Understanding Attention and the Transformer Architecture

Calculating Attention Step-by-Step

We will now walk through how a Transformer calculates attention using the Query (Q), Key (K), and Value (V) vectors for words. Don’t worry, we will keep the math simple and explain each part in plain English. Here’s the core formula for scaled dot-product attention (the heart of the Transformer’s attention mechanism):

\[ \text{Attention}(Q, K, V) = \text{softmax}\!\left(\frac{Q K^T}{\sqrt{d_k}}\right) V \tag{1} \]

This formula might look complex, so we will break it into pieces. It’s doing a few things: dot products (\( QK^T \)), a scaling by \( d_k \), a softmax, and then using those weights on \( V \). Let’s explain each part in a simple way:

Dot Product for Similarity

First, the model compares the Query with each Key by taking a dot product. A dot product between two vectors is just a way to measure how similar or aligned those two vectors are. You compute it by multiplying corresponding components and adding them up:

\[ \mathbf{a} \cdot \mathbf{b} = a_1b_1 + a_2b_2 + \cdots + a_nb_n \]

For example, if \( \mathbf{a} = [1, 2, 3] \) and \( \mathbf{b} = [4, 5, 6] \), then:

\[ \mathbf{a} \cdot \mathbf{b} = 1 \cdot 4 + 2 \cdot 5 + 3 \cdot 6 = 4 + 10 + 18 = 32 \]

In our context, if \( q_i \) is the Query for word \( i \) and \( k_j \) is the Key for word \( j \), the dot product \( q_i \cdot k_j \) gives a score that represents how much word \( i \) should pay attention to word \( j \). A larger dot product means the Query and Key are more similar, which implies word \( j \) is more relevant to word \( i \).

In our earlier example, the Query for "it" would have a high dot product with the Key for "animal" (because they are related) and a low dot product with the Key for "street" (less related).

Scaling (Divide by \( \sqrt{d_k} \))

Why do we divide by \( \sqrt{d_k} \)? Here, \( d_k \) is the dimension (length) of the Key and Query vectors. If these vectors are long, their dot product can be a big number just because of many components—not necessarily because the vectors are very similar.

Dividing by the square root of the vector length keeps these scores from growing too large and keeps them in a nice range. It's a bit like averaging the score or normalizing it.

The original Transformer paper introduced this scaling factor to prevent very large dot products, which would make the next step (softmax) produce extremely peaked (or small gradient) outputs—leading to training issues.

In plain terms, without scaling, if you had big vectors, one score might overshadow others too much. Scaling fixes that.

Softmax to Get Weights

Next, we apply a softmax function to these scaled scores. The softmax turns the scores into probabilities (or weights) that sum up to 1. It also makes higher scores stand out more by exponentiating them.

The formula for softmax for a set of scores \( \{z_1, z_2, \ldots, z_n\} \) is:

\[ \text{softmax}(z_i) = \frac{e^{z_i}}{\sum_{j=1}^{n} e^{z_j}} \]

What this does is take each score \( z_i \), exponentiate it (so it becomes positive), and divide by the sum of all exponentials. After this, each \( \text{softmax}(z_i) \) is between 0 and 1, and all of them add up to 1 (like percentages). A high original score will become a large fraction, and a low score becomes a tiny fraction.

In our attention case, suppose the (scaled) dot product scores for “it” with keys {animal, street, tired} were something like {5, 1, 3}. After softmax, these might become weights like {0.70, 0.02, 0.28}. That means the word “it” will pay 70% of its attention to “animal”, 28% to “tired”, and basically ignore “street” with 2%.

Softmax essentially highlights the biggest scores and suppresses the smaller ones, while ensuring the weights are easier to work with (summing to 1).

