Transformer: Exploring Their Architecture and Diverse Applications

In the ever-evolving landscape of artificial intelligence, the Transformer has emerged as a revolutionary force, rewriting the narrative on sequence modeling. Introduced in 2017 through the seminal paper “Attention is All You Need” by Vaswani et al., the Transformer disrupted the dominance of LSTM and RNN architectures. Its innovative self-attention mechanisms introduced a parallelized approach, allowing for more efficient computation and improved capture of long-range dependencies.

Why did the Transformer take center stage when LSTM and RNNs were prevalent? The answer lies in its ability to process sequences simultaneously, alleviating the sequential bottlenecks of its predecessors. This breakthrough not only transformed natural language processing but also extended its applications across diverse domains within artificial intelligence.

Reference: RNNs

As we explore the Transformer’s journey in this blog series, we will delve into its unique training mechanisms, intricate architecture, and versatile applications. From computer vision to speech recognition, the Transformer’s impact has been profound, marking a new era in intelligent computation. Join us on this transformative exploration into the heart of the Transformer and its enduring influence on the field of artificial intelligence.


Self-attention, also known as intra-attention or internal attention, is a mechanism in neural networks that allows the model to focus on different positions of the input sequence when making predictions for a particular position.

For each position i in the input sequence, the input embeddings are linearly transformed into three vectors: Query (Qi), Key (Ki), and value (Vi). These transformations are represented by learned weight matrices: WQ, WK, and WV.

Attention scores are calculated by taking the dot product of the Query (Qi) and Key (K) matrices, scaled by the square root of the dimension of key(dk). The SoftMax operation normalizes the attention scores, and the weighted sum of value(V) vectors is computed.

The weighted sum is the output for the position is

Outputi = Attention (Qi, K, V)

The key idea is that each position in the sequence attends to all positions, and the attention mechanism determines the importance or relevance of each position for the current position. This allows the model to capture dependencies and relationships between different elements in the sequence.

Multi Head Attention

Multi-head attention is an extension of the self-attention mechanism used in the Transformer architecture. It allows the model to attend to different positions in the input sequence simultaneously, through multiple sets of attention weights. The primary motivation behind multi-head attention is to enable the model to capture various types of relationships and patterns in the data.

Parallelization: The input sequence is transformed into multiple sets of Query(Q). Key(K), and Value(V) matrices, each with its own set of learned weight matrices (WQ, WK, WV). For h attention heads, h sets of these weight matrices are created.

The self-attention mechanism is applied independently to each set of Query, Key, and Value matrices. This results in h sets of attention-weighted outputs. The outputs from all the attention heads are concatenated along the feature dimension.

The concatenated outputs are linearly transformed by a weight matrix(W0) to produce the final output of the multi-head attention mechanism.

MultiHead (Q, K, V) = Concat (Head1, Head2, Head3, …………, Headh) W0

The final output is used as input to subsequent layers of the model. The introduction of multiple attention heads allows the model to capture different aspects of the input sequence in parallel. Each attention head focuses on different relationships and patterns, enabling the model to learn more expressive and nuanced representations.

Benefits of Multi-Head Attention:

Increased Capacity: Multi-head attention increases the model’s capacity to capture complex relationships in the data.

Diversity of Attention: Different attention heads can attend to different parts of the sequence, providing a more comprehensive understanding.

Improved Generalization: The model can learn to weigh the importance of different relationships independently, leading to improved generalization.

In summary, multi-head attention enhances the expressive power of the self-attention mechanism by allowing the model to jointly consider multiple perspectives on the input sequence.


The Transformer consists of an encoder-decoder structure, each composed of multiple identical layers. The key components of the architecture are as follows:


Input Embedding: Convert the input sequence (a series of words or tokens) into continuous vector representations, known as embeddings. Adds positional encodings to these embeddings to convey the sequential order.

Reference: Embeddings

Self-Attention Mechanism: Transform the embedded sequence into Query (Q), Key (K), and Value (V) matrices. Computes attention scores using the dot product of Query and Key, scale it, apply softmax to obtain attention weights. Calculate a weighted sum of the Value vectors to produce attention output. This step allows the model to focus on different parts of the input sequence.

Linear Transformation and Residual Connection: Apply a linear transformation to the output of the self-attention mechanism. Add the original input (residual connection).Normalize the result using layer normalization.

Feedforward Neural Network (FNN): Pass the output through a feedforward neural network. Typically, this network consists of two linear transformations with a ReLU activation in between. Again, apply layer normalization and add the residual connection. The output of the feedforward network is the encoder’s output for that layer.


Input Embedding: Similar to the encoder, convert the input sequence into embeddings and add positional encodings.

Self-Attention Mechanism (Masked): Apply self-attention as in the encoder but with a mask to prevent attending to future positions. This ensures the model attends only to previous positions during training.

Encoder-Decoder Attention Mechanism: Attend to the encoder’s output using the decoder’s current input. Similar to self-attention but with Query from the decoder and Key, Value from the encoder.

Linear Transformation and Residual Connection (Both Self-Attention and Encoder-Decoder Attention): Apply linear transformation and add a residual connection for both attention mechanisms.

Normalize the results using layer normalization. Feedforward Neural Network (FNN), Similar to the encoder, pass the output through a feedforward neural network with normalization and a residual connection.

Output: The final output of the decoder is the result of the feedforward network.

During training, the models’ predictions are compared to the actual target sequence using a loss function, often cross_entrophy loss on the predicted probabilities and the actual target sequence. Gradients loss with respect to the model parameters are computed through back propagation.

Reference: Mathematical introduction to Transformer


While the Transformer architecture gained significant popularity and success in natural language processing (NLP) tasks, it has transcended its initial application and found success in various domains. Originally designed for sequence-to-sequence tasks like machine translation, the inherent characteristics of the Transformer make it versatile and applicable to a broader range of problems. Here are some areas beyond NLP where Transformers have proven effective:

1. Computer Vision:

Transformers have been successfully applied to computer vision tasks, such as image classification, object detection, and image generation. Vision Transformer (ViT) is an adaptation of the Transformer architecture for image-related tasks.

2. Speech Processing:

Transformers have been employed in speech processing tasks, including automatic speech recognition (ASR) and speaker recognition. They excel in capturing long-range dependencies in sequential data, making them suitable for analyzing speech signals.

3. Time Series Analysis:

Transformers are effective in handling time series data, such as financial market trends or sensor data. The ability to capture dependencies across varying time steps makes them well-suited for these applications.

4. Graph-Based Tasks:

Transformer models have been adapted for graph-based tasks, including graph classification and node classification. Graph Transformer Networks (GTNs) leverage the self-attention mechanism to process graph-structured data.

5. Recommender Systems:

Transformers have demonstrated success in recommender systems, where the model needs to understand complex user-item interactions over time.

6. Scientific Applications:

In scientific domains, Transformers have been applied to tasks like protein structure prediction and drug discovery, showcasing their adaptability to diverse and complex data.

The flexibility and parallelization capabilities of the Transformer architecture contribute to its broad applicability. Researchers and practitioners continue to explore and adapt the Transformer model to address challenges across various domains, extending its impact beyond its initial breakthrough in NLP.

Reference: Attention is all you need.


Dive deeper into the world of neural networks and enhance your expertise by enrolling in a comprehensive deep learning course. Uncover the intricacies of advanced models like RNNs, LSTMs, and GRUs, gaining a profound understanding of their applications in natural language processing and beyond.