text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
### Our Mission
In this lesson you gained some insight into a number of techniques used to understand how well our model is performing. This notebook is aimed at giving you some practice with the metrics specifically related to classification problems. With that in mind, we will again be looking at the spam dataset ... | github_jupyter |
# Mini Web App Finding Similar Members with the Meetup API
This notebook will present a little application that uses the Meetup.com API to get member info from the Houston Data Science Meetup group.
## Get your API Key
To make this tutorial work, you will need to get an [API key from Meetup][1]. Once you get your key... | github_jupyter |
# Week 6 - An introduction to machine learning (Part II) - Exercise and Solution
We'll apply some of the material from the previous lectures to recreating the analysis from a [nature machine intelligence](https://www.nature.com/natmachintell/) paper, ["An interpretable mortality prediction model for COVID-19 patients... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# 1D Degenerate Alfven Wave `GiRaFFEfood` Initial Data for ... | github_jupyter |
Deep Learning
=============
Assignment 2
------------
Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).
The goal of this assignment is to progressively train deep... | github_jupyter |
# Lecture 7: Load/save and structure data
[Download on GitHub](https://github.com/NumEconCopenhagen/lectures-2020)
[<img src="https://mybinder.org/badge_logo.svg">](https://mybinder.org/v2/gh/NumEconCopenhagen/lectures-2020/master?urlpath=lab/tree/07/Load_save_and_structure_data.ipynb)
1. [Pandas dataframes](#Pandas... | github_jupyter |
```
import pandas as pd
import numpy as np
import hddm
import sys
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
%matplotlib inline
pd.options.display.max_columns = None
#generate data
data, params = hddm.generate.gen_rand_data(params={'a': 2, 't': .4, 'v': .5},
... | github_jupyter |
# Bayesian Linear Regression part 4: Plots
Now I ha... | github_jupyter |
```
import pickle as pkl
import pandas as pd
import imodels
import itertools
import os
from imodels.util.evaluate.compare_models import run_comparison
from sklearn.metrics import accuracy_score, f1_score
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
import matplotlib.pyplot as plt
from... | github_jupyter |
# VERIFICATION TESTING
# HER2 One Scanner - Aperio FDA
- 5-Fold (80/20) split, No Holdout Set
- Truth = Categorical from Mean of 7 continuous scores
- Epoch at automatic Stop when loss<.001 change
- LeNet model, 10 layers, Dropout (0.7)
```
import numpy as np
import pandas as pd
import random
from keras.callbacks ... | github_jupyter |
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidata_150x150.png" alt="Unidata Logo" style="height: 98px;">
</div>
<h1>Introduction to MetPy</h1>
<h3>Unidata Python Workshop</h3>
<div style="clear... | github_jupyter |
<a href="https://colab.research.google.com/github/kirubarajan/roft/blob/master/annotation/analysis/research.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Dataset Loading and Cleaning
```
!pip install fsspec gcsfs
!pip install --upgrade matplotl... | github_jupyter |
We build a multi-layer perceptron with its hidden layers batch normalized, and contrast it with the version without
batch normalization.
We train and evaluate both versions of the multi-layer perceptron on MNIST dataset.
```
import os
import gzip
import numpy as np
import matplotlib.pyplot as plt
import autodiff as ... | github_jupyter |
# References
I've made use of some great kernels already - check them out and give them an upvote if any of this is useful!
### Preprocessing
- https://www.kaggle.com/christofhenkel/how-to-preprocessing-when-using-embeddings
- https://www.kaggle.com/theoviel/improve-your-score-with-text-preprocessing-v2
### Model... | github_jupyter |
(nm_ill_conditioning_roundoff_errors)=
# Ill-conditioning and roundoff errors
## Ill-conditioned matrices
The conditioning (or lack of, i.e. the ill-conditioning) of matrices we are trying to invert is incredibly important for the success of any algorithm.
