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<img src="../../img/ods_stickers.jpg">
## Открытый курс по машинному обучению. Сессия № 2
Автор материала: программист-исследователь Mail.ru Group, старший преподаватель Факультета Компьютерных Наук ВШЭ Юрий Кашницкий. Материал распространяется на условиях лицензии [Creative Commons CC BY-NC-SA 4.0](https://c... | github_jupyter |
# High-level Chainer Example
```
# Parameters
EPOCHS = 10
N_CLASSES=10
BATCHSIZE = 64
LR = 0.01
MOMENTUM = 0.9
GPU = True
LOGGER_URL='msdlvm.southcentralus.cloudapp.azure.com'
LOGGER_USRENAME='admin'
LOGGER_PASSWORD='password'
LOGGER_DB='gpudata'
LOGGER_SERIES='gpu'
import os
from os import path
import sys
import num... | github_jupyter |
# KEN 3140 Semantic Web: Lab 5 🧪
### Writing and executing "complex" SPARQL queries on RDF graphs
**Reference specifications: https://www.w3.org/TR/sparql11-query/**
We will use the **DBpedia SPARQL endpoint**:
>**https://dbpedia.org/sparql**
And **SPARQL query editor YASGUI**:
> **https://yasgui.triply.cc**
# ... | github_jupyter |
```
def foobar(a: int, b: str, c: float = 3.2) -> tuple: pass
import collections
import functools
import inspect
from typing import List
Vector = List[float]
def formatannotation(annotation, base_module=None):
if getattr(annotation, '__module__', None) == 'typing':
return repr(annotation).replace('typing.... | github_jupyter |
# Working with data
Overview of today's learning goals:
1. Introduce pandas
2. Load data files
3. Clean and process data
4. Select, filter, and slice data from a dataset
5. Descriptive stats: central tendency and dispersion
6. Merging and concatenating datasets
7. Grouping and summarizing data
```
# so... | github_jupyter |
##### Copyright 2019 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 writin... | github_jupyter |
# Programming Exercise 5:
# Regularized Linear Regression and Bias vs Variance
## Introduction
In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video le... | github_jupyter |
```
import os
os.chdir('../app')
import matplotlib
print(matplotlib.__version__)
import frontend.stock_analytics as salib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
from datetime import datetime,timedelta
... | github_jupyter |
# Conservative SDOF - Multiple Scales
- Introduces multiple time scales (Homogenation)
- Treate damped systems easier then L-P
- Built-in stability
Introduce new independent time variables
$$
\begin{gather*}
T_n = \epsilon^n t
\end{gather*}
$$
and
$$
\begin{align*}
\frac{d}{dt} &= \frac{\partial}{\partial... | github_jupyter |
# Complex Graphs Metadata Example
## Prerequisites
* A kubernetes cluster with kubectl configured
* curl
* pygmentize
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html) to setup Seldon Core with an ingress.
```... | github_jupyter |
# INFO
This is my solution for the fourth homework problem.
# **SOLUTION**
# Description
I will use network with:
- input layer with **2 neurons** (two input variables)
- **one** hidden layer with **2 neurons** (I need to split the plane in a nonlinear way, creating a U-shaped plane containing the diagonal points)
... | github_jupyter |
# Import KBase and cFBA
```
# import kbase
import os
local_cobrakbase_path = 'C:\\Users\\Andrew Freiburger\\Dropbox\\My PC (DESKTOP-M302P50)\\Documents\\UVic Civil Engineering\\Internships\\Agronne\\cobrakbase'
os.environ["HOME"] = local_cobrakbase_path
import cobrakbase
token = 'JOSNYJGASTV5BGELWQTUSATE4TNHZ66U'
kbas... | github_jupyter |
<a href="https://colab.research.google.com/github/enakai00/rl_book_solutions/blob/master/Chapter06/SARSA_vs_Q_Learning_vs_MC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
from numpy import random
from pandas import DataFrame... | github_jupyter |
# Regression with Amazon SageMaker XGBoost (Parquet input)
This notebook exhibits the use of a Parquet dataset for use with the SageMaker XGBoost algorithm. The example here is almost the same as [Regression with Amazon SageMaker XGBoost algorithm](xgboost_abalone.ipynb).
This notebook tackles the exact same problem ... | github_jupyter |
# Model Centric Federated Learning - MNIST Example: Create Plan
This notebook is an example of creating a simple model and a training plan
for solving MNIST classification in model-centric (aka cross-device) federated learning fashion.