Use Weights to Combine Values

Finally, those softmax results are used to create a weighted sum of the Value vectors. This means we take each Value vector (which represents the content/information of each word) and multiply it by the attention weight. Then we add them all up. If we denote by \( \alpha_{ij} \) the softmax weight that word \( i \) gives to word \( j \), and \( v_j \) is the Value of word \( j \), then the output for word \( i \) after attention is:

\[ \text{Output}_i = \sum_j \alpha_{ij} v_j. \]

If word \( i \) (say “it”) heavily attends to word \( k \) (“animal”), then \( \alpha_{ik} \) will be large (close to 1) for “animal” and small for others, so the output will be mostly \( v_k \) (the value from “animal”). Intuitively, word \( i \) is gathering information from word \( k \).

In our example, “it” would get a vector that is a mix of mainly “animal”’s value and some of “tired”’s value (and almost none of others). This means the new representation of “it” after the attention layer contains knowledge that “it” is related to an animal that is tired. The model has effectively linked “it” to the right context. To put it another way, attention allows the model to inject context from relevant words into the current word’s representation. Unimportant words get multiplied by near-zero, so they don’t contribute much (they’re “drowned out”), while important words keep their values intact.

\( QK^T \) computes all pairwise dot products between Query vectors and Key vectors, producing a matrix of scores (often called the attention scores or compatibility scores of each query-word with each key-word).

\( \frac{1}{\sqrt{d_k}} \) is just scaling those scores down a bit (this is a simple number division, not changing the relationships, just the magnitude).

\( \text{softmax}(\ldots) \) turns each row of scores into weights between 0 and 1 that add to 1. Each row corresponds to one Query (one word that’s “paying attention”), and the weights in that row tell how strongly it attends to each other word.

Finally, multiplying by \( V \) applies those weights to the Value vectors, giving a weighted combination. If we do this for every word’s Query, we get an output for every word (often noted as an output matrix \( Z \), where each row is the attended result for a word).

Example with Numbers

To cement this understanding, let’s consider a simple example with numbers. Suppose we have a tiny sentence with just two words: “Thinking Machines”. We want to compute the attention for the first word “Thinking” (just as a toy example). Imagine the model has produced the following (made-up) vectors:

• Query for “Thinking” = \( q_1 \)
• Key for “Thinking” = \( k_1 \), Key for “Machines” = \( k_2 \)
• Value for “Thinking” = \( v_1 \), Value for “Machines” = \( v_2 \)

First, we take dot products: \( q_1 \cdot k_1 \) might be, say, 112, and \( q_1 \cdot k_2 = 96 \) (these are just example numbers). This means “Thinking” is somewhat more related to itself (112) than to “Machines” (96).

Next, assume \( d_k = 64 \) (just an example dimension), so \( \sqrt{d_k} = 8 \). We divide the scores by 8: they become 14 and 12.

Now apply softmax: if we exponentiate 14 and 12 and normalize, we get something like weights 0.88 for “Thinking” itself and 0.12 for “Machines”.

So “Thinking” pays 88% attention to itself and 12% to “Machines” in this step. Finally, we multiply the Value vectors by these weights and add:

\[ \text{output} = 0.88 \cdot v_1 + 0.12 \cdot v_2 \]

The resulting vector (let’s call it \( z_1 \)) is the updated representation of “Thinking” after attending to the other word.

This process happens for each word in parallel (so “Machines” would do the same with its own Query, and likely focus mostly on itself or maybe a bit on “Thinking”). In a real sentence with many words, each word’s Query attends to all the words (including itself) via these dot product scores.

Multi-Head Attention: Multiple Perspectives

The Transformer doesn’t just do one attention calculation; it does several in parallel, which are called multiple heads. The idea of Multi-Head Attention is actually straightforward: we run the above attention process \( h \) times (where \( h \) could be 8 or 12 or so), each time with a different set of Query, Key, Value projections. Each such run is called an “attention head.” In the original Transformer, they used 8 heads. But why do we want multiple heads?

Think of multi-head attention like having a team of readers instead of just one, where each reader focuses on the text from a different perspective:

• One head might focus on syntactic relations (like which word is the subject of the sentence).
• Another head might focus on coreference or pronouns (like linking “it” to “animal” as we saw).
• Yet another head might focus on the next word (like ensuring the phrase order makes sense).
• Essentially, each head can learn to pay attention to different types of patterns or relationships.

