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.