It consists of the following steps:
* Defining the model
* Defining the Training P... | github_jupyter |
# Malaria Detection
Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable.
In 2017, there were an estimated 219 million cases of malaria in 90 countries.
Malaria deaths reached 435 000 ... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Surprise Singular Value Decomposition (SVD)
This notebook serves both as an introduction to the [Surprise](http://surpriselib.com/) library, and also introduces the 'SVD' algorithm which is very similar to ALS ... | github_jupyter |
# Moving Square Video Prediction
This is the third toy example from Jason Brownlee's [Long Short Term Memory Networks with Python](https://machinelearningmastery.com/lstms-with-python/). It illustrates using a CNN LSTM, ie, an LSTM with input from CNN. Per section 8.2 of the book:
> The moving square video prediction... | github_jupyter |
# Bayesian Optimization for Single-Interface Nanoparticle Discovery
**Notebook last update: 3/26/2021** (clean up)
This notebook contains the entire closed-loop process for SINP discovery with BO through SPBCL synthesis, STEM-EDS characterization, as reported in Wahl et al. *to be submitted* 2021.
```
import pandas ... | github_jupyter |
# Shallow Copy Versus Deep Copy Operations
Here's the issue we are looking at now: when we make a copy of an object that contains other objects, what happens if the object we are copying "contains" other objects. So, if `list_orig` has `inner_list` as one of its members, like...
`list_orig = [1, 2, [3, 5], 4]`
and w... | github_jupyter |
```
import tensorflow as tf
import pickle
import numpy as np
def load(data_path):
with open(data_path,'rb') as f:
mnist = pickle.load(f)
return mnist["training_images"], mnist["training_labels"], mnist["test_images"], mnist["test_labels"]
class MnistData:
def __init__(self, filenames, need_shuffl... | github_jupyter |
# MLP example using PySNN
```
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from tqdm import tqdm
from pysnn.connection import Linear
from pysnn.neuron import LIFNeuron, Inp... | github_jupyter |
<a href="https://csdms.colorado.edu/wiki/ESPIn2020"><img style="float: center; width: 75%" src="../../../media/ESPIn.png"></a>
# Introduction to Landlab: Creating a simple 2D scarp diffusion model
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorial... | github_jupyter |
## Padding Characters around Strings
Let us go through how to pad characters to strings using Spark Functions.
```
%%HTML
<iframe width="560" height="315" src="https://www.youtube.com/embed/w85C18tvYNA?rel=0&controls=1&showinfo=0" frameborder="0" allowfullscreen></iframe>
```
* We typically pad characters to ... | github_jupyter |
# NBA Free throw analysis
Now let's see some of these methods in action on real world data.
I'm not a basketball guru by any means, but I thought it would be fun to see whether we can find players that perform differently in free throws when playing at home versus away.
[Basketballvalue.com](http://basketballvalue.com... | github_jupyter |
# Noise Detection Algorithm
The data presented are measurements of a gaussian beam for varying beam-frequencies and distances. Due to technical difficulties, our measuring device would sometimes crash and provide us with completely noisy data, or data that was only half complete. Our intent was to automize the measuri... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good model.