In the example “The animal didn’t cross the street because it was too tired”, when the word “it” is being processed, one attention head might strongly focus on “the animal”, while another head might focus on the word “tired”. That means one head is capturing the link it → animal (to understand what “it” is), and another head is capturing it → tired (to understand the state or property of “it”). When you combine these, the word “it” ends up with information that it’s an animal and that this animal is tired. Each head contributed a piece of understanding.

How do we implement multiple heads? The model actually creates different sets of Q, K, V vectors for each head by using different learned weight matrices. So, head 1 will have its own \( W_1^Q, W_1^K, W_1^V \) to produce \( Q_1, K_1, V_1 \); head 2 will have \( W_2^Q, W_2^K, W_2^V \), and so on.

Each head does attention as described before, producing its own output (let’s call them \( Z_1, Z_2, \ldots, Z_h \) for \( h \) heads).

Now we have \( h \) different representations for each word, each emphasizing different aspects of the context. To merge them, the Transformer concatenates these \( h \) outputs into one long vector and then passes it through one more linear layer (with weights \( W^O \)) to mix them into a single vector of the original size.

This final vector is what gets passed on to the next layer (or to the next part of the model). The process of combining heads is designed so that the overall operation can still be learned end-to-end (the model learns how to weight and use each head’s information via \( W^O \)).

In simpler terms, multi-head attention is just doing “attention” several times in different ways, and then combining the results. This helps the model capture different kinds of relationships simultaneously with the input words.

With a single head, the model averages all those considerations into one set of weights, which might make it miss some subtler connections. Multiple heads let some attention patterns happen in one head and different patterns in another.

The concept of multi-head attention might sound complex, but remember, it’s essentially just parallel attention computations. Each head is like an expert focusing on one aspect. When the model puts it all together, it’s as if it consulted multiple experts and combined their opinions. This makes the final understanding richer. The Transformer paper famously said that “Multi-head attention allows the model to jointly attend to information from different representation subspaces” – meaning each head might capture a different kind of similarity or relation in the data.

To summarize multi-head attention:

• We have several sets of Q, K, V (one per head), so each head looks at the sentence with different “eyes”.
• Each head produces its own attention output.
• We concatenate all these outputs and mix them to form the final output for that layer.
• This way, at the same position (say the word “it”), the model can encode multiple kinds of information (who “it” refers to, what properties “it” has, etc., each from a different head).

Now that we understand attention and multi-head attention, let’s see how these fit into the overall Transformer architecture, including how a Transformer processes an entire sentence and produces an output (for example, translating a sentence or answering a question).

\[ \begin{aligned} PE(\text{pos}, 2i) &= \sin\!\Big(\frac{\text{pos}}{10000^{\,2i/d_{\text{model}}}}\Big), \\ PE(\text{pos}, 2i+1) &= \cos\!\Big(\frac{\text{pos}}{10000^{\,2i/d_{\text{model}}}}\Big), \end{aligned} \] where \(\text{pos}\) is the position index (starting from 0 for the first word, 1 for second, etc.), \(i\) is the index of the component in the positional vector, and \(d_{\text{model}}\) is the total dimension of these vectors (which is the same as the word embedding dimension). This looks complicated, but you don’t need to memorize it. Here’s what to take away in simple terms:

• The positional encoding is a bunch of sine and cosine waves at different frequencies. For each position (word index in the sentence), the encoding gives a unique combination of values.
• Words that are close in position will have similar positional encodings, and those far apart will have very different encodings. This helps the model infer distance between words.
• The reason for the sine/cosine pattern is that it provides a smooth way for the model to learn relative positions – for example, the model can combine a position 5 encoding and position 6 encoding in a way to sense that 6 is just one ahead of 5, etc., because of properties of these functions.
• In practice, these \(PE\) vectors are just added to the initial word embeddings at the bottom of the model, so that each word embedding is slightly changed based on its position in the sentence. After this, the attentions and other computations can make use of the positional differences. Essentially, by the time the model is computing attention, the position info is baked into the Query/Key/Value vectors (since those come from the embeddings).

