Machine Learning In Just 5 Lines Of Code: Fast.ai New Release

Fast.ai makes it easy to migrate from PyTorch, Ignite, or any other PyTorch-based library, or use fastai in conjunction with other libraries. 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.

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

The existing frameworks are programming language-specific. This problem is overcome by MXNet framework and it provides one system for different programming flavor. As the popularity and need for deep learning networks increase, there has been a lot of effort to build tools that ease the development of deep learning models. One such tool that we will discuss today is MXNet. You might be wondering what makes MXNet better than the already existing deep learning frameworks like Theano or Caffe.

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Deep learning is a branch of machine learning, in essence, its the implementation of neural networks with more than a single hidden layer of neurons.

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