This does have the potential to make the project quite messy! Before subm... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import time
data = pd.read_csv('mnist.csv')
data.head()
train_data = data.sample(frac=0.8)
test_data = data.drop(train_data.index)
train_labels = train_data['label'].values
train_data = train_data.drop('label', axis=1).values
test_... | github_jupyter |
```
from toolz import curry
import pandas as pd
import numpy as np
from scipy.special import expit
from linearmodels.panel import PanelOLS
import statsmodels.formula.api as smf
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
style.use("ggplot")
```
# Difference-in-Diferences... | github_jupyter |
<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#AlphaFold2 w/ MMseqs2
Easy to use version of AlphaFold 2 [(Jumper et al. 2021, Nature)](https://www.natu... | github_jupyter |
# Exercise 5 - Variational quantum eigensolver
## Historical background
During the last decade, quantum computers matured quickly and began to realize Feynman's initial dream of a computing system that could simulate the laws of nature in a quantum way. A 2014 paper first authored by Alberto Peruzzo introduced the *... | github_jupyter |
OK, to begin we need to import some standart Python modules
```
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 12 13:21:45 2016
@author: GrinevskiyAS
"""
from __future__ import division
import numpy as np
from numpy import sin,cos,tan,pi,sqrt
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pypl... | github_jupyter |
## cuDF perf tests
### Loading financial time-series (per-minute ETFs) data from CSV files into a cuDF and running the queries
```
data_path = '/workspace/data/datasets/unianalytica/group/analytics-perf-tests/symbols/'
import sys
import os
import csv
import pandas as pd
import numpy as np
import cudf
from pymapd impor... | github_jupyter |
# Tutorial 3: SQL data source
## Preparing
### Step 1. Install LightAutoML
Uncomment if doesn't clone repository by git. (ex.: colab, kaggle version)
```
#! pip install -U lightautoml
```
### Step 2. Import necessary libraries
```
# Standard python libraries
import os
import time
import requests
# Installed libra... | github_jupyter |
# Chapter3 ニューラルネットワークの基本
## 3. 糖尿病の予後予測【サンプルコード】
```
# 必要なパッケージのインストール
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import TensorDataset, DataLoader
from torc... | github_jupyter |
```
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
The raw code for this Jupyter notebook is by default hidden for easier rea... | 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 |
# Adadelta
:label:`sec_adadelta`
Adadelta is yet another variant of AdaGrad (:numref:`sec_adagrad`). The main difference lies in the fact that it decreases the amount by which the learning rate is adaptive to coordinates. Moreover, traditionally it referred to as not having a learning rate since it uses the amount of ... | github_jupyter |
<a href="https://githubtocolab.com/giswqs/geemap/blob/master/examples/notebooks/65_vector_styling.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a>
Uncomment the following line to install [geemap](https://geemap.org) if needed.
```
# !pip install gee... | github_jupyter |
<a href="https://colab.research.google.com/github/mscouse/TBS_investment_management/blob/main/PM_labs_part_4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
[](https://colab.r... | github_jupyter |
# Section 2.2: Naive Bayes
In contrast to *k*-means clustering, Naive Bayes is a supervised machine-learning (ML) algorithm. It provides good speed and good accuracy and is often used in aspects of natural-language processing such text classification or, in our case in this section, spam detection.
Spam emails are mo... | github_jupyter |
# Computing FSAs
**(C) 2017-2019 by [Damir Cavar](http://damir.cavar.me/)**
**Version:** 1.0, September 2019
**License:** [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/) ([CA BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))
## Introduc... | github_jupyter |
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
from constants_and_util import *
import matplotlib.pyplot as plt
import pandas as pd
import random
import numpy as np
from copy import deepcopy
from scipy.signal import argrelextrema
import statsmodels.api as sm
from scipy.special import expit
from scipy.stats i... | github_jupyter |
# smFRET Analysis
This notebook is for simple analysis of smFRET data, starting with an hdf5 file and ending with a FRET efficiency histogram that can be fitted with a gaussians. Burst data can be exported as a .csv for analysis elsewhere.
You can analysis uncorrected data if you are simply looking for relative chang... | github_jupyter |
# Verzweigung
#### Marcel Lüthi, Departement Mathematik und Informatik, Universität Basel
### If-Anweisung
### Anweisungsblöcke
Anweisungsblöcke sind geklammerte Folgen von Anweisungen:
```
{
Anweisung1;
Anweisung2;
...
Anweisung3;
}
```
``then`` und ``else``-... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (8,10)
class CancelOut(keras.layers.Layer):
... | github_jupyter |
# Deep Learning and Transfer Learning with pre-trained models
This notebook uses a pretrained model to build a classifier (CNN)
```
# import required libs
import os
import keras
import numpy as np
from keras import backend as K
from keras import applications
from keras.datasets import cifar10
from keras.models import... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.o... | github_jupyter |
# Example 1b: Spin-Bath model (Underdamped Case)
### Introduction
The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.
In this example we show the evolution of a single two-level system in contact with a single Bos... | github_jupyter |
# Dataprep
### Objective
Crawls through raw_data directory and converts diffusion and flair into a data array
### Prerequisites
All diffusion and FLAIR should be registrated and put in a NIFTI file format.
### Data organisation
- All b0 diffusion should be named "patientid_hX_DWIb0.nii.gz" where "hX" corresponds... | github_jupyter |
# DAY0 - Looking for Dataset + Problem
```
# needed to make web requests
import requests
#store the data we get as a dataframe
import pandas as pd
#convert the response as a structured json
import json
#mathematical operations on lists
import numpy as np
#parse the datetimes we get from NOAA
from datetime import d... | github_jupyter |
# Mask R-CNN
This notebook shows how to train a Mask R-CNN object detection and segementation model on a custom coco-style data set.
