What is Recurrent Neural Network (RNN)? Deep Learning Tutorial 33 (Tensorflow, Keras & Python)

Summary

This video tutorial covers the basics of Recurrent Neural Networks (RNNs) and their applications in natural language processing (NLP). It explains why RNNs are suitable for sequence modeling tasks, such as language translation, sentiment analysis, and named entity recognition, due to their ability to handle sequential data and remember context. The tutorial also highlights the limitations of using traditional neural networks for these tasks, emphasizing the importance of sequence in language. Practical examples, including Google autocomplete and translation, illustrate RNNs' effectiveness in real-life scenarios.

Takeaways

• 🧠 Recurrent Neural Networks (RNNs) are primarily used for natural language processing (NLP) tasks, in contrast to Convolutional Neural Networks (CNNs) which are mainly used for image processing.
• 🔄 RNNs are designed to handle sequence data where the order of elements matters, as opposed to traditional Artificial Neural Networks (ANNs) which do not consider sequence order.
• 💡 Google's Gmail auto-complete feature is an example of an application of RNNs, demonstrating their ability to predict and complete sentences based on the context of previous words.
• 🌐 Google Translate is another application of RNNs, showcasing their use in translating sentences from one language to another, highlighting the importance of sequence in language translation.
• 🔍 Named Entity Recognition (NER) is a use case for RNNs where the network identifies and categorizes entities in a text, such as names of people, companies, and places.
• 📊 Sentiment Analysis is a task where RNNs can determine the sentiment of a product review, classifying it into categories like one star to five stars.
• 📈 One of the limitations of using ANNs for sequence modeling is the fixed size of input and output layers, which does not accommodate variable sentence lengths.
• 🔢 The computational inefficiency of ANNs in sequence tasks is due to the need for one-hot encoding of words, which can lead to a very large input layer with many neurons.
• 🔗 The lack of parameter sharing in ANNs when dealing with different sentence structures that convey the same meaning is another drawback, as RNNs can share parameters through their recurrent connections.
• 🔄 RNNs maintain a 'memory' of previous inputs through their recurrent structure, allowing them to understand and process language with context, unlike ANNs.
• 🔧 Training an RNN involves passing all training samples through the network multiple times (epochs), adjusting weights based on loss to minimize errors and improve predictions.

Q & A

What is the main focus of the video script?

The video script focuses on explaining Recurrent Neural Networks (RNNs), their applications in natural language processing (NLP), and how they differ from other types of neural networks like CNNs and ANNs.

What are the primary use cases for Recurrent Neural Networks mentioned in the script?

The primary use cases for RNNs mentioned are auto-completion in Gmail, language translation using Google Translate, Named Entity Recognition (NER), and Sentiment Analysis.

Why are Recurrent Neural Networks particularly suited for natural language processing tasks?

RNNs are suited for NLP tasks because they can handle sequence data where the order of elements is important, unlike traditional neural networks which do not consider the sequence of inputs.

How does the script describe the process of auto-completion in Gmail using RNNs?

The script describes the auto-completion process as the user types a sentence, and the RNN embedded in Gmail predicts and completes the sentence based on the context provided by the user's input.

What are the challenges with using a simple neural network for sequence modeling problems?

The challenges include the need for a fixed sentence size, high computational cost due to one-hot encoding, and the inability to share parameters across different sentences with the same meaning.

How does the script explain the concept of one-hot encoding in the context of neural networks?

One-hot encoding is explained as a method of converting words into vectors where each word is represented by a vector with a '1' at its corresponding position in the vocabulary and '0's elsewhere.

What is the significance of the sequence in language translation according to the script?

The sequence is significant because changing the order of words in a sentence can completely alter its meaning, which is a key aspect that RNNs can handle but simple neural networks cannot.

How does the script describe the architecture of an RNN?

The script describes an RNN as having a single hidden layer that processes words one by one, carrying the context from the previous word to the next, thus providing a memory of the sequence.

What is the purpose of Named Entity Recognition (NER) as explained in the script?

The purpose of NER is to identify and classify entities in text, such as recognizing 'Dhaval' and 'baby yoda' as person names in the given example.

How does the script explain the training process of an RNN for Named Entity Recognition?

The script explains the training process as initializing the network weights, processing each word in the training samples, calculating the predicted output, comparing it with the actual output, and adjusting the weights to minimize the loss through backpropagation.

