Transformers In Machine Learning Geeksforgeeks

Leo Migdal

-Nov 18, 2025, 4:13 AM

transformers in machine learning geeksforgeeks

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. Transformer Architecture uses self-attention to transform one whole sentence into a single sentence. This is useful because 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. 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. In deep learning, the transformer is an artificial neural network architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a... Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM).[2] Later variations have been widely adopted for training... The modern version of the transformer was proposed in the 2017 paper "Attention Is All You Need" by researchers at Google.[1] The predecessors of transformers were developed as an improvement over previous architectures for... They are used in large-scale natural language processing, computer vision (vision transformers), reinforcement learning,[6][7] audio,[8] multimodal learning, robotics,[9] and even playing chess.[10] It has also led to the development of pre-trained systems, such as... For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs).

A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable... A key breakthrough was LSTM (1995),[note 1] an RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units.[13] Neural networks using multiplicative units were later called sigma-pi networks[14] or... However, LSTM still used sequential processing, like most other RNNs.[note 2] Specifically, RNNs operate one token at a time from first to last; they cannot operate in parallel over all tokens in a sequence. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 14, 2025

The Transformer architecture has revolutionized the field of Machine Learning, particularly in Natural Language Processing (NLP) and Computer Vision. Introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017[^1], Transformers have become a staple in many state-of-the-art models. In this section, we will provide an overview of the Transformer architecture, its key components, and compare it with traditional recurrent neural networks. The Transformer architecture is primarily designed for sequence-to-sequence tasks. It consists of an encoder and a decoder, both of which are composed of multiple identical layers.

The encoder takes a sequence of tokens (e.g., words or characters) as input and outputs a sequence of vectors. The decoder then generates the output sequence, one token at a time, based on the output vectors from the encoder. The Transformer architecture can be visualized as follows: The Transformer architecture relies heavily on two key components: self-attention mechanisms and feed-forward networks. Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They do this by learning context and tracking relationships between sequence components.

For example, consider this input sequence: "What is the color of the sky?" The transformer model uses an internal mathematical representation that identifies the relevancy and relationship between the words color, sky, and blue. It uses that knowledge to generate the output: "The sky is blue." Organizations use transformer models for all types of sequence conversions, from speech recognition to machine translation and protein sequence analysis. Early deep learning models that focused extensively on natural language processing (NLP) tasks aimed at getting computers to understand and respond to natural human language. They guessed the next word in a sequence based on the previous word. To understand better, consider the autocomplete feature in your smartphone.

It makes suggestions based on the frequency of word pairs that you type. For example, if you frequently type "I am fine," your phone autosuggests fine after you type am. Early machine learning (ML) models applied similar technology on a broader scale. They mapped the relationship frequency between different word pairs or word groups in their training data set and tried to guess the next word. However, early technology couldn’t retain context beyond a certain input length. For example, an early ML model couldn’t generate a meaningful paragraph because it couldn’t retain context between the first and last sentence in a paragraph.

To generate an output such as "I am from Italy. I like horse riding. I speak Italian.", the model needs to remember the connection between Italy and Italian, which early neural networks just couldn’t do. Transformers are a type of deep learning model that utilizes self-attention mechanisms to process and generate sequences of data efficiently. They capture long-range dependencies and contextual relationships making them highly effective for tasks like language modeling, machine translation and text generation. Transformer model is built on encoder-decoder architecture where both the encoder and decoder are composed of a series of layers that utilize self-attention mechanisms and feed-forward neural networks.

This architecture enables the model to process input data in parallel making it highly efficient and effective for tasks involving sequential data. The encoder and decoder work together to transform the input into the desired output such as translating a sentence from one language to another or generating a response to a query. The primary function of the encoder is to create a high-dimensional representation of the input sequence that the decoder can use to generate the output. Encoder consists of multiple layers and each layer is composed of two main sub-layers: Layer normalization and residual connections are used around each of these sub-layers to ensure stability and improve convergence during training. Decoder in transformer also consists of multiple identical layers.

Its primary function is to generate the output sequence based on the representations provided by the encoder and the previously generated tokens of the output. Transformer is a neural network architecture introduced in the 2017 paper "Attention is All You Need" by Vaswani et al. It uses self-attention to process entire sentences at once, a major shift from older models like RNNs and LSTMs. Transformers have been widely adopted for various machine learning tasks, particularly in NLP. This article explores the architecture, working, and applications of transformers. Discover how transformers are revolutionizing machine learning tasks beyond NLP.For more details, check out the full article: Transformers in Machine Learning.

Transformers In Machine Learning Geeksforgeeks

People Also Search

Transformer Is A Neural Network Architecture Used For Performing Machine

Traditional Models Like RNNs (Recurrent Neural Networks) Suffer From The

This Sequential Processing Means LSTMs Can't Analyze An Entire Sentence

A Well-cited Early Example Was The Elman Network (1990). In

The Transformer Architecture Has Revolutionized The Field Of Machine Learning,