An analogy: Think of each word as a book in a stack. Without positional encoding, it’s like the books are scattered on the floor — the model can see all the content but doesn’t know the order. Positional encoding is like numbering the books or placing them on a numbered shelf in order. Now the model knows which book comes first, second, and so on, without changing the content of the books themselves. The numbering scheme (sine/cosine pattern) might seem odd, but it ensures each position has a unique code and that the model can learn the notion of “nearby” vs “far” positions through these codes. In summary, positional encoding gives Transformers a sense of sequence order, which is crucial for language understanding. Some modern Transformers instead use learned positional embeddings (where the model learns a vector for each position index) – but the original used this fixed sinusoidal method. Either way, the concept is the same: provide the model with information about word positions.

The Transformer Architecture: Encoder and Decoder

Now that we have all the pieces (self-attention, multi-head, positional encoding, etc.), let’s see how a full Transformer model is structured. The original Transformer was designed for sequence-to-sequence tasks like translating from one language to another, so it has two main parts: an Encoder and a Decoder.

• Encoder: The encoder’s job is to read and understand the input sentence (for example, an English sentence to be translated). It takes the sequence of words (with positional encodings added) and passes them through a series of layers to produce a set of encoded representations (one for each word/token in the input). These representations can be thought of as a transformed version of the input sentence that now captures the meaning and relationships of the words in a way that’s useful for the decoder.
• Decoder: The decoder’s job is to generate the output sentence, one word at a time (for example, the translated French sentence), using the encoder’s outputs as needed for context. The decoder also has to take into account what it has generated so far, to keep the output coherent.

Let’s break down what happens in a Transformer step by step using a translation example (English to French) for intuition:

Encoder process (e.g., reading an English sentence):

1. Input embedding + position: Each word in the input is converted to a vector (embedding), and positional encoding is added to include word order. So if the input is “I love NLP”, each word “I”, “love”, “NLP” becomes an embedding, then we add positional encoding for position 0,1,2 respectively.
2. Self-Attention layer: The first encoder layer lets each word attend to every other word in the input sentence (self-attention as we described). So each word’s representation is updated to include context from other words. For example, in the encoder, the word “love” might pay attention to “I” to understand the subject, etc.
3. Feed-Forward layer: After attention, there’s a small feed-forward network (a couple of linear layers with a ReLU activation, for instance) that further processes each word’s representation independently. Think of this as a way to further transform/refine the representation that came out of the attention layer. For example, after attention, “love” has info from “I” and “NLP”; the feed-forward layer might help to emphasize certain features or combine that information in a nonlinear way.
4. Residual & Norm: The Transformer uses residual connections (skipping connections) around the attention and feed-forward sublayers, and applies layer normalization at each step. Residual connection means the input to a sublayer is added to its output – this helps the model keep the original information and makes training easier (it’s like saying “this layer’s output = original input + some change”). Layer normalization stabilizes the values flowing through the network, which helps training converge (prevents extreme changes).
5. Stacking layers: The encoder has multiple layers (the original had 6). Each layer does the attention + feed-forward (with the residual connections around them). As you go up the layers, the representations become more abstract and enriched. After the final encoder layer, we have the encoder’s output: a set of vectors (one per input word) that encode the meaning of the entire sentence in context.

