Features
deep-learningneural-networksgpuresearch
Pros and Cons
Ventajas
- + Dynamic computational graphs (eager execution)
- + Intuitive debugging like native Python
- + Preferred in research and academia
- + Excellent GPU/CUDA support
- + Rich ecosystem (torchvision, torchaudio)
Desventajas
- - More complex deployment than TensorFlow
- - Fewer production tools
- - Higher memory consumption than alternatives
Use Cases
- Deep learning research
- Computer vision
- Natural language processing
- Generative networks (GANs)
- Reinforcement learning