As long as the matrix is non-singular, i.e. \\(\det(A)\ne 0\... | github_jupyter |
```
# nlp with recurrent neural networks
# autocheck word complete grammer check translation chatbot
# sentiment analysis / character generation
# bag of words implementation
def bag_of_words(text):
# find the words
words = text.lower().split(' ')
bag = {}
vocab = {}
word_encoding = 1
for word in words:... | github_jupyter |
```
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger("exchangelib").setLevel(logging.WARNING)
```
# Connecting melusine to an Outlook Exchange mailbox
The main use-case for Melusine is **email routing**. Melusine mostly focuses on the Machine Learning aspects of ... | github_jupyter |
# BepiColombo First Venus Swingby Hands-On Lesson
Virtual SPICE Training for BepiColombo, July 21-22, 2020
## Overview
In this lesson you will develop a series of simple programs that
demonstrate the usage of SpiceyPy to compute a variety of different
geometric quantities applicable to experiment... | github_jupyter |
<table> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="25%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by <a href="http://abu.lu.... | github_jupyter |
# **Tugas Besar 2**
## Kelompok 8:
1. 16520289 Gagas Praharsa Bahar
2. 16520299 Malik Akbar Hashemi Rafsanjani
3. 16520309 Alifia Rahmah
4. 16520319 Ng Kyle
## Sumber data:
Trending YouTube Video Statistics (US Videos) - Mitchell J ([Kaggle](https://www.kaggle.com/datasnaek/youtube-new))
### Data... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
```
%%time
import malaya
isu_kerajaan = [
'Kenyataan kontroversi Setiausaha Agung Barisan Nasional (BN), Datuk Seri Mohamed Nazri Aziz berhubung sekolah vernakular merupakan pandangan peribadi beliau',
'Timbalan Presiden UMNO, Datuk Seri Mohamad Hasan berkata, kenyataan tersebut tidak mewakili pendirian serta p... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Gena/hillshade.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="https:/... | github_jupyter |
# Xopt class, TNK test function
This is the class method for running Xopt.
TNK function
$n=2$ variables:
$x_i \in [0, \pi], i=1,2$
Objectives:
- $f_i(x) = x_i$
Constraints:
- $g_1(x) = -x_1^2 -x_2^2 + 1 + 0.1 \cos\left(16 \arctan \frac{x_1}{x_2}\right) \le 0$
- $g_2(x) = (x_1 - 1/2)^2 + (x_2-1/2)^2 \le 0.5$
```
fr... | github_jupyter |
# 範例 : (Kaggle)房價預測
# [教學目標]
- 以下用房價預測資料, 展示特徵篩選的作法
# [範例重點]
- 觀察相關係數過濾法的寫作方式(In[2], Out[2], In[4], Out[4]), 以及對線性迴歸與梯度提升機有什麼影響 (In[5]~In[8], Out[5]~Out[8])
- 觀察L1 嵌入法的寫作方式(In[9]~In[11], Out[9]~Out[11]), 以及對線性迴歸與梯度提升機有什麼影響 (In[12], Out[12], In[13], Out[13])
```
# 做完特徵工程前的所有準備
import pandas as pd
import numpy as np
... | github_jupyter |
# Galaxy Catalog Plots
This notebook reads the [LSST DM galaxy catalog](http://weaklensingdeblending.readthedocs.org/en/latest/catalog.html), calculates some size and shape parameters, and makes plots to summarize the inputs to the `simulate` program. Plots to summarize the `simulate` outputs are generated in a [sepa... | github_jupyter |
```
import pandas as pd
import numpy as np
import sentencepiece as spm
import nltk
import ast
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
import time
ncbi_com_0 = pd.read_csv("data/ncbi_comm_use_000000000000.csv")
ncbi_com_1 = pd.read_csv("data/ncbi_comm_use_000000000001.csv")
ncbi_n... | github_jupyter |
$$ \text{LaTeX command declarations here.}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
$$
# EECS 445: Machine Learning
## Hands On 05: Linear Regression II
* Instructor: **Zhao Fu, Valli, Jacob Abernethy and Jia Deng**
* Date: September 26, 2016
### Review: Maximum Likelihood
Suppose we have a... | github_jupyter |
```
ALPHABET = [' ', 'e', 't', 'a', 'i', 'o', 's', 'n', 'r', 'h', 'l', 'd', 'c', 'm', 'u', 'f', 'g', 'y', 'b', 'w', 'p',\
'.', 'v', ',', 'k', "'", '/', '>', '<', '-', '"', 'j', 'x', ')', '(', '!', 'z', 'q', '0', '1', '?', ':',\
'9', '2', '*', ';', '3', '5', '8', '4', '7', '&', '6', 'é', '\x96', ... | github_jupyter |
# 2A.ml - Déterminer la vitesse moyenne des vélib
Ce notebook explicite une solution pour calculer la vitesse moyenne des velib sachant qu'on ne connaît que l'état des stations à intervalle réguliers.
