful to share d... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/vae_mnist_pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# (Variational) autoencoders with CNNs on MNIST using PyTorch
Based on https://github.com/pro... | github_jupyter |
# Ensemble
Combines predictions of several estimators
## Methods
### Averaging Method
1. Several estimators are built independently and then their predictions are averaged
1. Better because variance is reduced
1. Works best with strong & complex models
1. e.g. [Bagging Methods](http://scikit-learn.org/stable/modules... | github_jupyter |
## Torch Core
This module contains all the basic functions we need in other modules of the fastai library (split with [`core`](/core.html#core) that contains the ones not requiring pytorch). Its documentation can easily be skipped at a first read, unless you want to know what a given function does.
```
from fastai.im... | github_jupyter |
**How to save this notebook to your personal Drive**
To copy this notebook to your Google Drive, go to File and select "Save a copy in Drive", where it will automatically open the copy in a new tab for you to work in. This notebook will be saved into a folder on your personal Drive called "Colab Notebooks".
Still stu... | github_jupyter |
```
import numpy as np
from plot_utils import read_Noise2Seg_results, fraction_to_abs, cm2inch
from matplotlib import pyplot as plt
plt.rc('text', usetex=True)
```
# Flywing n10: AP scores on validation data
```
alpha0_5_n10 = read_Noise2Seg_results('alpha0.5', 'flywing_n10', measure='AP', runs=[1,2,3,4,5],
... | github_jupyter |
# Getting started: import required modules
```
## Get dependencies ##
import numpy as np
import string
import math
import sys
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sn
from GIR import *
import scipy as sp
import pickle
import time
import scipy as sp
from scipy import n... | github_jupyter |
```
import random
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.framework import ops
ops.reset_default_graph()
sess = tf.Session()
mnist = input_data.read_data_sets("MNIST_data/", one_hot... | github_jupyter |
<img src="https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Attribution.png" width="400">
<h1 align="left">Multi-investment Attribution: Distinguish the Effects of Multiple Outreach Efforts</h1>
A startup that sells software would like to know whether its multiple outreach efforts were successful in attra... | github_jupyter |
# Statistical treatment for PASTIS
<font color='red'>**This notebook is outdated as of 9 May 2021.
Please use more recent notebooks for help.**</font>
Getting into a full statistical treatment of the WFE requirements both mode-based as well as segmnet-based, using normal distributions and covariance matrices.
1. s... | github_jupyter |
# Hyperparameter grid search
NB the input data to the DNN is not normalised.
Hyperparameter grid search adapted from Machine Learning Mastery
https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/
Using scikit-learn to grid search the batch size and epochs
```
import sys
fro... | github_jupyter |
## Colab Setup
```
# if you run this notebook in kaggle notebook or other platform, comment out the following codef
from google.colab import drive
drive.mount('/content/drive')
```
## Config
```
root = '/content/drive/MyDrive/Colab Notebooks/g2net/' # set your root directory in your google drive. if you use Kaggle n... | github_jupyter |
```
%matplotlib inline
```
Neural Networks
===============
使用torch.nn包来构建神经网络。
上一讲已经讲过了``autograd``，``nn``包依赖``autograd``包来定义模型并求导。
一个``nn.Module``包含各个层和一个``forward(input)``方法，该方法返回``output``。
例如：
它是一个简单的前馈神经网络，它接受一个输入，然后一层接着一层地传递，最后输出计算的结果。
神经网络的典型训练过程如下：
... | github_jupyter |
The `Estimator` APIs are a high-level API in Tensorflow or say a high-level representation of a model. It is designed for easy scaling and asynchronous training.
```
!pip install tf-nightly
import tensorflow as tf
import pandas as pd
print("Tensorflow Version: {}".format(tf.__version__))
print("Eager Model: {}".forma... | github_jupyter |
# Neural Network (Multilayer Perceptron) Demo
_Source: 🤖[Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) repository_
> ☝Before moving on with this demo you might want to take a look at:
> - 📗[Math behind the Neural Networks](https://github.com/trekhleb/homemade-machine-learning/tre... | github_jupyter |
```
import os
import tensorflow as tf
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
if tf.test.gpu_device_name():
print('GPU found')
else:
print("No GPU found")
from keras.datasets import reuters
(train_data, train_labels),(test_data, test_labels) = reuters.load_data(num_words=10000)
word_index = reuters.get_wor... | github_jupyter |
(linalg_eigen)=
# Eigenvalues and eigenvectors
To introduce eigenvalues and eigenvectors, let us begin with an example of matrix-vector multiplication. Consider the following square matrix $A \in \mathbb{R}^{2 \times 2}$ multiplying a vector $\mathbf{u}$:
$$ A \mathbf{u} =
\begin{pmatrix} 2 & 1 \\ 1 & 2 \end{pmatrix... | github_jupyter |
<a href="https://colab.research.google.com/github/raqueeb/Intermediate-scikit-learn/blob/master/feature_pipeline.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## ফিচার ইঞ্জিনিয়ারিং এবং পাইপলাইন
আমাদের আগের বইটাতে লিনিয়ার রিগ্রেশন নিয়ে বেশি ফোকাস ... | 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 |
```
import pandas as pd
import numpy as np
customer_data_2017 = pd.read_csv("mod2017.csv",index_col = 0)
customer_data_2018 = pd.read_csv("mod2018.csv",index_col = 0)
customer_data_2019 = pd.read_csv("mod2019.csv",index_col = 0)
customer_data = pd.read_csv("customer_data .csv")
