Hi Pythonistas!,
Last week we learned about RNNs.RNNs taught us a lot about sequences.
But they had a big problem: they’re slow and struggle with long-range dependencies.
Enter the Transformer - a model that turned everything upside down.
Why Transformers?
RNNs process sequences step by step → slow.
Transformers:
Process the entire sequence at once Use a mechanism called attention to focus on important parts anywhere in the input
This means no more waiting around for previous steps.
What Is Attention?
Imagine reading a book and instantly remembering the important parts related to the current sentence.
Attention does the same:
It looks at all words in a sentence Decides which ones matter most to the current word Weighs their influence when making decisions
Self-Attention -The Heart of Transformers
In self-attention, the model relates each word to every other word in the sequence.
Example:
In "The cat sat on the mat"
When processing "sat", the model pays attention to "cat" and "mat" to understand context.
This allows the model to capture long-distance relationships easily.
How Transformers Work: High Level
- Input is converted into vectors (embeddings)
- Self-attention layers compute relationships between all tokens
- Feed-forward dense layers process these relationships
- Multiple layers are stacked
- The output can be used for tasks like translation, text generation, or classification
Why Transformers Rock
- Handle long sequences efficiently
- Parallelizable → faster training on GPUs/TPUs
- Capture complex relationships without recurrence
- State-of-the-art results in NLP, vision, and more
Real-World Impact
Transformers power:
GPT series
BERT, T5
Image Transformers for vision tasks
Multimodal models combining text and images
What I Learned This Week
Transformers replaced sequential RNN processing with attention
Self-attention connects every word to every other word
Enables fast, parallel, and deep understanding of sequences
Revolutionized NLP and beyond
Transformers aren’t just a model they’re a paradigm shift in how machines understand data.
What’s Coming Next
Next week we will learn about autoencoders