```
%matplotlib inline
```
Même si je propose quelques jeux de données, il est possible de créer le sien en s'inspir... | github_jupyter |
# LMS filter and ADALINE algorithm
In this first project you will implement a Least Mean Square (LMS) error filter by using the Adaptive Linear Neuron (ADALINE) algorithm. This algorithm is a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least m... | github_jupyter |
# Managed Spot Training for XGBoost
This notebook shows usage of SageMaker Managed Spot infrastructure for XGBoost training. Below we show how Spot instances can be used for the 'algorithm mode' and 'script mode' training methods with the XGBoost container.
[Managed Spot Training](https://docs.aws.amazon.com/sagemak... | github_jupyter |
# Child Nutrition Calculator
### Input the required information about child
```
# personal info -> input name, age, gender, height, weight
def personalInfoChild():
Name = input("Enter your Name: ")
Age = int(input("Enter your Age: "))
Gender = input("Enter your Gender: ")
height = float(input... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import R... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
# DiscreteDP Example: Mine Management
**Daisuke Oyama**
*Faculty of Economics, University of Tokyo*
From Miranda and Fackler, <i>Applied Computational Economics and Finance</i>, 2002,
Section 7.6.1
```
%matplotlib inline
import itertools
import numpy as np
from scipy import sparse
import matplotlib.pyplot as plt
fr... | github_jupyter |
<a href="https://colab.research.google.com/github/Kabongosalomon/Cat-vs-Dog-Classifier/blob/master/Helper_Colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Load My data
```
! git clone https://github.com/Kabongosalomon/Cat-vs-Dog-Classifier.... | github_jupyter |
# Logistic Regression with L2 regularization
```
import pandas as pd
products = pd.read_csv('amazon_baby_subset.csv')
products.head()
import json
with open('important_words.json', 'r') as f:
important_words = json.load(f)
important_words = [str(s) for s in important_words]
products = products.fillna({'review':''})... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
%matplotlib inline
import datetime
import cPickle as pickle
import csv
import numpy as np
import random
import sys
maxInt = sys.maxsize
decrement = True
while decrement:
# decrease the maxInt value by factor 10
# as long as the Overfl... | github_jupyter |
## 1. The most Nobel of Prizes
<p><img style="float: right;margin:5px 20px 5px 1px; max-width:250px" src="https://s3.amazonaws.com/assets.datacamp.com/production/project_309/img/Nobel_Prize.png"></p>
<p>The Nobel Prize is perhaps the worlds most well known scientific award. Except for the honor, prestige and substantia... | github_jupyter |
<a href="https://colab.research.google.com/github/agemagician/CodeTrans/blob/main/prediction/single%20task/api%20generation/small_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Install the library and download the pretrained models
```
pr... | github_jupyter |
```
import sys
import numpy as np
from collections import Counter
sys.path.append('../scales_project/')
from utils import simulate_EPR
from importlib import reload
reload(simulate_EPR)
simul
import matplotlib as mpl
def setup_mpl():
mpl.rc('font', size=20)
mpl.rcParams['legend.fontsize'] = 'small'
mpl.rcPar... | github_jupyter |
```
#Adapted from the method described in
#Bhatt, Samir, Edward C. Holmes, and Oliver G. Pybus. 2011. “The Genomic Rate of Molecular Adaptation of the Human Influenza A Virus.” Molecular Biology and Evolution 28 (9): 2443–51.