What are the components of the language translation process using RNNs as described in the script?

The components include an encoder that processes the input sentence and a decoder that translates it into the target language, with the process requiring all words to be supplied before translation can occur.

Outlines

00:00

🧠 Introduction to Recurrent Neural Networks (RNNs) for NLP

This paragraph introduces the concept of Recurrent Neural Networks (RNNs) and their primary application in natural language processing (NLP) tasks. It contrasts RNNs with Convolutional Neural Networks (CNNs), which are more suited for image processing. The speaker outlines various real-life applications of RNNs, such as Gmail's auto-complete feature, Google Translate, Named Entity Recognition (NER), and Sentiment Analysis. The importance of sequence in language is emphasized, and the limitations of using simple neural networks for sequence modeling problems are discussed, including issues with sentence size variability, neuron count, and computational efficiency.

05:01

🔄 Understanding the Working of RNNs in Named Entity Recognition

This section delves deeper into how RNNs work, using Named Entity Recognition (NER) as an example. It explains the process of converting words into vectors, typically through one-hot encoding, and then feeding these vectors into a neural network layer. The unique aspect of RNNs is their ability to maintain a memory of previous outputs, which is crucial for understanding the context in language. The paragraph illustrates how RNNs process words sequentially, carrying the context from previous words to inform the interpretation of subsequent words, thus effectively recognizing named entities within a text.

10:04

🔄 Deep Dive into RNN's Sequential Processing and Training

The paragraph further explains the sequential processing nature of RNNs, emphasizing the 'time travel' concept where the same layer of neurons is used repeatedly at different time steps to process a sequence of words. It clarifies that despite the appearance of multiple layers in the visual representation, there is essentially only one hidden layer that is being used over time. The training process of RNNs is also discussed, detailing how the network is trained on samples for tasks like NER, using backpropagation and gradient descent to minimize loss and adjust weights over multiple epochs.

15:06

🌐 RNNs in Language Translation and Deep RNN Architecture

The final paragraph discusses the application of RNNs in language translation, describing the process of encoding and decoding where the network uses the context of all input words to produce an accurate translation. It also touches upon the possibility of having deep RNNs with multiple hidden layers to capture more complex patterns in data. The paragraph concludes by reinforcing the importance of RNNs in handling sequence-dependent tasks where the order of elements significantly impacts the outcome, and invites viewers to look forward to more tutorials on RNNs and NLP.

Keywords

💡Recurrent Neural Network (RNN)

Recurrent Neural Network, or RNN, is a type of artificial neural network used in various fields, particularly in natural language processing (NLP). It is designed to recognize patterns in sequences of data, such as text or speech. In the video, RNN is highlighted as the main topic, with examples of its applications in Gmail's auto-complete feature and Google Translate, showcasing its ability to handle sequential data effectively.

💡Natural Language Processing (NLP)

Natural Language Processing refers to the interaction between computers and human language. It involves understanding, interpreting, and generating human language in a way that computers can process and analyze. The video emphasizes the use of RNNs in NLP tasks, such as translation and sentiment analysis, where the sequence of words is crucial for meaning.

💡Sequence Modeling

Sequence Modeling is a concept in machine learning where the model is trained to understand the order of the data points. In the context of the video, sequence modeling is essential for tasks like translation and Named Entity Recognition (NER), where the arrangement of words affects the overall meaning, and RNNs are particularly adept at handling such sequences.

💡Auto-complete

Auto-complete is a feature in many text input systems that predicts and suggests the completion of a word or sentence based on the initial characters or words typed by the user. The video script mentions Gmail's auto-complete as an example of RNNs in action, where the network predicts the next word in a sequence after the user types a few characters.

💡Google Translate

Google Translate is an online service that automatically translates text from one language to another. The script uses Google Translate as an example of how RNNs can be applied to language translation, where the network learns to map sequences of words from one language to another while preserving their meaning.

💡Named Entity Recognition (NER)

Named Entity Recognition is a subfield of NLP that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, etc. The video script explains how RNNs can be used for NER, where the network learns to recognize and categorize entities within a given text.

💡Sentiment Analysis

Sentiment Analysis is the process of determining the sentiment or emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer. The video mentions sentiment analysis as a use case for RNNs, where the network evaluates the sentiment of a product review or any given text.

