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Transformers in Machine Learning

Last Updated : 27 Feb, 2025

Transformer is a neural network architecture used for performing

machine learning tasks particularly in natural language processing
(NLP) and computer vision. In 2017 Vaswani et al. published a paper "
Attention is All You Need" in which the transformers architecture was
introduced. The article explores the architecture, workings and
applications of transformers.

Need For Transformers Model in Machine Learning

Transformer Architecture is a model that uses self-attention to
transform one whole sentence into a single sentence. This is useful
where older models work step by step and it helps overcome the
challenges seen in models like RNNs and LSTMs. Traditional models
like RNNs (Recurrent Neural Networks) suffer from the vanishing
gradient problem which leads to long-term memory loss. RNNs process
text sequentially meaning they analyze words one at a time.

For example, in the sentence: "XYZ went to France in 2019 when

there were no cases of COVID and there he met the president of
that country" the word "that country" refers to "France".
However RNN would struggle to link "that country" to "France"
since it processes each word in sequence leading to losing context
over long sentences. This limitation prevents RNNs from
understanding the full meaning of the sentence.

While adding more memory cells in LSTMs (Long Short-Term Memory

networks) helped address the vanishing gradient issue they still
process words one by one. This sequential processing means LSTMs
can't analyze an entire sentence at once.

For instance the word "point" has different meanings in these two
sentences:
"The needle has a sharp point." (Point = Tip)
"It is Tutorial
Deep Learning not polite toAnalysis
Data point Tutorial
at people." (Point
Python = visualization
– Data Gesture) tutorial Sign In

Traditional models struggle with this context dependence, whereas,

Transformer model through its self-attention mechanism, processes
the entire sentence in parallel addressing these issues and making it
significantly more effective at understanding context.

Architecture and Working of Transformers

1. Positional Encoding

Unlike RNNs transformers lack an inherent understanding of word order

since they process data in parallel. To solve this Positional Encodings
are added to token embeddings providing information about the
position of each token within a sequence.

2. Position-wise Feed-Forward Networks

The Feed-Forward Networks consist of two linear transformations with

a ReLU activation. It is applied independently to each position in the
sequence.

Mathematically:

FFN(x) = max(0, xW1 + b1 )W2 + b2

​ ​ ​ ​

This transformation helps refine the encoded representation at each

position.

3. Attention Mechanism

The attention mechanism allows transformers to determine which

words in a sentence are most relevant to each other. This is done using
a scaled dot-product attention approach:

1. Each word in a sequence is mapped to three vectors:

Query (Q)
Key (K)
Value (V)

2. Attention scores are computed as: Attention(Q, K, V ) =

softmax ( QKdk ) V
T
​

​ ​

3. These scores determine how much attention each word should pay to
others.
Multi-Head Attention
Instead of using a single attention mechanism transformers apply
multi-head attention where multiple attention layers run in parallel.
This enables the model to capture different types of relationships
within the input.

4. Encoder-Decoder Architecture

The encoder-decoder structure is key to transformer models. The

encoder processes the input sequence into a vector, while the decoder
converts this vector back into a sequence. Each encoder and decoder
layer includes self-attention and feed-forward layers. In the decoder,
an encoder-decoder attention layer is added to focus on relevant parts
of the input.

For example, a French sentence "Je suis étudiant" is translated

into "I am a student" in English.

The encoder consists of multiple layers (typically 6 layers). Each layer

has two main components:

Self-Attention Mechanism – Helps the model understand word

relationships.
Feed-Forward Neural Network – Further transforms the
representation.

The decoder also consists of 6 layers, but with an additional encoder-

decoder attention mechanism. This allows the decoder to focus on
relevant parts of the input sentence while generating output.
For instance in the sentence "The cat didn't chase the mouse, because
it was not hungry", the word 'it' refers to 'cat'. The self-attention
mechanism helps the model correctly associate 'it' with 'cat' ensuring an
accurate understanding of sentence structure.

Applications of Transformers
Some of the applications of transformers are:

1. NLP Tasks: Transformers are used for machine translation, text

summarization, named entity recognition and sentiment analysis.
2. Speech Recognition: They process audio signals to convert speech
into transcribed text.
3. Computer Vision: Transformers are applied to image classification,
object detection, and image generation.
4. Recommendation Systems: They provide personalized
recommendations based on user preferences.
5. Text and Music Generation: Transformers are used for generating
text (e.g., articles) and composing music.

Transformers have redefined deep learning across NLP, computer vision,

and beyond. With advancements like BERT, GPT and Vision
Transformers (ViTs) they continue to push the boundaries of AI and
language understanding and multimodal learning.
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