<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Netvouz / henrik / tag / python</title>
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<description>henrik&#39;s bookmarks tagged &quot;python&quot; on Netvouz</description>
<item><title>LSTM in Python: Stock Market Predictions (article) - DataCamp</title>
<link>https://www.datacamp.com/community/tutorials/lstm-python-stock-market</link>
<description>Discover Long Short-Term Memory (LSTM) networks in Python and how you can use them to make stock market predictions! In this tutorial, you will see how you can use a time-series model known as Long Short-Term Memory. LSTM models are powerful, especially for retaining a long-term memory, by design, as you will see later.</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Tue, 23 Jul 2019 14:50:24 GMT</pubDate>
</item><item><title>hadara / python-plugwise / wiki / Home – Bitbucket</title>
<link>https://bitbucket.org/hadara/python-plugwise/wiki/Home</link>
<description>python-plugwise is a Python library for communicating with Plugwise smartplugs (Circle &amp; Circle+). Currently it&#39;s known to work only with the devices with firmwares from the year 2010 and later.</description>
<category domain="http://www.netvouz.com/henrik?category=5336132243938792835">Hus &amp; Hem &gt; Home Automation</category>
<author>henrik</author>
<pubDate>Mon, 14 Feb 2011 20:29:17 GMT</pubDate>
</item><item><title>How to Develop LSTM Models for Time Series Forecasting</title>
<link>https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/</link>
<description>Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Fri, 30 Aug 2019 21:19:44 GMT</pubDate>
</item><item><title>kPOD This Python package implements an extension to the k-means clustering algorithm for use with missing data.</title>
<link>https://github.com/iiradia/kPOD</link>
<description>The k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data. In addition, k-POD presents strong advantages in computation time and resources over other methods for removing missingness, while still maintaining accuracy.</description>
<category domain="http://www.netvouz.com/henrik?category=3788618044265731538">Development &gt; Machine Learning</category>
<author>henrik</author>
<pubDate>Thu, 10 Mar 2022 09:37:04 GMT</pubDate>
</item><item><title>Learn Clustering Algorithms Using Python and SciKit-Learn</title>
<link>https://huzaifah-saleem.gitbook.io/learn-clustering-alogrithms-using-python-and-sciki/</link>
<description>Learn Clustering Algorithms Using Python and SciKit-Learn The purpose of this tutorial is to demonstrate how you can detect anomalies and clusters in your data using algorithms provided by SciKit-Learn library in python programming language.</description>
<category domain="http://www.netvouz.com/henrik?category=5419735123151897888">Artificial Intelligence AI &gt; Training and courses</category>
<author>henrik</author>
<pubDate>Fri, 03 Apr 2020 10:30:12 GMT</pubDate>
</item><item><title>sumnerboy12/mqtt-gpio-monitor · GitHub</title>
<link>https://github.com/sumnerboy12/mqtt-gpio-monitor</link>
<description>Python script for controlling and monitoring Raspberry Pi GPIO bus or Piface via MQTT</description>
<category domain="http://www.netvouz.com/henrik?category=1034309999892917417">Hus &amp; Hem &gt; Home Automation &gt; Raspberry Pi</category>
<author>henrik</author>
<pubDate>Sun, 11 Jan 2015 23:35:46 GMT</pubDate>
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