text stringlengths 2.5k 6.39M | kind stringclasses 3
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```
%matplotlib notebook
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import scipy.optimize
```
# Diagnóstico de cancer usando un regresor logístico
Considere el dataset de [diagnóstico de cancer de mama de la Universidad de Wisconsin](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wi... | github_jupyter |
```
from __future__ import print_function
import sys
import numpy as np
from time import time
import matplotlib.pyplot as plt
from tqdm import tqdm
import math
import struct
import binascii
sys.path.append('/home/xilinx')
from pynq import Overlay
from pynq import allocate
SUDOKU_BLOCK_LEN = 3
SUDOKU_BOARD_LEN = SUDO... | github_jupyter |
#An example machine learning notebook
###Notebook by [Randal S. Olson](http://www.randalolson.com/)
####Supported by [Jason H. Moore](http://www.epistasis.org/)
####[University of Pennsylvania Institute for Bioinformatics](http://upibi.org/)
**It is recommended to [view this notebook in nbviewer](http://nbviewer.ipyt... | github_jupyter |
# Hyperparameter tuning by randomized-search
In the previous notebook, we showed how to use a grid-search approach to
search for the best hyperparameters maximizing the generalization performance
of a predictive model.
However, a grid-search approach has limitations. It does not scale when
the number of parameters to... | github_jupyter |
```
import numpy as np
import cv2
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.models import load_model
from os import listdir
import matplotlib.pyplot as plt
from keras.applications.imagenet_utils import preprocess_input
faceCascade= cv2.CascadeClassifier('haarcascade_frontalface_d... | github_jupyter |
# Introduction to Testing
Testing is an easy thing to understand but there is also an art to it as well; writing good tests often requires you to try to figure out *what input(s) are most likely to break your program*.
In addition to this, tests can serve different purposes as well:
* Testing for correctness
* Test... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import scipy.io as sio
import os
import subprocess
import bisect
import errno
import time
import pandas
import pickle
import num2word
from sklearn.decomposition import PCA
from sklearn.svm import SVC, SVR
from sklearn.metric... | github_jupyter |
```
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
# config = tf.ConfigProto()
# config.gpu_options.allocator_type = 'BFC' #A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.
# config.gpu_options.per_process_gpu_memory_fraction = 0.3
# config.gpu_options.all... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
```
# Downloading the dependencies
- Downloading the dataset from kaggle using the kaggle API
- Downloading pretrained GloVe embeddings
```
from IPython.display import clear_output
!pip install kaggle
%env KAGGLE_USERNAME=xerefic
%env KAGGLE_KEY=83aac... | 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 ... | github_jupyter |
# Run RERF C++ vs CySPORF Side By Side on CC-18 Dataset
```
%load_ext lab_black
import sys
import os
from pathlib import Path
import numpy as np
import collections
from tqdm import tqdm
from pathlib import Path
import time
import logging
import json
from collections import defaultdict
import pandas as pd
import seab... | github_jupyter |
# 用通用强化学习算法自我对弈，掌握国际象棋和将棋
[`程世东`](http://zhihu.com/people/cheng-shi-dong-47) 翻译
[`GitHub`](http://github.com/chengstone) [`Mail`](mailto:69558140@163.com)
国际象棋是人工智能史上研究最为广泛的领域。最强大的象棋程序是基于复杂的搜索技术、适应于特定领域、和过去几十年里人类专家手工提炼的评估函数的结合。相比之下，通过自我对弈进行“白板”强化学习，在围棋游戏中AlphaGo Zero取得了超越人类的成绩。在本文中，我们将这种方法推广到一个单一的AlphaZero算法中，从“白板”... | github_jupyter |
### Maximal Clique
A clique is a subset of a graph that each vertex is interconnected. A maximal clique is a clique that has reached its maximum degree. No extra vertex can be added into the clique so that each vertex is interconnected.
