fastai is a modern, open source, deep learning library which is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable

that using a layered API in deep learning has very significant benefits for researchers, practitioners, and students. Researchers can see links across different areas more easily, rapidly combine and restructure ideas, and run experiments on top of strong baselines. Practitioners can quickly build prototypes, and then build on and optimize those prototypes by leveraging fastai’s PyTorch foundations, without rewriting code. Students can experiment with models and try out variations, without being overwhelmed by boilerplate code when first learning ideas.

a bewildering array of comparisons, useful as a literature review of sorts

The concept of non-linearity in a Neural Network is introduced by an activation function which serves an integral role in the training and performance evaluation of the network. Over the years of theoretical research, many activation functions have been proposed, however, only a few are widely used in mostly all applications which include ReLU (Rectified Linear Unit), TanH (Tan Hyperbolic), Sigmoid, Leaky ReLU and Swish. In this work, a novel neural activation function called as Mish is proposed. The experiments show that Mish tends to work better than both ReLU and Swish along with other
standard activation functions in many deep networks across challenging datasets. For instance, in Squeeze Excite Net- 18 for CIFAR 100 classification, the network with Mish had an increase in Top-1 test accuracy by 0.494% and
1.671% as compared to the same network with Swish and ReLU respectively. The similarity to Swish along with providing a boost in performance and its simplicity in implementation makes it easier for researchers and developers to
use Mish in their Neural Network Models.