On Friday, Jeremy Howard’s fast.ai announced the release of super productive libraries along with a very handy machine learning book and also a course. Fast.ai is popular deep learning that provides high-level components to obtain state-of-the-art results in standard deep learning domains. Fast.ai allows practitioners to experiment, mix and match to discover new approaches. In short, to facilitate hassle-free deep learning solutions.
The libraries leverage the dynamism of the underlying Python language and the flexibility of the PyTorch library.
Now the latest version, ‘fastai v2’ is a complete rewrite of fastai which is faster, easier, and more flexible, implementing new approaches to deep learning framework design.
Overview Of The New Releases
Fast.ai makes it very easy to migrate from plain PyTorch, Ignite, or any other PyTorch-based library, or even to use fastai in conjunction with other libraries. Users can use fastai’s GPU-accelerated computer vision library, along with your own training loop. They can also pick and choose with mixup and cutout augmentation, a uniquely flexible GAN training framework, which isn’t available in any other framework.
#developers corner #deep learning library #fastai #frameworks deep learning