For details, you can check out this video.
https://www.youtube.com/watch... | github_jupyter |
Now we run this a second time, on the second (`b`) feature table that has removed all epithets with fewer than 27 representative documents. The results are better (overall F1 score for decision tree is `0.44`, random forest is `0.47`; in `a` these were `0.33` and `0.40`, respectively).
```
import os
from sklearn impor... | github_jupyter |
# Intrusion detection on NSL-KDD
This is my try with [NSL-KDD](http://www.unb.ca/research/iscx/dataset/iscx-NSL-KDD-dataset.html) dataset, which is an improved version of well-known [KDD'99](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html) dataset. I've used Python, Scikit-learn and PySpark via [ready-to-run J... | github_jupyter |
### `pysgrid` only works with raw netCDF4 (for now!)
```
from netCDF4 import Dataset
url = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/'
'jcwarner/Sandy/triple_nest/00_dir_NYB07.ncml')
#url = '00_dir_NYB05.nc'
nc = Dataset(url)
```
### The sgrid object
```
import pysgrid
sgrid = pysgri... | github_jupyter |
# Riemannian Optimisation with Pymanopt for Inference in MoG models
The Mixture of Gaussians (MoG) model assumes that datapoints $\mathbf{x}_i\in\mathbb{R}^d$ follow a distribution described by the following probability density function:
$p(\mathbf{x}) = \sum_{m=1}^M \pi_m p_\mathcal{N}(\mathbf{x};\mathbf{\mu}_m,\mat... | github_jupyter |
## TL;DR
Hey it's me hijacking **Shujian Liu**'s kernel again. Sorry for the clickbait (it works?), anyway this is what we did, and it's kind of embarassing:
- Various tests done by the author showed that the models tend to overfit/cease to improve after a certain number of epochs.
- Because of that, the whole purpo... | github_jupyter |
# BLU03 - Learning Notebook - Part 2 of 3 - HTTP requests
## 1. Introduction
In this notebook, you'll be introduced to the wonderful world of getting data from APIs (Application Programming Interfaces).
And APIs really are a fantastic data source because they can usually give access to structured data, very fast.
Bu... | github_jupyter |
# Classy Models
Before reading this, please go over the [Getting Started tutorial](https://classyvision.ai/tutorials/getting_started).
Working with Classy Vision requires models to be instances of `ClassyModel`. A `ClassyModel` is an instance of `torch.nn.Module`, but packed with a lot of extra features!
If your mo... | github_jupyter |
# Keras tutorial - the Happy House
Welcome to the first assignment of week 2. In this assignment, you will:
1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.
2. See how you c... | github_jupyter |
# Estimating Mandatory Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household travel survey files and write out
the estimation data bundles used in this notebook. To review how to do so, ple... | github_jupyter |
# Drugs From One-Step Amide Formation
## Load modules
```
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import Draw
from rdkit import rdBase
from rdkit.Chem import PandasTools
import pandas as pd
import csv
print('RDKit version: %s' % rdBase.rdkitVe... | github_jupyter |
# Parsing a data2dome feed to find and acquire fulldome images
This notebook shows how to connect to a [data2dome](https://docs.google.com/document/d/1USG1clnxSyGf9lsDe-alb6nZzxn_xFzxbjCnSVPzlWU/edit#) JSON feed.
In this example we query the ESO images feed and search for recent images in fulldome format. We then con... | github_jupyter |
# N-Body Problem and Symplectic Integrators
> Author: Gil Miranda Neto<br>
> Contact: gilsmneto@gmail.com<br>
> Repo: [@mirandagil](https://github.com/mirandagil/university-courses/analise-numerica-edo-2019-1/project)<br>
`last update: 30/05/2019`
---
```
import time
import numpy as np
from math import sqrt
fr... | github_jupyter |
# Text Processing Exercise
In this exerise, you will learn some building blocks for text processing . You will learn how to normalize, tokenize, stemmeize, and lemmatize tweets from Twitter.