#and
#Bhatt, Samir, Aris Katzourakis, and Oliver G. Pybus. 2010. “Detecting Natural Selection... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
%matplotlib inline
from scipy.st... | github_jupyter |
# Coverage of MultiPLIER LV using _P. aeruginosa_ data
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 fac... | github_jupyter |
**Exploratory Data Analysis**
```
import pandas as pd
import seaborn as sns
import numpy as np
from rdkit import Chem
from rdkit.Chem.Descriptors import MolLogP
from tqdm.auto import tqdm
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
from umap import UMAP
```
Make Pandas use Seabo... | github_jupyter |
# Multi-Class Single-Label classification
The natural extension of binary classification is a multi-class classification task.
We first approach multi-class single-label classification, which makes the assumption that each example is assigned to one and only one label.
We use the *Iris flower* data set, which consist... | github_jupyter |
```
import Bio.PDB as PDB
import numpy as np
import freesasa
import glob
from Bio.PDB.DSSP import DSSP
```
# Calculate parameters
```
surfaces = []
rsas = []
surface_seq = []
for file in glob.glob("data/training/crystal_structs/*.pdb"):
# parse the pdb file
p = PDB.PDBParser(QUIET=True)
s = p.get... | github_jupyter |
```
#@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 agreed to in writing, software
# distributed u... | github_jupyter |
# Tensor Creation
```
from __future__ import print_function
import torch
import numpy as np
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/pytorch_exercises"
torch.__version__
np.__version__
```
NOTE on notati... | github_jupyter |
```
%reload_ext nb_black
import json
import pandas as pd
with open("../secrets.json", "r") as f:
secrets = json.load(f)
import spotipy
import spotipy.util as util
from spotipy.oauth2 import SpotifyClientCredentials
import spotipy.oauth2 as oauth2
CLIENT_ID = secrets["spotify_client_id"]
CLIENT_SECRET = secrets["... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# 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 agreed to in writi... | github_jupyter |
```
# import required dependencies
import sys
sys.path.insert(0, '../../../BERT-FAQ/')
from shared.utils import load_from_json
from shared.utils import dump_to_json
from shared.utils import make_dirs
from reranker import ReRanker
```
**1. Generating reranked results from Answer (BERT-Q-a)"**
```
# define output path... | github_jupyter |
```
"""Copyright 2020-2021 Google LLC
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 agreed to in writing, softwa... | github_jupyter |
**Chapter 13 – Loading and Preprocessing Data with TensorFlow**
_This notebook contains all the sample code and solutions to the exercises in chapter 13._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.... | github_jupyter |
# Census Notebook
**Authorship**<br />
Original Author: Taurean Dyer<br />
Last Edit: Taurean Dyer, 9/26/2019<br />
**Test System Specs**<br />
Test System Hardware: GV100<br />
Test System Software: Ubuntu 18.04<br />
RAPIDS Version: 0.10.0a - Docker Install<br />
Driver: 410.79<br />
CUDA: 10.0<br />
**Known Worki... | github_jupyter |
# 采用机器翻译实现Seq2Seq
```
import sys
sys.path.append('../')
import collections
import d2l
import zipfile
from d2l.data.base import Vocab
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import data
from torch import optim
```
## Seq2Seq的结构
# Sequence to Sequence模型
### 模型：
... | github_jupyter |
# Estimate car price - Introduction to Python wrapper for SAP HANA
This notebook is part of a Machine Learning project that is described and available to download on
<BR><a href="https://blogs.sap.com/2019/11/05/hands-on-tutorial-machine-learning-push-down-to-sap-hana-with-python/">https://blogs.sap.com/2019/11/05/han... | github_jupyter |
```
#from scipy.io import loadmat
#import h5py
import xarray as xr
import numpy as np
#PLOTTING
import cartopy
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.colorbar import Colorbar
import matplotlib.ticker as mticker... | github_jupyter |
# Black Scholes Model
In this notebook we illustrate the basic properties of the Black Scholes model.
