Transformers is a revolutionary AI architecture introduced in 2017 by Vaswani et al. It transformed (hence the name) the field of Natural Language Processing (NLP) and beyond.
*Key Features:*
1.Self-Attention Mechanism: Allows models to focus on relationships between different parts of input data.
- 2. Non-Sequential Processing: Processes input data in parallel, improving efficiency.
- 3. Encoder-Decoder Structure: Suitable for sequence-to-sequence tasks.
- 4. Positional Encoding: Preserves sequential information.
*How Transformers Work:*
1.Input Embeddings: Convert input data into numerical representations.
2. Encoder: Processes input sequence through self-attention and feed-forward neural networks.
3.Decoder: Generates output sequence, using Encoder’s output and self-attention.
4. Output Linear Layer: Produces final output.
*Transformer Variants:*
1.BERT (Bidirectional Encoder Representations from Transformers)
2. RoBERTa (Robustly optimized BERT approach)
3. XLNet
4. Transformer-XL
5. Reformer
*Applications:*
- Language Translation
- 2. Text Classification
- 3. Sentiment Analysis
- 4. Question Answering
- 5. Text Generation
- 6. Image Captioning
- 7. Speech Recognition
*Advantages:*
1.Parallelization
2. Efficient handling of long-range dependencies
3. Improved performance
4. Scalability
*Limitations:*
1.Computational complexity
2.Memory requirements
3. Training challenges
*Real-World Impact:*
- Google’s BERT-based search algorithm
- 2. AI-powered chatbots
- 3. Automated language translation
- 4. Sentiment analysis tools
Transformers have revolutionized AI, enabling more efficient and accurate processing of sequential data.