### Fetch Data from the online resource
First, we will use the `get_tweets()` function from the `exercise_helper` module to g... | github_jupyter |
## 2300 extensions
Reads in data from historical, SSP 5-8.5 + SSP5-3.4OS & SSP extensions
b.e21.BWSSP585extcmip6.f09_g17.CMIP6-SSP5-8.5ext-WACCM.001
b.e21.BWSSP534osextcmip6.f09_g17.CMIP6-SSP5-3.4OSext-WACCM.001
plots change in global TOTECOSYSC
```
import xarray as xr
import cf_units as cf
import numpy as np
impor... | github_jupyter |
# CME statistics
cme_statistics.py
https://github.com/cmoestl/heliocats
analyses ICMECAT data for paper on CME statistics
Author: C. Moestl, IWF Graz, Austria
twitter @chrisoutofspace, https://github.com/cmoestl
last update April 2020
For installation of a conda environment to run this code and how to download the ... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
from datetime import datetime
from PIL import Image, ImageFilter
from pprint import pprint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from utils.dataloader import gen_dataloader_with_specified_train_val_data
from util... | github_jupyter |
# IDA - Customer Churn Prediction
#### This dataset contains 7043 observations (i.e. customers) and 21 features that can be broken down into three categories:
1) Demographics
2) Account information
3) Payment information.
#### Our target feature is the “Churn” column, which indicates whether a customer has... | github_jupyter |
# Multi-GPU Training Example
Train a convolutional neural network on multiple GPU with TensorFlow.
This example is using TensorFlow layers, see 'convolutional_network_raw' example
for a raw TensorFlow implementation with variables.
## Training with multiple GPU cards
In this example, we are using data parallelism t... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from EMAN2 import *
#### select one GPU when multiple GPUs are present
os.environ["CUDA_VISIBLE_DEVICES"]='0'
#### do not occupy the entire GPU memory at once
## seems necessary to avoid some errors...
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]='true'
#### final... | github_jupyter |
# 05__mpranalyze_compare_crossrep
in this notebook, i run MPRAnalyze in 'compare' mode to get log2 foldchanges and p-values between (a) sequence orthologs and (b) cell types; this time *only* looking across replicates (rep1 in human/mouse for cis effects, rep2 in human/mouse for trans effects)
```
# # install MPRAnal... | github_jupyter |
# Hyperparameter Tuning using SageMaker Tensorflow Container
This tutorial focuses on how to create a convolutional neural network model to train the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) using **SageMaker TensorFlow container**. It leverages hyperparameter tuning to kick off multiple training jobs with d... | github_jupyter |
# Airplane Capital Budgeting Monte Carlo Problem
# The Basic Model
Before we get to the Monte Carlo part or bringing in any of the distributions, we just want to be able to get the base NPV for a plane. To get there, we will need to find the cash flows of the plane. We already have the research and manufacture costs ... | github_jupyter |
# Coverage of MultiPLIER LV
The goal of this notebook is to examine why genes were found to be generic. Specifically, this notebook is trying to answer the question: Are generic genes found in more multiplier latent variables compared to specific genes?
The PLIER model performs a matrix factorization of gene expressi... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import nibabel as nib
import bct
from os import makedirs
from matplotlib.colors import LinearSegmentedColormap
from os.path import join, exists
from nilearn.plotting import plot_glass_brain, plot_roi, find_parcellation_cut_coords
#import bct
import dateti... | github_jupyter |
# Convolutional Neural Networks
## Project: Write an Algorithm for a Dog Identification App
---
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond ... | github_jupyter |
# Nu-Support Vector Classification with RobustScaler
This Code template is for the Classification task using Nu-Support Vector Classifier(NuSVC) based on the Support Vector Machine algorithm with RobustScaler as feature rescaling technique in a pipeline.
