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Everything you need to know about the deep learning model

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Machine learning is zooming ahead, bringing new models each year. One neural network architecture is particularly useful for natural language processing tasks and allows computers to understand and interact with people.


Initially introduced in the “Attention Is All You Need” article, Transformers represent one of the latest and most powerful models developed. This is the same model OpenAI uses for prediction, summarization, question answering, and more. This article explores the architecture of Transformer models and how they work.


First, an introduction to neural networks

To fully grasp the concept of Transformer models, you must understand the basics of neural networks. Drawing inspiration from the intricacies of the human brain, neural networks form the cornerstone of deep learning technology.

Neurons in the human brain are interconnected to process information through electrical signals, and artificial neural networks are similar. They consist of software-based nodes that mimic these neurons. These nodes use computational power to perform intricate mathematical operations. Neural networks lay the groundwork for advanced machine learning models like Transformers.

The input layer is the first step in neural network data analysis

The input layer is the entry point where the artificial neural network receives information from the world. The input layer processes, analyzes, or categorizes the incoming data. After this preliminary processing, the data is forwarded to the subsequent layer for further analysis.

In facial recognition systems, like those in your camera, the input layer of the neural network processes pixel data from image or video frames. For instance, when a person stands in front of a camera, the camera captures their image and sends it to the input layer of the neural network.

The hidden layers are the heart of neural network processing

The hidden layers are the core of the network’s processing power. These layers are positioned between the input and output layers. The number of hidden layers in an artificial neural network varies and depends on the task’s complexity and the data’s nature.

Each hidden layer receives its input either from the input layer or preceding hidden layers. The primary function of these layers is to analyze and process the data received from the previous layer before transmitting it to the next.

These layers extract features like edges, contours, and facial elements (eyes, nose, mouth). Each hidden layer progressively learns more complex features. The first detects edges, subsequent layers identify shapes, and deeper layers recognize intricate facial features.

The output layer delivers the neural network’s final results

The output layer is the network’s concluding stage. It delivers the outcome of the data processing conducted by the neural network. The configuration of this layer can vary depending on the task. For binary classification problems, it might consist of a single node producing a binary outcome (1 or 0).

Conversely, for multi-class classification challenges, the output layer could contain multiple nodes, each corresponding to a different class or category in the classification task.

The output layer classifies individuals using recognized facial features. Trained to identify specific people, it contains nodes for each person, activating the relevant node when a match is detected.

Source: “Machine learning methods for wind turbine condition monitoring: A review” by Stetco et al.

How Transformer models vary from conventional models

What sets Transformer models apart is their move away from the traditional frameworks used in neural network designs. Despite their effectiveness, conventional architectures have inherent limitations in capturing long-range dependencies. This concept is particularly relevant in sequential data like time series, natural language, or music.

Imagine a sentence: “I grew up in France. Many years later, I still remember how to speak fluent French.” In this sentence, to correctly predict or understand the word “French” at the end, the model needs to remember the context provided at the beginning of the sentence (“I grew up in France”). This gap between the relevant input (“France”) and the point where it’s needed to make a decision (“French”) is called a long-term dependency.

Transformers revolutionize this landscape by adopting parallel processing of input lines. This approach makes them efficient in the training and inference phases. Transformers can handle entire sequences at once, which reduces training time.

This efficiency is an intrinsic advantage of their design, making them a robust choice for natural language processing and complex sequence modeling tasks. This capability expands the functionality of texting applications, where they can be seamlessly integrated to enhance the user experience.

How does Transformer architecture work?

This section explores the key components of the Transformer architecture, including input embedding, positional encoding, encoder and decoder layers, and the model training and inference processes. You’ll learn how Transformers interpret with high accuracy.

Input embedding

Understanding the input is the first step in how Transformer models work. The input embedding phase converts the data element into a numerical vector in a process known as vector embedding. These embeddings capture the semantic essence of the elements, which lets the models work with number patterns. This way, the models can understand and process the data better.

Imagine asking your phone’s virtual assistant a question like, “What’s the weather like today?” Your voice input is transformed into text. This text is broken down into words or phrases, which Transformer models must understand. Each word or phrase from your query is converted into a numerical vector in the input embedding phase.

Positional encoding

Next, the Transformer model gets to know the order. By their design, Transformer models don’t inherently understand the order in which elements, like words in a sentence, appear. This presents a challenge, especially in tasks like language processing, where the sequence of words is critical to meaning. Transformers use positional encoding to compensate for this challenge.

These tags inform the model of each word’s position in the sequence. When the model integrates this information with the embeddings, it better understands the sequence’s structure. This process allows Transformers to comprehend the nuanced relationships between words in a sentence, such as which words are subjects, objects, or else.

Since the order of words in your query is crucial (the meaning of “What’s the weather like today?” is different from “Today, what’s the weather like?”), Transformer models use positional encoding. This step adds information to each word’s vector, indicating its position in the sentence.