```
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt
sys.path.insert(0, '../librarie... | github_jupyter |
```
import sys
sys.path.append('./../')
%load_ext autoreload
%autoreload 2
from ontology import get_ontology
ontology = get_ontology('../data/doid.obo')
name2doid = {term.name: term.id for term in ontology.get_terms()}
doid2name = {term.id: term.name for term in ontology.get_terms()}
import numpy as np
import re
```
... | github_jupyter |
<a href="https://colab.research.google.com/github/mirianfsilva/The-Heat-Diffusion-Equation/blob/master/FiniteDiff_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Implementation of schemes for the Heat Equation:
- Forward Time, Centered Spac... | github_jupyter |
# Overview
The tool serves to let you create task files from CSVs and zip files that you upload through the browser
```
import ipywidgets as ipw
import pandas as pd
import json, io, os, tempfile
import fileupload as fu
from IPython.display import display, FileLink
def upload_as_file_widget(callback=None):
"""Crea... | github_jupyter |
<div class="devsite-table-wrapper"><table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https://www.tensorflow.org/tfx/tutorials/transform/simple">
<img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a></td>
<td><a target="_blank" href="https://colab.researc... | github_jupyter |
## U.S. GDP vs. Wage Income
### For every wage dollar paid, what is GDP output?
- Each worker on average currently contributes over
90,000 dollars annually of goods and services valued as GDP.
- Each worker on average currently earns about
43,300 dollars annually (steadily up from 35,000 since the 1990's).
... | github_jupyter |
# WARNING
**Please make sure to "COPY AND EDIT NOTEBOOK" to use compatible library dependencies! DO NOT CREATE A NEW NOTEBOOK AND COPY+PASTE THE CODE - this will use latest Kaggle dependencies at the time you do that, and the code will need to be modified to make it work. Also make sure internet connectivity is enabled... | github_jupyter |
##### Copyright 2020 The Cirq Developers
```
#@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 agre... | github_jupyter |
- title: Equivalence between Policy Gradients and Soft Q-Learning
- summary: Inspecting the gradients of entropy-augmented policy updates to show their equivalence
- author: Braden Hoagland
- date: 2019-08-12
- image: /static/images/soft_q.png
# Introduction
This article will dive into a lot of the math surrounding t... | github_jupyter |
----
<img src="../../../files/refinitiv.png" width="20%" style="vertical-align: top;">
# Data Library for Python
----
## Content layer - Pricing stream - Used as a real-time data cache
This notebook demonstrates how to retrieve level 1 streaming data (such as trades and quotes) either directly from the Refinitiv Dat... | 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>
# Start-to-Finish Example: `GiRaFFE_NRPy` 3D tests
### Aut... | github_jupyter |
```
import keras
import keras.backend as K
from keras.datasets import mnist
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, Bat... | github_jupyter |
# Lab 2 Single Qubit Gates
Prerequisite
[Ch.1.3 Representing Qubit States](https://qiskit.org/textbook/ch-states/representing-qubit-states.html)
[Ch.1.4 Single Qubit Gates](https://qiskit.org/textbook/ch-states/single-qubit-gates.html)
Other relevant materials
[Grokking the Bloch Sphere](https://javafxp... | github_jupyter |
# PyStan: Golf case study
Source: https://mc-stan.org/users/documentation/case-studies/golf.html
```
import pystan
import numpy as np
import pandas as pd
from scipy.stats import norm
import requests
from lxml import html
from io import StringIO
from matplotlib import pyplot as plt
```
Aux functions for visualizat... | github_jupyter |
# #1 Discovering Butterfree - Feature Set Basics
Welcome to **Discovering Butterfree** tutorial series!