### Required Packages
```
!pip install imblearn
import numpy ... | github_jupyter |
# Dirichlet-Multinomial Distribution
This notebook is about the [Dirichlet-Multinomial distribution](https://en.wikipedia.org/wiki/Dirichlet-multinomial_distribution). This distribution has a wide ranging array of applications to modelling categorical variables. It has found its way into machine learning areas such as... | github_jupyter |
# pycaret machine learning
# setup
```
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
# reference: https://www.pycaret.org/
# reference: https://pycaret.org/guide/
# reference: https://github.com/pycaret/
# reference: https://pycaret.org/install/
# pip ... | github_jupyter |
# Analyse de la *Bibliothèque* du pseudo-Apollodore
## Objectif
Ce travail est lié à [ce projet](https://louislesueur.github.io/gods/). Le but est d'utiliser des outils de *Natural Language Processing* issus de la bibliothèque **CLTK** pour extraire les noms propres du texte et identifier les relations entre les pers... | github_jupyter |
**Building a Model to Predict QB Pass Completion**
*by Ben Diner*
In American football, two teams of 11 players play on a rectangular field. The player in the quarterback position is the player who passes the football, and is generally seen as a leader of the team, calling plays and sometimes modifying them according ... | github_jupyter |
# Quantization aware training in Keras example
## Overview
Welcome to an end-to-end example for *quantization aware training*.
**Learning Objectives**
1. Train a tf.keras model for MNIST from scratch.
2. Fine tune the model by applying the quantization aware training API, see the accuracy, and export a quantization... | github_jupyter |
```
import warnings
warnings.filterwarnings(action='once')
import time
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from keras.datasets import cifar10
#loading data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
num_train, img_channels, img_rows, img_cols = x_train.shape
num_test ... | github_jupyter |
# Case Study 5 - SGD & SVM
__Team Members:__ Amber Clark, Andrew Leppla, Jorge Olmos, Paritosh Rai
# Content
* [Business Understanding](#business-understanding)
- [Scope](#scope)
- [Introduction](#introduction)
- [Methods](#methods)
- [Results](#results)
* [Data Evaluation](#data-evaluation)
- [Lo... | github_jupyter |
# Semantic Segmentation Inference using Neo-AI-DLR
In this example notebook, we describe how to use a pre-trained Semantic Segmentation model for inference using the ***Neo-AI DLR*** interface.
- The user can choose the model (see section titled *Choosing a Pre-Compiled Model*)
- The models used in this example were t... | github_jupyter |
```
# Import the necessary libraries
import tensorflow as tf
import tensorflow.keras as keras
# This loads the EfficientNetB1 model from the Keras library
# Input Shape is the shape of the image that is input to the first layer. For example, consider an image with shape (width, height , number of channels)
# 'include_... | 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... | github_jupyter |
## This Jupyter notebook is available at https://github.com/dkp-quantum/Tutorials
## Further Information
#### * Qiskit: https://qiskit.org
#### * Qiskit GitHub: https://github.com/Qiskit
![hardware](j... | github_jupyter |
<a href="https://colab.research.google.com/github/vijishmadhavan/Crop-CLIP/blob/master/Crop_CLIP.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Install requirements
%%capture
!pip install -r https://raw.githubusercontent.com/ultralytics... | github_jupyter |
<a href="https://colab.research.google.com/github/dishankkalra23/Medical-Appointment-No-Shows/blob/main/Medical_Appointment_No_Shows.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import os
os.environ['KAGGLE_USERNAME'] = 'replace_this_with_you... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use("default")
df=pd.read_csv("/content/train.csv")
df.head(5)
df.count()
df.shape
df.isnull().sum()# hence from here we can see that there are missing values in the train data set we must f... | github_jupyter |
# SKNet
+ M = 2
+ kernel_size = 3, dilation_rate = 1
+ kernel_size = 3, dilation_rate = 2
Refernence:
+ [https://liaowc.github.io/blog/SKNet-structure/](https://liaowc.github.io/blog/SKNet-structure/)
+ M：是分支數，也就是有幾種 kernel size。
+ G：是各分支的卷積層做分組卷積的分組數。
+ r： z 的維度為 d... | github_jupyter |
# Pulling data from public APIs (without registration) - GET request
```
# loading the packages
# requests provides us with the capabilities of sending an HTTP request to a server
import requests
```
## Extracting data on currency exchange rates
```
# We will use an API containing currency exchange rates as publishe... | github_jupyter |
# Test reduced variance of gene expression data
**Motivation**: When we plotted a volcano plot of the E-GEOD-51409 array experiment using the [actual data](volcano_original_data_E-GEOD-51409_example_adjp.png) and the [experiment-level simulated data](volcano_simulated_data_E-GEOD-51409_example_adjp.png), we found that... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy as sp
import scipy.io.wavfile as wavutils
