· Deep Learning · 2 min read
📋 Prerequisites
- Basic understanding of neural networks
🎯 What You'll Learn
- Understand what transformers are
- Learn why transformers replaced RNNs and LSTMs
- Grasp the key components of transformer architecture
- Gain a clear intuition for transformers in NLP and beyond
Introduction
Transformers are one of the most important architectures in modern deep learning, powering models like BERT, GPT, and T5 in natural language processing (NLP) and vision transformers in computer vision.
1️⃣ Why Were Transformers Created?
Before transformers, RNNs and LSTMs were used for sequence tasks, but they:
✅ Process data sequentially, making training slow.
✅ Struggle with long-range dependencies.
Transformers were introduced to:
✅ Enable parallel processing of sequences.
✅ Better capture long-range dependencies using attention mechanisms.
2️⃣ What Are Transformers?
Transformers are neural networks that rely entirely on attention mechanisms, removing the need for recurrence. They process sequences efficiently and scale well with large datasets.
Introduced in the paper “Attention Is All You Need” by Vaswani et al. (2017).
3️⃣ Key Components of Transformers
✅ Self-Attention: Allows each token in the input to attend to every other token, creating context-aware representations.
✅ Multi-Head Attention: Multiple attention heads capture different aspects of relationships in data simultaneously.
✅ Feed-Forward Layers: Applied to each position separately for additional transformation.
✅ Layer Normalization: Stabilizes and accelerates training.
✅ Residual Connections: Help gradients flow during backpropagation, making training deep networks easier.
4️⃣ Encoder-Decoder Structure
Transformers typically have:
- Encoder: Processes the input sequence.
- Decoder: Generates output sequences, using encoder outputs for context.
For tasks like translation, the encoder processes the input sentence, and the decoder generates the translated output.
5️⃣ Why Transformers Matter
✅ Allow fast, parallel training on large datasets.
✅ Capture long-range relationships effectively.
✅ Achieve state-of-the-art results in NLP, vision, and more.
6️⃣ Practical Applications
✅ Language Modeling (GPT)
✅ Text Classification
✅ Translation (T5, MarianMT)
✅ Vision Transformers (ViT) for image tasks
Conclusion
Transformers are a revolutionary architecture in deep learning that:
✅ Enable efficient and scalable sequence processing.
✅ Replace older RNN-based approaches for most NLP tasks.
✅ Power many modern AI applications across domains.
What’s Next?
✅ Explore attention mechanisms in detail to understand how transformers work internally.
✅ Try fine-tuning a pretrained transformer for a text classification task.
✅ Continue your structured deep learning journey on superml.org.
Join the SuperML Community to learn transformers practically and share your projects.
Happy Learning! ✨