The notebook is structured as follows:
1. Black-Scholes model code
2. Analysis of value function
3. Analysis of Greeks, i.e. sensitivities to model parameters
## Black-Scholes Model Code
We use a c... | github_jupyter |
```
from argparse import Namespace
import contextlib
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass, field
from omegaconf import MISSING, II, open_dict
from typing import Any, Optional
from fairseq import checkpoint_utils,... | github_jupyter |
```
%run -i ../python/common.py
UC_SKIPTERMS=True
%run -i ../python/ln_preamble.py
```
# SLS Lecture 8 : Writing some simple assembly programs
Spend some time writing some very simple assembly programs and learn to use the debugger so that we have enough skills to explore how things work. We will be repeat variou... | github_jupyter |
# DSCI 572 Lab 4
```
import numpy as np
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from scipy.signal import convolve2d
import matplotlib.pyplot as plt
%matplotlib inline
```
To install scikit-image, use
```
conda install -c conda-forge scikit-image
```
or
```
pip install ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica Neue"],
"font.size": 28,
# "contour.negative_linestyle": 'solid',
})
# Define function
x_min = -24
x_max = 24
y_min = -13.5
y_max = 13.5
... | github_jupyter |
## 3DCORE with THUX
```
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d import axes3d
from matplotlib.colors import LightSource
from matplotlib import cm
import heliopy
import astropy
import datetime
from datetime import timedelta
import ... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import kenlm
from tqdm import tqdm
import fastText
import pandas as pd
from bleu import *
import torch, os
#bert classifier
from tqdm import trange
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.mod... | github_jupyter |
## Collaborative filtering using Python
Alright, so let's do it! We have some Python code that will use Pandas, and all the various other tools at our disposal, to create movie recommendations with a surprisingly little amount of code.
The first thing we're going to do is show you item-based collaborative filt... | github_jupyter |
<a id='top'></a>
# Log completion by ML regression
- Typical and useful Pandas
- Data exploration using Matplotlib
- Basic steps for data cleaning
- **Exercise: Find problem in specific well log data.**
- Feature engineering
- Setup scikit-learn workflow
- Making X and y
- Choosing a model
- Cl... | github_jupyter |
# FairWorkflows execution demo
## Define the steps of your workflow
Each step should be its own function. Mark the function as such with the @fairstep decorator.
```
%cd ..
from fairworkflows import is_fairworkflow, is_fairstep, FairStep, FairWorkflow
@is_fairstep(label='Addition')
def add(a:float, b:float) -> float... | github_jupyter |
#### Fancy indexing and index tricks
NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans.
