Which architectures (CNN, RNN, Transformer) suit our problem best?
Deep learning will do to software what the steam engine did to manufacturing.
How It Works:
Match model types to data: CNNs for spatial data (images), RNNs/LSTMs for sequential data (time series, speech), and Transformers for long-range dependencies in text or multi-modal tasks.
Key Benefits:
- Optimized performance: Each architecture plays to its strengths.
- Efficient training: Pretrained backbones accelerate development.
- Future-proof: Transformer-based models dominate current benchmarks.
Real-World Use Cases:
- Video analysis: 3D-CNNs extract spatiotemporal features.
- Document understanding: Transformers for question-answering over PDFs.
FAQs
Can I ensemble architectures?
How select hyperparameters?
.png)
.svg)