Decoder process (e.g., producing a French sentence):

1. Masked Self-Attention: At the bottom of the decoder, suppose we start generating the output. At the very beginning, we have no output words yet, so we feed a start-of-sentence token (like "<start>"). The decoder first applies self-attention among the words generated so far. This is similar to encoder’s self-attention but with a twist: it’s masked so that the decoder can’t see future output words (because when generating, you shouldn’t know the next word ahead of time). This mask is usually implemented by giving effectively negative infinite scores to any illegal positions in the attention softmax, so each position can only attend to earlier positions (the words that are already generated) and itself. For the first word, it attends just to itself (nothing else generated yet).
2. Encoder-Decoder Attention: Next, in the decoder layer, there is another attention sublayer where the Query comes from the decoder (current word representation), and the Keys and Values come from the encoder outputs. This is often just called “encoder-decoder attention.” This mechanism allows the decoder to look at the entire input sentence (via the encoder outputs) to gather relevant information for generating the next word. For example, if the decoder is about to produce the French word “rouge” (for “red”), this attention will allow it to focus on the word “red” in the English sentence by attending to the encoder’s representation of “red”. Essentially, at each step the decoder queries the encoder outputs to ask “What did the input say about this part I’m generating?”.
3. Feed-Forward layer: Similar to the encoder, each decoder layer also has a feed-forward network to process each position’s data after the attention steps.
4. Residuals & Norms: The decoder layers also use residual connections around each sublayer (self-attention, enc-dec attention, feed-forward) and layer normalization to keep things stable.
5. Output generation: The decoder produces an output word probability at each step (usually through a final linear layer and softmax over the vocabulary). For generation, it picks the highest probability word (or uses beam search, etc., but that’s beyond scope) and that word becomes the next input to the decoder (since it generates word by word). This repeats until an end-of-sentence token is produced.

To illustrate, suppose we are translating “I love NLP” to French:

• Encoder reads “I love NLP” and outputs encoded vectors.
• Decoder starts: at first step, it has “<start>”. Self-attention doesn’t do much with one token. Then encoder-decoder attention lets it look at the encoder outputs: perhaps it attends mostly to “I” and “love” because usually a French sentence might start with the subject. It predicts the first French word “J’” (short for “Je”, meaning “I”).
• Next step, decoder input is “J’”. Masked self-attention now considers “J’” (only one token so far). Encoder-decoder attention might focus on “love” this time, and it outputs “aime” (meaning “love”). Now we have “J’aime”.
• Next, decoder input is “J’aime”. Self-attends to both (but masked ensures it doesn’t look ahead to nothing). Encoder-decoder attention might focus on “NLP” now. It produces “le NLP” or something appropriate (just hypothetically).
• This continues until the full translation is generated.

The key takeaway is: The encoder provides a full picture of the input, and the decoder produces the output stepwise, attending to the input via the encoder-decoder attention at each step. This design allows the output to be deeply connected to the input at every stage, which is why Transformers do so well in translation and similar tasks. In the case of tasks that don’t require a separate encoder and decoder (like text classification or filling a blank in a sentence), sometimes only the encoder part is used (this is what BERT does). For tasks like text generation (like GPT), sometimes only the decoder part is used (this is what GPT does). But the original architecture had both.

Let’s summarize the components we’ve covered in the context of the whole model:

• Embedding Layer: Converts words (or tokens) into vectors; add positional encoding so the model knows the order.
• Encoder Layers: Each has Multi-Head Self-Attention + Feed-Forward, with residual connections around both and layer norm. The encoder produces a sequence of encoded outputs (same length as input, but now in a contextualized form).
• Decoder Layers: Each has Masked Multi-Head Self-Attention (looking at output so far) + Multi-Head Encoder-Decoder Attention (looking at encoder’s output) + Feed-Forward, all with residual connections and norms. The decoder outputs one token at a time (through final linear+softmax) using the context gathered.
• Final Linear & Softmax (in decoder): To turn the decoder’s last layer output into probabilities for the next word in the vocabulary.