##### Indexing with Arrays of Indices¶
```
import numpy as np
a = np.arange(12... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.ticker import ScalarFormatter
import math
```
This notebook assumes you have completed the notebook [Introduction of sine waves](TDS_Introduction-sine_waves.ipynb). This notebook follows... | github_jupyter |
# ResNet-50 Inference with FINN on Alveo
This notebook demonstrates the functionality of a FINN-based, full dataflow ResNet-50 implemented in Alveo U250. The characteristics of the network are the following:
- residual blocks at 1-bit weights, 2/4-bit activations
- first convolution and last (fully connected) layer ... | github_jupyter |
```
import twitter
import os
import yaml
import re
import time
import tweepy
import pandas as pd
from textblob import TextBlob
from collections import Counter
import pickle
credentials = yaml.load(open(os.path.expanduser('~/.ssh/api_credentials.yml')))
```
# Try Tweepy
```
#!/usr/bin/env python
# encoding: utf-8
imp... | github_jupyter |
## 绘制数组
```
import matplotlib.pyplot as plt
import numpy as np
a = np.zeros([2, 3])
print(a)
a[0, 0] = 1
a[0, 1] = 2
a[1, 1] = 4
a[1, 2] = 1
plt.imshow(a, interpolation="nearest") # 创建绘图
```
## 神经网络框架代码
- 构建一个神经网络类
- 包含3个函数
1. 初始化函数, 设定输入层节点,隐藏节点和输出层节点的数量
2. 训练,学习给定训练集样本后,优化权重
3. 查询,给定输出,从输出节点给出答案
```
import num... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import seaborn as sns
from pylab import rcParams
import matplotlib.pyplot as plt
from matplotlib import rc
%matplotlib inline
%config InlineBackend.figure_format='retina'
RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)
tf.ran... | github_jupyter |
# Chapter 1: Pandas Foundations
## Recipes
* [Dissecting the anatomy of a DataFrame](#Dissecting-the-anatomy-of-a-DataFrame)
* [Accessing the main DataFrame components](#Accessing-the-main-DataFrame-components)
* [Understanding data types](#Understanding-data-types)
* [Selecting a single column of data as a Series](#S... | github_jupyter |
```
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tensorflow.keras.backend as kb
from backwardcompatibilityml import scores
from backwardcompatibilityml.tensorflow import helpers as tf_helpers
from backwardcompatibilityml.tensorflow.loss.strict_imitation import BCStrictImitationKLDivLoss
i... | github_jupyter |
<a href="https://colab.research.google.com/github/ashikshafi08/Learning_Tensorflow/blob/main/Experiments/Generator_to_Dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
For this experiment, we'll use a dataset from AI Crowd competition (live n... | github_jupyter |
# Machine Translation with Transformer
Tutorial from:
https://www.tensorflow.org/tutorials/text/transformer
```
import tensorflow_datasets as tfds
import tensorflow as tf
import time
import numpy as np
import matplotlib.pyplot as plt
examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True,
... | github_jupyter |
```
import glob
import xml.etree.ElementTree as ET
import re
folder="/pi/proto/framework/applications/datamodel/entitydef"
inputfilepattern=folder+"/*.xml"
files=glob.glob(inputfilepattern)
entities=[]
for filename in files:
print("process {} ..".format(filename))
tree = ET.parse(filename)
root = tree.getr... | github_jupyter |
# Searching
Try running it in a live notebook for animation!
* peakSearch
* bracketSearch
* binarySearch
```
# Reload modules every time code is called. Set autoreload 0 to disable
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
from lightlab.util.search impor... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import datetime
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.transforms import Affine2D
import pickle
import copy as cp
import scipy.optimize
import casadi as cas
PROJECT_PATH = '/home/nbuckman/Dropbox (M... | github_jupyter |
# Use a custom parser
While many of the parsers included within this libary may be useful, we do not have parsers for **every** dataset out there. If you are interested in adding your own parser (and hopefully contributing that parser to the main repo 😊 ), check out this walkthrough of how to build one!
## What is a... | github_jupyter |
# Source reconstruction with lens mass fitting
Runs MCMC over lens model parameters, using SLIT to reconstruct the source at each iteration.
```
import os
import sys
import copy
import time
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as pf
import pysap
import corner
import pickle as pkl
... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import cvxpy as cp
import pandas as pd
from tqdm import tqdm
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica Neue"],
"font.size": 20,
})
np.random.seed(0)
# Load data from MNIST dataset (please... | github_jupyter |
# Discrete Fourier Transform in Python
This notebook is a quick refresher on how to perform FFT in python/scipy.
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import fft
```
We define:
- $N$: number of samples
- $f_s$: sampling frequency/rate in samples/second
```
N = 1000
f_s = 100
```... | github_jupyter |
# Plot Kmeans clusters stored in a GeoTiff
This is a notebook plots the GeoTiffs created out of [kmeans](../stable/kmeans.ipynb). Such GeoTiffs contains the Kmeans cluster IDs.
