<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>deep learning on Tom Roth</title><link>https://tomroth.dev/tags/deep-learning/</link><description>Recent content in deep learning on Tom Roth</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Mon, 06 Nov 2017 00:00:00 +0000</lastBuildDate><atom:link href="https://tomroth.dev/tags/deep-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>The Keras functional API: five simple examples</title><link>https://tomroth.dev/keras/</link><pubDate>Mon, 06 Nov 2017 00:00:00 +0000</pubDate><guid>https://tomroth.dev/keras/</guid><description>Building models in Keras is straightforward and easy. If you&amp;rsquo;re reading this, you&amp;rsquo;re likely familiar with the Sequential model and stacking layers together to form simple models. But what if you want to do something more complicated?
Enter the functional API. For complex models the functional API is really the only way to go - it can do all sorts of things that just aren&amp;rsquo;t possible with the Sequential model. Models with multiple inputs and outputs, models with shared layers - once you start designing architectures that need these things, you will have to use the functional API to build your model.</description></item></channel></rss>