This first tutorial will cover some basics of Butterfree library and you learn how to create your first feature set :rocket: :rocket:
Before diving into the tutorial make sure you have a basic understanding of the... | github_jupyter |
## Stacking
### 參考資料:
[Kaggle ensembling guide](https://mlwave.com/kaggle-ensembling-guide/)
<p></p>
[Introduction to Ensembling/Stacking in Python](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python)
#### 5-fold stacking
![](https://pic4.zhimg.com/80/v2-84dbc338e11fb89320f2ba310ad69ceb_hd... | github_jupyter |
```
import numpy as np
import json
from PIL import Image, ImageDraw
import os
import cv2
import pandas as pd
from tqdm import tqdm
import shutil
import random
import matplotlib.pyplot as plt
%matplotlib inline
from procrustes import procrustes
from sklearn.decomposition import PCA
import sys
sys.path.append('../infe... | github_jupyter |
<img align="left" width="40%" src="http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png">
<br>Patrick BROCKMANN - LSCE (Climate and Environment Sciences Laboratory)
<hr>
### Discover Milankovitch Orbital Parameters over Time by reproducing figure from https://biocycle.atmos.colostate.edu/shiny/Milankovitch/
```
from... | github_jupyter |
# Tutorial 5: Inception, ResNet and DenseNet
**Filled notebook:**
[](https://github.com/phlippe/uvadlc_no... | github_jupyter |
# Explorando Cartpole con Reinforcement Learning usando Deep Q-learning
Este cuaderno es una modificación del tutorial de [Pytorch RL DQN](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)
Sigue la línea de clase de Reinforcement Learning, Q-learning & OpenAI de la RIIA 2019
```
# Veamos de ... | github_jupyter |
```
import pandas as pd
import numpy as np
from boruta import BorutaPy
from IPython.display import display
```
### Data Prep
```
df = pd.read_csv('data/aml_df.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
display(df.info())
df.head()
#holdout validation set
final_val = df.sample(frac=0.2)
#X and y for holdout... | 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 |
## Preamble
### Import libraries
```
import os, sys
# Import Pandas
import pandas as pd
# Import Plotly and Cufflinks
# Plotly username and API key should be set in environment variables
import plotly
plotly.tools.set_credentials_file(username=os.environ['PLOTLY_USERNAME'], api_key=os.environ['PLOTLY_KEY'])
import ... | github_jupyter |
# Task 9: Random Forests
_All credit for the code examples of this notebook goes to the book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" by A. Geron. Modifications were made and text was added by K. Zoch in preparation for the hands-on sessions._
# Setup
First, import a few common modules, ensure Matp... | github_jupyter |
```
# default_exp data.tabular
```
# Data Tabular
> Main Tabular functions used throughout the library. This is helpful when you have additional time series data like metadata, time series features, etc.
```
#export
from tsai.imports import *
from tsai.utils import *
from fastai.tabular.all import *
#export
@delegat... | github_jupyter |
<img src="https://github.com/pmservice/ai-openscale-tutorials/raw/master/notebooks/images/banner.png" align="left" alt="banner">
# Part 4: Drift Monitor
The notebook will train, create and deploy a Credit Risk model. It will then configure OpenScale to monitor drift in data and accuracy by injecting sample payloads f... | github_jupyter |
# Difference between gridded field (GRIB) and scattered observations (BUFR)
<img src="http://pandas.pydata.org/_static/pandas_logo.png" width=200>
In this example we will load a gridded model field in GRIB format and a set of observation data in BUFR format. We will then use Metview to examine the data, and compute a... | github_jupyter |
# Hands-on: `pandas` & Data Wrangling
By now, you have some experience in using the `pandas` library which will be very helpful in this module. In this notebook, we will explore more of `pandas` but in the context of data wrangling. To be specific, we will be covering the following topics:
- Reading in data
- Descript... | github_jupyter |
# Building your Deep Neural Network: Step by Step
Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!
- In this notebook, you will implement all the functio... | github_jupyter |
# Info Extraction
it's much more easier to extract information of model from pytorch module than onnx...onnx doesn't have output shape
```
import onnx
# Load the ONNX model
model = onnx.load("onnx/vgg19.onnx")
# Check that the IR is well formed
onnx.checker.check_model(model)
# Print a human readable representatio... | github_jupyter |
<a href="https://colab.research.google.com/github/TheoPantaz/Motor-Imagery-Classification-with-Tensorflow-and-MNE/blob/master/Motor_Imagery_clsf.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Install mne
```
!pip install mne
```
Import libraries
... | github_jupyter |
# Titanic Data Science Solutions
### This notebook is a companion to the book [Data Science Solutions](https://www.amazon.com/Data-Science-Solutions-Startup-Workflow/dp/1520545312).
The notebook walks us through a typical workflow for solving data science competitions at sites like Kaggle.