customer_data_2017.isnull().sum()
custome... | github_jupyter |
# Natural language processing
```
import numpy as np
import pandas as pd
from sklearn import model_selection as ms, feature_extraction as fe, ensemble
from scipy.sparse import hstack
import spacy
from gensim.matutils import Sparse2Corpus
from gensim.models import LdaModel, Word2Vec
H2020_URL = 'http://cordis.europ... | github_jupyter |
## Partial Dependence (PDP) and Individual Conditional Expectation (ICE) plots
Partial Dependence Plot (PDP) and Individual Condition Expectation (ICE) are interpretation methods which describe the average behavior of a classification or regression model. They are particularly useful when the model developer wants to ... | github_jupyter |
<a href="https://colab.research.google.com/github/lenyabloko/SemEval2020/blob/master/SemEval2020_Paraphraser.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
UPLOAD FILES - Place [train.csv](https://github.com/arielsho/Subtask-1/archive/master.zip) a... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, pub... | github_jupyter |
# Imports
```
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.externals import joblib
import torch
import torchvision
import torchvisi... | github_jupyter |
## Tutorial Outline
**In this tutorial we will demonstrate how to:**
1. Use the new *Chempy* functions, which take the stellar birth-time as inputs
2. Create and train a neural network to emulate the *Chempy* with birth-time as a free parameter
3. Generate mock data-sets using *Chempy*. We will also describe the fi... | github_jupyter |
# Bayesian Statistics From Scratch
## Building up to MCMC
# Justin Bozonier
## Lead Data Scientist, GrubHub
### @databozo
### justin@bozonier.com
### http://www.databozo.com
# GETTING STARTED
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
* Probability and Bayes Theroem
* Infinite Hyp... | github_jupyter |
```
"""
We use following lines because we are running on Google Colab
If you are running notebook on a local computer, you don't need this cell
"""
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow1/semantic_parsing/tree_slu/main')
!pip install t... | 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 |
### Legenda
_**Definitie**_. Body.
⚠️ _**Eigenschap**_ bla.
## Gauss-eliminatie, echelon/rijgereduceerde vorm
_**Rij-equivalent**_.
Als een stelsel kan ontstaan door een opeenvolging van elementaire rijoperaties uit te voeren op een ander stelsel, dan zijn deze twee stelsels *rij-equivalent*.
_**Gebonden/basis vari... | github_jupyter |
```
# Load dependencies
import pandas as pd
import numpy as np
from scipy.stats import gmean
import sys
sys.path.insert(0, '../../statistics_helper/')
from excel_utils import *
```
# Estimating the biomass of Annelids
To estimate the total biomass of annelids, we rely on data collected in a recent study by [Fierer et ... | github_jupyter |
```
# Initialize Otter
import otter
grader = otter.Notebook("example.ipynb")
import matplotlib.pyplot as plt
import numpy as np
```
<!-- BEGIN QUESTION -->
**Question 1.** Assign `x` to the smallest prime number.
_Points:_ 16
```
x = 2 # SOLUTION
grader.check("q1")
```
<!-- END QUESTION -->
<!-- BEGIN QUESTION --... | github_jupyter |
```
!nvidia-smi
%cd /content/
!git clone https://github.com/westphal-jan/peer-data
%cd /content/peer-data
# !git checkout huggingface
!git submodule update --init --recursive
# !pip install pytorch-lightning wandb python-dotenv catalyst sentence-transformers numpy requests nlpaug sentencepiece nltk
# !pip install wandb... | github_jupyter |
## Welcome to the BioProv tutorials!
### Tutorial index
* <a href="./introduction.ipynb">Introduction to BioProv</a>
* <a href="./w3c-prov.ipynb">W3C-PROV projects</a>
* <a href="./workflows_and_presets.ipynb">Presets and Workflows</a>
## W3C-PROV projects
In the last tutorial we learned about how to start a **Proje... | github_jupyter |
# Image file formats
When working with microscopy image data, many file formats are circulating. Most microscope vendors bring proprietary image file formats, image analysis software vendors offer custom and partially open file formats. Traditional file formats exist as well which are supported by common python librari... | github_jupyter |
# Ens'IA - Session 3: Neural network
After having seen how both a neuron and backpropagation works, it is time to do some more serious business and make an ENTIRE neural network. Of course, we won't ask you to reprogram everything from the ground up! In order to build our neural network, we are going to use the famous... | github_jupyter |
# AutoML for Text Classification with Vertex AI
**Learning Objectives**
1. Learn how to create a text classification dataset for AutoML using BigQuery
1. Learn how to train AutoML to build a text classification model
1. Learn how to evaluate a model trained with AutoML
1. Learn how to predict on new test data with Au... | github_jupyter |
### *Before start: make sure you deleted the output_dir folder from this path*
# Some things we get for free by using Estimators
Estimators are a high level abstraction (Interface) that supports all the basic operations you need to support a ML model on top of TensorFlow.
Estimators:
* provide a simple interface f... | github_jupyter |
```
import os
import sys
import pandas as pd
import numpy as np
from scipy import stats
from sklearn.model_selection import RandomizedSearchCV, cross_val_score
from sklearn.pipeline import make_pipeline, make_union
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures, OneH... | github_jupyter |
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