In the previous video, we learned about convolutional neural networks, which are
supervised deep learning models that have revolutionized the field of
computer vision, especially object detection in images. In this video, we
will learn about another supervised deep learning model, which is the recurrent
neural network. So far, we have seen neural networks and deep learning models
that see datapoints as independent instances. However, let's say you want to
build a model that can analyze scenes in a movie.
Well, you cannot assume that scenes in a movie are independent, and therefore,
traditional deep learning models are not suitable for this application. Recurrent
neural networks overcome this issue. Recurrent neural networks or (RNNs)
for short, are networks with loops that don't just take a new input at a time,
but also take in as input the output from the previous dat point that was fed
into the network. Accordingly, this is how the architecture of a recurrent neural
network would look like. Essentially, we can start with a normal neural network.
At time t = 0, the network takes in input x0 and outputs a0. Then, at time t = 1,
in addition to the input x1, the network also takes a0 as input,
weighted with weight w0,1, and so on and so forth. As a result, recurrent neural
networks are very good at modelling patterns and sequences of data, such as
texts, genomes, handwriting, and stock markets. These algorithms take time and
sequence into account, which means that they have a temporal dimension. A very
popular type of recurrent neural network is the long short-term memory model or
the (LSTM) model for short. It has been successfully used for many applications
including image generation, where a model trained on many
images is used to generate new novel images. Another application is
handwriting generation, which I described in the welcome video of this course. Also
LSTM models have been successfully used to build algorithms that can
automatically describe images as well as streams of videos. I think this is a good
overview of recurrent neural networks. Given that this is just an introductory
course I will leave it here. This concludes this video
on recurrent neural networks. I will see you in the next video
we will switch to unsupervised deep learning models and talk about
autoencoders.