There are several excell... | github_jupyter |
```
"""
LICENSE MIT
2020
Guillaume Rozier
Website : http://www.covidtracker.fr
Mail : guillaume.rozier@telecomnancy.net
README:
This file contains scripts that download data from data.gouv.fr and then process it to build many graphes.
I'm currently cleaning the code, please ask me if something is not clear enough.
T... | github_jupyter |
## First step in gap analysis is to determine the AEP based on operational data.
```
%load_ext autoreload
%autoreload 2
```
This notebook provides an overview and walk-through of the steps taken to produce a plant-level operational energy asssessment (OA) of a wind plant in the PRUF project. The La Haute-Borne wind f... | github_jupyter |
<img src='https://mundiwebservices.com/build/assets/Mundi-Logo-CMYK-colors.png' align='left' width='15%' ></img>
# Mundi GDAL
```
from mundilib import MundiCatalogue
# other tools
import os
import numpy as np
from osgeo import gdal
import matplotlib.pyplot as plt
```
### Processing of an in-memory image (display/m... | github_jupyter |
# End to End example to manage lifecycle of ML models deployed on the edge using SageMaker Edge Manager
**SageMaker Studio Kernel**: Data Science
## Contents
* Use Case
* Workflow
* Setup
* Building and Deploying the ML Model
* Running the fleet of Virtual Wind Turbines and Edge Devices
* Cleanup
## Use Case
The ... | github_jupyter |
# TRANSCOST Model
The TRANSCOST model is a vehicle dedicated-dedicated system model for determining the cost per flight (CpF) and Life Cycle Cost (LCC) for launch vehicle systems.
Three key cost areas make up the model:
1. Development Cost
1. Production Cost
1. Operations Cost
Each of these cost areas and strategie... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
# Experiments for ER Graph
## Imports
```
%load_ext autoreload
%autoreload 2
import os
import sys
from collections import OrderedDict
import logging
import math
from matplotlib import pyplot as plt
import networkx as nx
import numpy as np
import torch
from torchdiffeq import odeint, odeint_adjoint
sys.path.appen... | github_jupyter |
Parallel Single-channel CSC
===========================
This example compares the use of [parcbpdn.ParConvBPDN](http://sporco.rtfd.org/en/latest/modules/sporco.admm.parcbpdn.html#sporco.admm.parcbpdn.ParConvBPDN) with [admm.cbpdn.ConvBPDN](http://sporco.rtfd.org/en/latest/modules/sporco.admm.cbpdn.html#sporco.admm.cbp... | github_jupyter |
# Let's compare 4 different strategies to solve sentiment analysis:
1. **Custom model using open source package**. Build a custom model using scikit-learn and TF-IDF features on n-grams. This method is known to work well for English text.
2. **Integrate** a pre-built API. The "sentiment HQ" API provided by indico has ... | github_jupyter |
```
#Python Basics
#Dictionaries in Python
#Keys and Elements
#Dictionary is defined by {"key1": element1, "key2": element2, "key3": element3}
#Examples
dic1={"Fist Name":"Behdad", "Surname": "Jam", "Age": 35, "Records": [11.32, 14.34, 13.003]}
print(dic1)
History={"band1":1943, "bandx": 1967, "bandy": 1984, "band4":... | github_jupyter |
```
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV
import pandas as pd
df = np.genfromtxt('D:/Github/eeg.fem/public/data/Musical/6080072/data_for_train/ALL_PCA_64.csv',delimiter=',')
x = df[:, :-1]
y = df[:... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import pickle
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import cv2
from sklearn.model_selection import KFold, cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier, Baggi... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from sklearn.utils import shuffle
import re
import time
import collections
import os
import itertools
from sklearn.cross_validation import train_test_split
def build_dataset(words, n_words, atleast=1):
count = [['GO', 0], ['PAD', 1], ['EOS', 2], ['UNK', 3]]
counter... | github_jupyter |
<a href="https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/tf_Face_SSD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction
In this task we will detect faces in the wild using single shot detector (SSD) models. T... | github_jupyter |
# Working with Python: functions and modules
## Session 4: Using third party libraries
- [Matplotlib](#Matplotlib)
- [Exercise 4.1](#Exercise-4.1)
- [BioPython](#BioPython)
- [Working with sequences](#Working-with-sequences)
- [Connecting with biological databases](#Connecting-with-biological-databases)
- [Exercise 4... | github_jupyter |
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