💡One-hot Encoding

One-hot Encoding is a representation of categorical variables as a binary vector, where each category is represented by a unique vector with a single '1' and the rest '0's. In the video, one-hot encoding is discussed as a method for converting words into vectors for input into neural networks, which is crucial for handling text data in machine learning models.

💡Artificial Neural Network (ANN)

An Artificial Neural Network is a computational model inspired by the way biological neural networks in the human brain process information. The video script contrasts ANNs with RNNs, explaining that while ANNs are good for certain tasks, they do not handle sequential data as effectively as RNNs, which is why RNNs are preferred for tasks like translation and NER.

💡Training Samples

Training samples are the input-output pairs used to train a machine learning model. In the context of the video, training samples are used to teach the RNN to perform tasks like NER, where the input is a statement and the output is the classification of each word as a person's name or not.

💡Backpropagation

Backpropagation is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights. The video script explains that after calculating the loss between the predicted and actual outputs, backpropagation is used to adjust the weights of the network, which is a fundamental part of the training process for RNNs.

Highlights

Introduction to Recurrent Neural Networks (RNNs) and their primary use in natural language processing (NLP) tasks.

Comparison of CNNs and RNNs, with CNNs being mainly for image processing and RNNs for NLP.

Explanation of RNNs' ability to handle sequence modeling problems where the order of data matters.

Demonstration of RNNs in Gmail's auto-complete feature, showcasing its practical application.

Use of RNNs in Google Translate for efficient language translation.

Application of RNNs in Named Entity Recognition (NER) to identify entities like person names in text.

RNNs in Sentiment Analysis to determine the sentiment of product reviews.

Challenges with using simple neural networks for sequence problems due to their inability to handle variable sentence lengths.

Issues with high computational costs when converting words to vectors using one-hot encoding for neural networks.

Problem of parameter non-sharing in translation when different English sentences result in the same Hindi translation.

The importance of sequence in language translation and how it affects the meaning of sentences.

Introduction to the concept of Named Entity Recognition (NER) and its goal to identify entities within text.

Description of how RNNs process text word by word while carrying the context from previous words.

Explanation of the training process for RNNs, including initialization, loss calculation, and weight adjustment.

RNN architecture in the context of language translation, emphasizing the encoder and decoder parts.

Potential for deep RNNs with multiple hidden layers for more complex processing.

Summary of RNN's architecture and its necessity for sequence-dependent tasks in language.

Transcripts

So far in our deep learning tutorial

series we looked at

Artificial Neural Network and

Convolutional Neural Network which is

mainly used for image processing.

In this video we will talk about

Recurrent Neural Network which is used

mainly for

natural language processing tasks so if

you think about deep learning overall,

CNNs are mainly for images, RNNs are

mainly for

NLP. There are other use cases as well so

we'll understand

how Recurrent Neural Network works and

we'll look at different

applications of RNN in the field of NLP

as well as some other domains.

We will be looking at some real-life use

cases where

sequence models are useful. You must have

used

Google mail-Gmail. Here, when you type in

a sentence it will auto complete it. So

just see,

when I type "not interested at this time"

is something it auto completed.

So google has this RRN or Recurrent

Neural Network

embedded into it where, when you type in

a sentence "not interested at"

it will auto complete with "this time". If

you say "we'll let you know if it changes"

it will also say "in the future" so this

saves you time.

It will write the sentence for you.

Another use case is translation.

You must have used Google Translate

where you can translate

sentence from one to another language

easily.

Third use case is Named Entity

Recognization

where in the X you know you give Neural

Network a statement

and in the Y Neural Network will tell

you the person name

the company and time. Rudolph Smith must

be a millionaire with Tesla's

prices skyrocketing.

So these are various use cases where

using sequence models or

RNN-Recurrent Neural Network helps.

The fourth use case is Sentiment

Analysis where you have a paragraph and

it will tell you

the sentiment whether this product

review is One star, Two star

and so on. Now you would think -

Why can't we use a simple Neural Network

to solve this problem? See all these

problems

they are called Sequence Modeling

problem

because the sequence is important. When

it comes to human language

sequence is very important. For example

when you say, "how are you?" versus

"you are how" doesn't make sense,

right? So the sequence is important here

and you would

think - Why don't we use simple neural

network for that? Well,

let's try it. So for language translation

how about we build this kind of neural

network

we know where input is the English

statement and the output could be Hindi

statement

Once I build this network, what if my

sentence size changes? So i might be

inputting

different sentence size and with a fixed

neural network architecture it's not

going to work

because you have to decide how many

neurons are there in the input and

output layer.