from sklearn.linear_model import LinearRegression
from typing import Tuple
from scipy.interpolate import interp1d
def freq_calc(sig: np.ndarray, Ss: int) -> float:
"""Calculates the a... | github_jupyter |
```
!pip install pandas
!pip install numpy
!pip install matplotlib.pyplot
!pip install sklearn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
!pip install matplotlib
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression... | github_jupyter |
```
# 코드로 형식 지정됨
```
#StyleGAN3 Reactive Audio
By Derrick Schultz for the StyleGAN2 Deep Dive class.
This notebook shows one basic example of how to alter your StyleGAN2 vectors with audio. There are lots of different techniques to explore in this, but this is one simple way.
Big thanks to Robert Luxemburg who pr... | github_jupyter |
# Purged KFold as a method
The below was an issue that was reported in mlfinlab, which aroused my curiosity.
Hence to test the relationship of PurgedKFold with different parameters.
[https://github.com/hudson-and-thames/mlfinlab/issues/295#](https://github.com/hudson-and-thames/mlfinlab/issues/295#)
At the same tim... | github_jupyter |
<a href="https://colab.research.google.com/github/mattignal/article-summary-details/blob/main/Article_Summary_Details.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Summarization Exercise with Two Articles
Can we quickly produce useful abstra... | github_jupyter |
```
data_path = 'C:/PillView/NIH/nd320-c4-wearable-data-project-starter/data'
data_path2 = 'D:/Datasets/competition_data/Training_data'
import os
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import glob
import scipy.io
import scipy as sp
from fastai.vision.all import *
import mpld3
%matpl... | github_jupyter |
<a href="https://colab.research.google.com/github/ColmTalbot/gmm_sensitivity_estimation/blob/main/gmm_sensitivity_estimation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Flexible and accurate evaluation of gravitational-wave Malmquist bias with... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import chirp, sweep_poly
from librosa import cqt,stft, note_to_hz, pseudo_cqt
from librosa.feature import melspectrogram
import sys
sys.path.insert(0,'../')
from scipy.io import wavfile
from nnAudio import Spectrogram
import torch
import torch.nn... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_04_4_backprop.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 4: Training for Tabul... | github_jupyter |
# Modeling and Simulation in Python
Copyright 2018 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%c... | github_jupyter |
# Example Tool Usage - Regression Problems
----
# About
This notebook contains simple, toy examples to help you get started with FairMLHealth tool usage. This same content is mirrored in the repository's main [README](../../../README.md)
# Example Setup
```
from fairmlhealth import report, measure, stat_utils
import... | github_jupyter |
## 5. Visualização de dados
A apresentação dos dados estatísticos através de tabelas ou medidas de centralidade e
variabiliadade nem sempre proporciona um entendimento adequado dos dados. Assim,
com a finalidade de melhorar esse processo, muitos recorrem ao uso dos gráficos. Para
isso, é necessário saber o que se pre... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
import skimage.io
# Write your imports here
```
# Visualizing Linear Transformations
Write a code which visualizes a linear transformation. It should show "the old space" and "the new space" imposed on ... | github_jupyter |
# Global Fishing Effort
```
import time
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib import colors,colorbar
import matplotlib
%matplotlib inline
import csv
import math
# from scipy import stats
import bq
client = bq.Client.Get()
def Query(q):
t0 = t... | github_jupyter |
<a id='top'></a>
# Demonstration of the filters available in scipy.signal
This notebook is not intended to replace the SciPy reference guide but to serve only as a one stop shop for the filter functions available in the signal processing module (see http://docs.scipy.org/doc/scipy/reference/signal.html for detailed i... | github_jupyter |
# ***Introduction to Radar Using Python and MATLAB***
## Andy Harrison - Copyright (C) 2019 Artech House
<br/>
# Optimum Binary Detection
***
Binary integration is another form of noncoherent integration, often referred to as $M$ of $N$ detection, and is shown in Figure 6.10. In this form of integration, each of the... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 1: Geometric view of data
**Week ... | github_jupyter |
# Deploying Time-Series Models on Seldon
The following notebook are steps to deploy your first time-series model on Seldon. The first step is to install statsmodels on our local system, along with s2i. s2i will be used to convert the source code to a docker image and stasmodels is a python library to... | github_jupyter |
# Chapter 24
# Augmented Reality with OpenCV II
1. Augmented Reality
## Augmented Reality
What is augmented Reality?