## Dependencies
```
import sys
sys.path.append("/usr/lib/spark/python")
sys.path.append("/usr/lib/spark/python/lib/py4j-0.10.4-src.zip")
sys... | github_jupyter |
# Dynamics 365 Business Central Trouble Shooting Guide (TSG) - Login issues (SaaS)
This notebook contains Kusto queries that can help getting to the root cause of a login issue for an environment in the online version of Business Central (SaaS). Each section in the notebook contains links to the TSG part of the auth... | github_jupyter |
# Introduction to Glue-Viz
**version 0.1**
***
By AA Miller (Northwestern CIERA/Adler Planetarium)
03 May 2018
## Introduction
[All of my slides from Tuesday morning]
... that is all
## Glue
As a point of review, on Tuesday we learned about ParaView. I'd summarize the major strength of ParaView as providing an i... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
# Transformer
> Training a Timesformer model for UCR video classif.
Tbaks to Phil wang (@lucidrains) we have a bunch of attention based models to trian:
- `Is Space-Time Attention All You Need for Video Understanding?`: This paper looks pretty cool, as it is the first full... | github_jupyter |
# Dive
wrapped in a python class
## Implementation
- Dive profile is usually shown as a series of depth and time, in `MM:SS` format, points.
- Need to convert the latter into decimal minutes,
- and convert it to time and current depth.
- constructor: initialize the model to ZH-L16C w/ 5-minute compartment,
... | github_jupyter |
```
import os, sys
import matplotlib.pyplot as plt
import numpy as np
from sklearn import decomposition, manifold
% matplotlib notebook
def compute_distance(x,y):
x = x / np.linalg.norm(x)
y = y / np.linalg.norm(y)
return np.linalg.norm(x-y)
def compute_xcorr(x,y):
return x.dot(y.T).sum()
def print_per... | github_jupyter |
# Naas - NLP Examples
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Naas/Naas_NLP_Examples.ipynb" target="_parent"><img src="https://img.shields.io/badge/-Open%20in%20Naas-success?labelColor=000000&logo=data:image/svg+xml;base64,PD... | github_jupyter |
```
import os
import pandas as pd
import sys
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, StratifiedKFold
import tensorflow as tf
sys.path.append("../../DNN-RE-new/src")
raw_data = pd.read_csv('raw_data/MBdata_33CLINwMiss_1KfGE_1KfCNA.csv')
de... | github_jupyter |
Arun Das
Research Fellow
Secure AI and Autonomy Laboratory
University of Texas at San Antonio
# Rotational Invariance in Convolutional Neural Networks
Over the course of history, convolution operation has helped accelerate science and signal processing in a variety of ways. With the advent of deep lea... | github_jupyter |
# COVID-19 Exploratory Data Analysis
> (Almost) Everything You Want To Know About COVID-19.
- author: Devakumar kp
- comments: true
- categories: [EDA]
- permalink: /corona-eda/
- toc: true
- image: images/copied_from_nb/covid-eda-2-1.png
These visualizations were made by [Devakumar kp](https://twitter.com/imdevskp).... | github_jupyter |
```
import numpy as np
import pandas as pd
from tqdm import tqdm
from rdkit import Chem
import seaborn as sns
from sklearn.cluster import AgglomerativeClustering, DBSCAN, SpectralClustering
from scipy.stats import ks_2samp, chisquare, power_divergence
import tmap, os
from faerun import Faerun
from mhfp.encoder impor... | github_jupyter |
```
from __future__ import print_function # to use Python 3 features in Python 2
%matplotlib inline
import matplotlib as mpl
from matplotlib import pyplot as plt
import numpy as np
from astropy import constants as const
```
# Line Plot
```
def gaussian(x, sigma=2):
y = (2*np.pi*sigma**2)**-0.5 * np.exp(- x**2 ... | github_jupyter |
# Structure learning with cause2e
This notebook shows how ```cause2e``` can be used for learning causal graphs. Structure learning (also called causal discovery) can be performed by the ```discovery.StructureLearner``` after reading data and specifying domain knowledge. If we only want to perform a quick exploratory s... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.