RNN intro

Author: darkslab

assets/rnn/types.png

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       <a href="choose-project/">
         Taking a Course Project to Publication
       </a>
+      <a href="rnn/">
+        Recurrent Neural Networks
+      </a>
     </div>
 
   </div>

index.html

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+---
+layout: page
+permalink: /rnn/
+---
+
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+Table of Contents:
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+- [Intro to RNN](#intro)
+
+
+
+
+<a name='intro'></a>
+
+## Intro to RNN
+
+In this lecture note, we're going to be talking about the Recurrent Neural Networks (RNNs). One
+great thing about the RNNs is that they offer a lot of flexibility on how we wire up the neural
+network architecture. Normally when we're working with neural networks (Figure 1), we are given a fixed sized
+input vector (red), then we process it with some hidden layers (green), and then we produce a
+fixed sized output vector (blue) as depicted in the leftmost model in Figure 1. Recurrent Neural
+Networks allow us to operate over sequences of input, output, or both at the same time. For
+example, in the case of image captioning, we are given a fixed sized image and then through an RNN
+we produce a sequence of words that describe the content of that image (second model in Figure 1).
+Or for example, in the case of sentiment classification in the NLP, we are given a sequence of words
+of the sentence and then we are trying to classify whether the sentiment of that sentence is
+positive or negative (third model in Figure 1). In the case of machine translation, we can have an
+RNN that takes a sequence of words of a sentence in English, and then this RNN is asked to produce
+a sequence of words of a sentence in French, for example (forth model in Figure 1). As a last case,
+we can have a video classification RNN where we might imagine classifying every single frame of
+video with some number of classes, and most importantly we don't want the prediction to be only a
+function of the current timestep (current frame of the video), but also all the timesteps (frames)
+that have come before it in the video (rightmost model in Figure 1).
+
+<div class="fig figcenter fighighlight">
+  <img src="/assets/rnn/types.png" width="100%">
+  <div class="figcaption">Different (non-exhaustive) types of Recurrent Neural Network architectures. Red boxes are input vectors. Green boxes are hidden layers. Blue boxes are output vectors.</div>
+</div>