1. Combining reality with virtual
2. Placing object or element like virtual button switch on a wall.
3. Making reality beautiful by superimposing objects on real images.
## Class Activity
1. Use ... | github_jupyter |
Центр непрерывного образования
# Программа «Python для автоматизации и анализа данных»
Неделя 2 - 1
*Татьяна Рогович, НИУ ВШЭ*
## Последовательности: списки и кортежи
# Списки (list)
Давайте представим, что при написании программы нам нужно работать, например, с большой базой данных студентов университета.
Есл... | github_jupyter |
```
import os
os.chdir('../../')
```
## GPU 设置
```
GPUID='0'##调用GPU序号
os.environ["CUDA_VISIBLE_DEVICES"] = GPUID
import numpy as np
import tensorflow as tf
from glob import glob
from PIL import Image
import cv2
Input =tf.keras.layers.Input
Lambda = tf.keras.layers.Lambda
load_model = tf.keras.models.load_model
Model ... | github_jupyter |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | github_jupyter |
# Word prediction based on Pentagram
This program reads the corpus line by line so it is slower than the program which reads the corpus
in one go.This reads the corpus one line at a time loads it into the memory
## Import corpus
```
#%%timeit
from nltk.util import ngrams
from collections import defaultdict
import nlt... | github_jupyter |
## Paper visualizations
```
!pip install --user neural_renderer_pytorch
import os
import imageio
import trimesh
import torch
import numpy as np
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
%matplotlib inline
import neural_renderer as nr
from scipy.spatial import cKDTree as KDTr... | github_jupyter |
# Fleet Predictive Maintenance: Part 3
## Data Preparation: Featurization and Exploratory Data Visualization
*Using SageMaker Studio to Predict Fault Classification*
1. [Architecure](0_usecase_and_architecture_predmaint.ipynb#0_Architecture)
1. [Data Prep: Processing Job from Data Wrangler Output](./1_dataprep_dw_job_... | github_jupyter |
```
#!/usr/bin/python3
# coding=utf-8
import scipy.optimize as optimize
from numpy import genfromtxt
def manual_gd(f_, x_old=0, x_new=5, learningRate=0.1, precision=0.00001):
"""
A simple gradient descent usage for function optimization
"""
iteration = 0
while abs(x_new - x_old) > precision:
... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import math, os, re, warnings, random, glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.layers as L
import tensorflow.keras.backend as K
from tensorflo... | github_jupyter |
### GeostatsPy: Confidence Intervals and Hypothesis Testing for Subsurface Data Analytics in Python
#### Michael Pyrcz, Associate Professor, University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleSchola... | github_jupyter |
## Please input your directory for the top level folder
folder name : SUBMISSION MODEL
```
dir_ = 'INPUT-PROJECT-DIRECTORY/submission_model/' # input only here
```
#### setting other directory
```
raw_data_dir = dir_+'2. data/'
processed_data_dir = dir_+'2. data/processed/'
log_dir = dir_+'4. logs/'
model_dir = dir_... | github_jupyter |
## Week 8: Reinforcement Learning for seq2seq
This time we'll solve a problem of transribing hebrew words in english, also known as g2p (grapheme2phoneme)
* word (sequence of letters in source language) -> translation (sequence of letters in target language)
Unlike what most deep learning practicioners do, we won't... | github_jupyter |
# Using crack submodels in PyBaMM
In this notebook we show how to use the crack submodel with battery DFN or SPM models. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html).