Other Important Components: Residual Connections and Layer Normalization

We mentioned these in passing, but to avoid confusion, let’s clearly state what residual connections and layer normalization do, as they are crucial engineering details in Transformers:

• Residual Connections: Sometimes called skip connections, these are additions where the input of a layer is added to the output of that layer (usually before applying an activation or normalization). In the Transformer, residual connections are used around the self-attention sublayer and around the feed-forward sublayer. This means the original input to that sublayer is added to the result of the sublayer. Why do this? It helps the flow of gradients during training (so the model can learn easier) and it prevents the network from unlearning the identity function. In simpler terms, residuals give the model a way to “fallback” to the input if a layer doesn’t need to change it too much. It also helps to keep the original information intact as it moves through layers, ensuring that important parts aren’t lost after many transformations.
• Layer Normalization: This is a technique to stabilize and speed up training. After each sublayer (attention or feed-forward), the Transformer applies layer normalization, which normalizes the values across the features for each data point (each position’s vector). It ensures that the distribution of the values (the components of the vectors) remains stable as they pass through layers, which helps avoid extreme values that could make training unstable. In plain terms, it keeps numbers in a reasonable range, sort of like making sure each word’s representation has a consistent scale/variance throughout the network. You can think of it as a way to standardize the data at each step, which often leads to better and faster learning.

These components don’t directly contribute to the model’s ability to “understand” language, but they greatly help in training the model effectively and making sure information flows correctly through the network. They are like the scaffolding that holds the architecture together firmly.

Putting It All Together

Let’s recap how all these parts come together when a Transformer processes language:

• We start with an input sequence of words. Each word is turned into an embedding vector, and a positional encoding is added to each to encode the word’s position.
• Those go through a stack of encoder layers. Each encoder layer uses self-attention (multiple heads) so that each word gathers information from all other words, then a feed-forward network further processes each word. Residual connections help preserve the original info and layer norm keeps things smooth. By the top encoder layer, each word’s vector represents that word in the context of the entire sentence.
• The decoder, at each time step, does its own self-attention on the output generated so far (with masking so it doesn’t cheat by looking ahead), and also does attention over the encoder’s outputs. So the decoder is constantly cross-referencing the input sentence (via encoder outputs) while generating the translation or answer or summary, one token at a time.
• Finally, the decoder produces output tokens. If it’s a language model (like GPT), it might do this indefinitely (or until a stop condition) to generate text. If it’s translation, it stops when it produces an end-of-sentence token. If it’s some other task like summarization, similar stopping criteria apply.

Because of the attention mechanism:

• The encoder can capture long-range dependencies in the input. Even if two words are far apart, the self-attention can directly connect them if relevant.
• The decoder can always look at the entire input through the encoder-decoder attention, which means it doesn’t have to compress the source into one vector (unlike older seq2seq models) – it has access to everything on the fly.
• Within the decoder’s own self-attention, when writing a sentence, the model can also consider relationships between previously generated words (though usually this is mostly about language modeling/fluency).

Transformers thus achieve excellent results and are highly efficient because they allow parallel processing of sequences (the encoder processes all words at once with matrix operations, not one by one like an RNN). The main computation (attention) can be done with matrix multiplications which are optimized on modern hardware. The trade-off is that the attention mechanism is quadratic in time with respect to sequence length (because it compares every word with every other word). In practice, this is manageable up to certain lengths and has been one reason for the success of large-scale models.

Real-World Examples: Transformers in Action (GPT and BERT)

To solidify our understanding, let’s briefly look at two famous families of models based on the Transformer: GPT and BERT. These are just specific ways to use the Transformer components we described, tailored for different goals.