So with language translation, number of

neurons

becomes a problem. Like what do you

decide as a size of neurons?

Now one would argue okay I would decide

let's say a

huge size let's say 100 neurons and

remaining

if I am saying, did you eat biryani? So it

will occupy 4 neuron.

Remaining 96 I will just say 0

or you know blank statement. That might

work

but still it's not ideal. The second

issue is too much computation.

You all know neural networks work

on numbers, they don't work on string.

So you have to convert your word into

a vector. So one of the ways of

converting that into a vector

is -l et's say there are 25000 words

in your vocabulary and you will do one

hot encoding

where you know "how" let's say is at 46th

position

"are" is let's say second position "you" is

let's say

at 17000th position. So at that position

you put 1,

remaining position you put 0 and that's

called

one hot encoding. You have to do similar

thing for output as well.

But you realize this will increase too

much computation.

Each of the word when you convert it to

a vector

you know how many neurons you need in

the input layer itself.

Its humongous. The third issue

is this - Sometimes when you translate

language

you for let's say two different English

statements you might have a

Hindi statement. So in this case

when I say "On sunday I ate golgappa"

let's say

I train this network based on this

statement

and then for 'On Sunday' let's say it will

adjust the weights of

all these edges which I have highlighted

in yellow color.

Same statement I can say differently. I

can say "I

ate golgappa on Sunday". So now on Sunday

the meaning of on Sunday

is same but here neural network has to

learn different set of edges you see all

these edges are in yellow color.

So the parameters are not shared.

We looked at in our Convolutional Neural

Network tutorial as well that by

using convolution operation we can share

the parameters.

Here, the use of

ANN or Artificial Neural Network doesn't

allow you to

do that okay. Also

the most important part in all this

discussion is the sequence.

See when you have structured data, for

example you're trying to figure out

if the transaction is fraud or not and

let's say

your features are transaction amount,

whether the transaction was made out of

country

or whether the SSN that customer

provided is correct or not.

Now here if you change the order of this

features, let's say 'ssn correct?' I supply

you know my first neuron

it's not going to affect anything you know because

the sequence in which you supply the

input doesn't matter.

Whereas if you have

English to Hindi

translation

and instead of saying "I ate golgappa on sunday'

and if I say 181 00:06:25,360 --> 00:06:31,600 "I ate Sunday on golgappa" the meaning

becomes totally different. So now you

cannot say that

the Hindi translation is 'ravivar ko mene golgappe khaye"

because it becomes invalid so sequence

is very very important that's why

Artificial Neural Network doesn't work

in this case.

Just to summarize these are the three

major problems

with using ANN for sequence problems.

Let's once again talk about Named Entity

Recognition.

Let's say 'Dhaval loves baby yoda'

I love my baby grogu. I love Mandalorian

series and

we have got this nice baby grogu at

our home

which actually talks with us. In this

statement

Dhaval and baby yoda are person names,

okay.

So the whole purpose of Named Entity

Recognization is

to find out the

entity you know like 'Dhaval' as an entity

is a person 'baby yoda'

as an entity as a person so that's the

whole

goal of NER.

Now you can represent this as ones and

zero. So if the

word is person's name you would mark it as

one

and if it is not a person's name you would mark it as zero. so let's see how RNN works here. RNN is also called Recurrent Neural Network. so first of all you have to convert Dhaval into some vector. It doesn't matter how you convert 216 00:07:57,199 --> 00:08:01,680 it you can take a vocabulary and use one

hot encoding and there are other ways

of

vectorizing a word. Then

you have a layer of neurons. So these are

all individual neurons.

Let's say this is one layer. It's a

hidden layer

you supply that and you get one output

okay.

So each neuron all you know has a sigma

function

and activation function. So now while

processing the statement 'Dhaval loves

baby yoda'

now I will process it word by word.

So I supply 'Dhaval', get the output and

then I

go back again. Now I supply

'loves' converted into vector and the

previous output which I

got which was y Dhaval I now supply

that as an input to this

layer. So you see

the input of the layer is not only the

next word but the previous

output because

the language makes sense. Language needs

to carry the context

if I have just a word loss and if I

don't have Dhaval in front of it

it might mean a different thing. So there

is a context that you need

and this kind of architecture provides

your context or a memory.

