|Date:||January 31, 2016|
- Support for commonly used models including convnets, RNNs, LSTMs, and autoencoders. You can find many pre-trained implementations of these in our model zoo
- Tight integration with our state-of-the-art GPU kernel library
- 3s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 32 hrs)
- Basic automatic differentiation support
- Framework for visualization
- Swappable hardware backends: write code once and deploy on CPUs, GPUs, or Nervana hardware
New features in this release:
- Kepler GPU support
- New data loader and serialization formats
- Greatly expanded model zoo now featuring deep residual nets for image classification, fast-RCNN for object localization, C3D video action recognition.
- and many more.