```
%pip install pybamm ... | github_jupyter |
```
from pulp import *
problem = LpProblem("Marketing Spend", LpMaximize)
# Setup Sets
markets = ["Facebook", "Instagram", "Twitter"]
saturation_level = [1, 2]
buckets = [1, 2]
# Setup Data
clicks_per_dollar = {("Facebook", 1):0.10, ("Facebook", 2):0.20, \
("Instagram", 1):0.12, ("Instagram", 2... | github_jupyter |
# Housing Rental Analysis for San Francisco
In this challenge, your job is to use your data visualization superpowers, including aggregation, interactive visualizations, and geospatial analysis, to find properties in the San Francisco market that are viable investment opportunities.
Instructions:
Use the `san_franci... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# ResNet50 Image Classification using ONNX ... | github_jupyter |
# Code Snippets for Intro to TensorFlow Talk @ PyData Ann Arbor Aug 2017
GitHub Repo: https://github.com/rasbt/pydata-annarbor2017-dl-tutorial
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow,numpy
```
## Vectorization
```
import numpy as np
np.random.seed(123)
num_train_examles = 150
... | github_jupyter |
## K-Nearest Neighbor Algoritm ###
KNN is a classification algorithm. It is basic to understand.
K is the number of neighbors you want to look at. Algorithm looks at the classes of nearest k points and classify the point if a class have more points that are nearest to point.
### Import Libraries ###
I will only use ... | github_jupyter |
### Questions Classification Custom dataset.
In this notebook we are going to learn how to load the questions dataset using torchtext and prepare it for sentiment classification in pytorch. We are going to use [this series](https://github.com/CrispenGari/pytorch-python/tree/main/09_TorchText/02_Sentiment_Analyisis_Ser... | github_jupyter |
# Section 0 - Jupyter Notebook and Markdown Syntax
## Author: Gustavo Amarante
The jupyter notebook is an interactive programming environment that is made up of **code cells** and **text cells**. Text cells allow you to use not only plain text but also some commands to format it. These commands are called the **Markdo... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Softmax
# Build the Sequential feedforward neural network model
model = Sequential([Flatten(input_shape = (28,28))])
model.add(Dense(16, activation = 'relu'))
model.add(De... | github_jupyter |
```
%matplotlib inline
import numpy as np
import networkx as nx
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import cm
from bokeh.io import output_file, show
from bokeh.plotting import figure, from_networkx
import datashader as ds
import datashader.transfer_functions as tf
from bokeh.models... | 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 |
# 3. Content based model
This notebook is about creating a content based recommendation model with the tmdb dataset. In contrast to collaborative filetering models where the user ratings are taken into account, contet based models are, as the name implies, based only on the conent of items. To define the conent, Natur... | github_jupyter |
# How to make a histogram from scratch
---
Step by step implementation from scratch
the figure was implemented using only the matplotlib basic functions.
* nice instruction on how to create a histogram: https://www.youtube.com/watch?v=gSEYtAjuZ-Y
```
import numpy as np
import math as math
import matplotlib.pyp... | github_jupyter |
<a href="https://colab.research.google.com/github/ashikshafi08/Learning_Tensorflow/blob/main/Other%20Courses/Getting_Started_with_TensorFlow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
This notebook contains all the materials and notes for the G... | github_jupyter |
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