In the third word again you supply 'baby'

to the same network right. So our network

has only one layer.

it has only one layer, so there is input

layer output layer and the hidden layer

is just one

and it has bunch of neurons. In that we

are repeatedly processing word

one by one okay

and you keep on doing this. Now the

benefit of this is

when i'm processing 'baby' when i get why

loves

that 'why loves' carries the state, the

previous state or previous memory

of 'Dhaval loves' you know the whole

statement.

Now i'm presenting this in a different

way make sure

these are not four different hidden

layers.

This is a time travel

okay so actual hidden layer is only one.

I am just doing a time travel. So first

when I supplied word 'Dhaval'

i got this output and output was nothing

but the activation function which I am

denoting with a1

and you need some previous activation a0

as well.

Let's say it's a vector of all zeros

then you supply second word

'loves' and use the previous output which

was yDhaval

so yDhaval and a1 they are

both same here

and then you get another output a2

where

that you supply along with the third

word 'baby'

to the same network. So these four

neurons it's the same

single layer. I am just showing

the status of it at different times okay.

So you have to be very clear on this

that these are not four different layers.

It's just one layer just because I am

showing different time steps that's why

I'm showing you almost a time travel

here

and once the network is trained

of course it will output like 'Dhaval' is

one 'loves' is zero 'baby' is one and so on

okay. So you get your NER output

individually here. One other way of

representing the same network

okay because just to avoid confusion and

to

make presentation little more clear. Many

times

in literature you will see presentation

like this -

Where each word which is an input comes

from the bottom

and there is activation. So again this

and these two diagrams are exactly same

okay

I'm just putting this word at the bottom.

Generic Representation of RNN is this.

So this is the real representation. You have

only one layer

and you are you are kind of almost in a

loop. You are

supplying the output of previous

word as an input to the second word.

So now let's talk about training. So

again

the problem we are talking about is NER

where these are my training samples okay

x and y. 'x' is a statement 'y' is whether

a given word is person name or not

so we are processing first training

sample 'Dhaval loves baby yoda'

so this one I will first

initialize my neural network weights

with some random values,

then I supply each word, then I calculate

y hat which is predicted

y, then I compare with the real y so real

y here

is 1 0 1 1 so I compare that with here

so 1

0 1 1 I compare that with y hat

and I find out the loss okay

and then I sum the loss.

So that will be my total loss. You all

know about grade and descent right. So we

compute the loss then we back propagate

the loss

and we adjust the weights. So now I take

the second statement

'Iron man punched on hulk's face'

he was very angry with hulk. Again i

calculate all the losses

then I find total loss and

then I do grid and decent to reduce the

loss.

So i keep on doing this for all my

training samples. Let's I have 100 training

samples.

Passing all hundred training samples

through this network will be one epoch.

We might do let's say 20 epochs and at

the end of the 20 epoch

my loss might become very minimum. At

that point we can say my neural network

is trained.

Let's take a look at language

translation. So in language translation

what happens is

you supply first word to your

network then you get the output

then again same network you supply

second word and the output from previous

step

as an input and of course when you

supply first where you have to

pass in some activation values let's say

all

a vector of all zeros.

Then you supply third word for fourth

word and so on

and when you're done with all the words

that's when the network starts to

translate it because

you cannot translate one word by one,

because after

the statement I can push maybe one more

word and that will just totally change

my translation.

That's why for language translation you

have to supply all the words and only

then

the network can translate for you.

So the network will translate it like

this

and the first part is called encoder

the second part is called decoder.

We will go

more in depth into all this but I want

to quickly demonstrate how

the neural network looks in the case of

language translation.

Now this layer doesn't have to be just

single layer. It can be a deep RNN as

well

where the actual network might have

multiple hidden layers

okay. So I hope that clarifies

the architecture behind RNN and you

understand why you can't use simple

neural network here and you have to use

specialized

neural network called RNN which can

memorize for you, which can

remember previous state because language

is

all about sequence. If you change the

sequence the meaning changes

so if you like this video please give

it a thumbs up

and we'll be having more Recurrent

Neural Network and NLP type

tutorials in the future videos

Thank you.