• BERT (Bidirectional Encoder Representations from Transformers): As the name suggests, BERT uses only the encoder part of the Transformer architecture. It’s called “bidirectional” because it learns from all words at once (both left and right context), essentially using the self-attention from the encoder to build very rich representations of text. BERT is trained on a task of masking some words in a sentence and trying to predict them (among other objectives), which forces it to understand the context deeply. Because BERT doesn’t have a decoder generating new text, it’s not used for language generation. Instead, it’s fantastic at understanding or classifying text. For example, you can feed a paragraph into BERT and a question, and it can highlight the answer in the paragraph – this is question answering. Or you can give it a sentence with a blank and it will fill it. BERT was so successful that Google started using BERT to help understand search queries in 2019, improving search results. In our terms, BERT’s architecture is basically a stack of Transformer encoder layers (no decoder). After processing, you typically take the top layer representations for whatever task you need (maybe feed them to a classifier, etc.). BERT showed that Transformer encoders can be incredibly good at capturing the meaning of text.
• GPT (Generative Pre-trained Transformer): GPT uses only the decoder part of the Transformer (in fact, the initial GPT model was essentially the Transformer decoder stack, including masked self-attention). GPT is designed to generate text. It’s trained by simply predicting the next word in lots of sentences (that’s why it’s Generative and Pre-trained on a huge corpus). During training, GPT’s self-attention is always masked so it can’t see future words, only past ones, which makes it a left-to-right language model. Because of this, GPT can take a prompt and continue it, writing whole paragraphs that sound coherent. ChatGPT, for instance, is based on a variant of GPT (GPT-3 and beyond) and is essentially a very large decoder-only Transformer model. These models handle tasks like writing an essay, summarizing text, or holding a conversation by generating one word at a time very quickly. The GPT series demonstrated that with enough data and a large Transformer (with many layers and heads), the model can learn a surprising amount of knowledge and linguistic competence. Starting in 2018, the GPT models set state-of-the-art results in generating human-like text, and their improvement led to the widespread use of AI text generation we see today.

To put it succinctly: BERT is an encoder-only Transformer (great at understanding), and GPT is a decoder-only Transformer (great at generating). Both share the same fundamental building blocks we’ve discussed. In fact, GPT’s decoder uses masked self-attention and typically also has the ability to attend to some input if provided (like the conversation history or a prompt), but if not, it’s basically just attending to its own output. Other notable Transformer-based models include Transformer-XL, XLNet, T5, RoBERTa, etc. – all of which tweak the basic architecture or how they’re trained, but the essence of queries, keys, values, multi-head attention, and the encoder/decoder structure remains. Transformers are also used outside of pure text now – for example, Vision Transformers (ViT) treat an image as a sequence of patches and apply a Transformer encoder to classify images, and there are Transformers for audio and multimodal (text+image) tasks as well.

Conclusion

We’ve taken a deep concept – the Transformer and its attention mechanism – and broken it down into digestible pieces. Let’s quickly recap in very simple terms:

• Attention: A way for the model to figure out which words to focus on. Implemented by comparing vectors (dot products) and weighing important words more (softmax weights).
• Self-Attention: The model attends to other words in the same sentence. This helps link words like “she” to “Alice” automatically. Every word gets information from every other word.
• Scaled Dot-Product: The specific formula (dot product + divide by sqrt(d) + softmax) that gives the attention weights. We broke down why each part is there.
• Multi-Head Attention: Running multiple attention processes in parallel so that the model can capture different types of relationships at once. We combine these to get a more robust understanding.
• Positional Encoding: Since attention doesn’t inherently know word order, we add a positional signal to each word’s embedding so that order information is available.
• Encoder: A stack of self-attention layers that encodes an input sequence into context-rich vectors.
• Decoder: A stack that uses masked self-attention (for outputs so far) and attends to the encoder’s output to generate an output sequence.
• Residuals/Norms: Technical additions that help train deep networks (they keep information flowing and stable).
• Transformer vs Others: No recurrence, no convolution – just attention mechanisms repeated. This allows much more parallelization and has proven extremely effective.

With these concepts, you have a high-level understanding of how models like GPT-3 or BERT (and many others) work internally. You can imagine the attention weight tables being computed, multiple heads focusing on different words, and the encoder-decoder dance in translation. Modern large language models are essentially very large Transformers with some bells and whistles, but fundamentally, they’re doing what we described: using attention to decide which words matter, and using that to produce understanding or generate text.

Transformers are a complex topic, but breaking it down reveals a lot of repetitive simple operations. Hopefully, this explanation made the process clearer. With this foundation, you can further explore topics like how Transformers are trained, how they handle very long documents (since attention can be expensive), or newer variations like TransformerXL, Reformer, or Efficient Transformers that try to improve on the basic design. But those are next steps – for now, you have the big picture of attention mechanisms and Transformer architecture in a beginner-friendly nutshell.