Encoder layers

After the encoding, the Transformer model takes the input that was turned into number patterns and tagged with their order and sends it through several Encoder layers to understand it better. The encoder is a multi-layered structure, each layer being a complex assembly of two pivotal components:

  1. The self-attention mechanism is like a filter that assesses the input sequence (like words in a sentence) and calculates attention scores. These scores determine how much focus should be given to each part of the input in relation to the rest, allowing the model to understand and prioritize input elements based on their relevance and relationship.
  2. Following this, the feed-forward neural network takes over. It processes the outcomes of the self-attention mechanism and applies a non-linear transformation.

Non-linear transformation is a mathematical process that allows the model to capture even more complex relationships and patterns in the data. It helps grasp the context, the tone, the implied meanings, and the overall sense of the input. For example, the same word can have different meanings depending on the context. The FFNN in Transformer models plays a key role in capturing and interpreting these subtleties.

The self-attention mechanism in the encoder layers evaluates each word in your question, focusing on keywords like “weather” and “today.” The feed-forward neural network processes this, understanding the context (you’re asking about today’s weather, not yesterday’s).

Decoder layers

The output is passed into the layers of decoders. This component is like a bridge that connects the decoder with the context processed by the encoder. The decoder is also a multi-layered structure:

  1. The encoder-decoder attention mechanism allows the decoder to access and integrate contextual information from the entire input sequence that the encoder previously processed.
  2. Meanwhile, the self-attention mechanism within the decoder looks at each word (or element) in the output sequence and computes attention scores.

The decoder constructs the output sequence from an informed perspective, considering the output’s intricacies and the input sequence’s broader context. This dual attention system within the decoder allows the Transformer model to generate coherent, contextually rich, and accurate translations or responses.

After your question is encoded, the decoder layers generate a relevant draft response. It uses the context provided by the encoder (today’s weather) and constructs a reply. The encoder-decoder attention mechanism pulls specific details (like your current location and time) to personalize the response. The self-attention mechanism in the decoder focuses on forming a coherent and contextually appropriate sentence.

Output

After processing the input through its encoder and decoder, the model reaches the output projection stage with a preliminary output for the next word in the sequence. At this stage, two key processes occur:

  1. The linear projection refines the raw output from the decoder, organizing it into a structured format.
  2. The softmax function steps in, acting as a decision-maker. It evaluates the probability of each candidate word being the correct continuation of the sentence. The word with the highest probability is selected as the next word in the sequence.

The final step involves choosing the exact words for the response. The linear projection organizes the decoder’s output into a structured sentence. The softmax function selects the most probable words to complete the sentence, ensuring the response is fluent and accurate. The virtual assistant then says, “The weather in your area today is sunny with a high of 75 degrees,” providing you with the requested information.

Optimizing Transformer models for accurate predictions

Transformer models use a supervised learning method, comparing their predictions with known correct outputs. Optimization algorithms adjust the models’ parameters during training and optimization to improve accuracy if predictions don’t match the targets.

This process is iterative and involves working through batches of training data, allowing the model to gradually refine its accuracy by learning from its mistakes and successes. This ensures that the Transformer model becomes more adept at tasks like language translation or text generation as it processes more data and adjusts its parameters for better performance.

While typing on a smartphone keyboard with predictive text powered by a Transformer model, the model learns to forecast the next word based on training with large text datasets. If its predictions deviate from the correct sequences in the training data, optimization algorithms fine-tune its parameters for greater accuracy.

Imagine using a voice recognition system that transcribes speech to text, powered by a Transformer model. This model was trained on extensive audio recordings and their corresponding text transcripts. As you speak, the model converts your words into text in real time. If you say, “It looks like it’s going to rain,” but the system transcribes it as “It looks like it’s going to reign,” the discrepancy is noted.

The Transformer model uses optimization algorithms to adjust its parameters, improving its ability to differentiate between similar-sounding words like “rain” and “reign” in future transcriptions. This ongoing refinement process helps the model become more precise in understanding and transcribing spoken language.

Using trained models for new data inference

After training, the model can be used for inference on new data. During inference, the input sequence is passed through the pre-trained model, which applies the same techniques used during training to process this new input.

Having been previously trained on vast amounts of voice data, the model is now adept at understanding and processing new voice inputs. It recognizes the words, understands the question, and responds appropriately.

A diagram of the Transformer model's architecture showing the flow from inputs to output probabilities.

Source: “Attention Is All You Need” by Vaswani et al.

Transformer models and their impact on our lives

This overview clarifies key concepts in Transformer models for beginners. If you are interested, visit our articles on how an LLM works. Machine learning is experiencing an extraordinary era characterized by boundless possibilities. These advancements pave the way for innovative applications and enhanced AI features like AI-powered keyboards that do real-time translation and grammar checks.

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