A Quick Comparison Of The Two High-Level Frameworks For Deep Learning

fastai

1. Built on the top of PyTorch
2. Emphasis on:

a) Rapid initial results for beginners — SOTA results with a handful of lines of code.

b) Straightforward customisation as your skills increase

c) Smooth integration with PyTorch

3. Backbone of popular deep learning courses

Keras

1. Since TensorFlow 2.0, integrated with TensorFlow
2. Default platform for learning about deep learning
3. Taught in dozens of courses and used by thousands of developers

fastai : pros and cons

Pro :

a) Complete solution in 5 lines

b) Intelligent defaults & clever built-in optimizations = SOTA / close to SOTA results “out of the box”

c) Layered architecture = room to grow from begineer to expert

d) Huge suite of convenience functions, e.g. for data preparation

e) Big set of curated datasets

Cons :

a) Smaller community than Keras

b) Limited examples of implementations in commercial production

c) Inconsistent documentation

Keras : pros and cons

Pro :

a) Huge user community

b ) Many examples of deployments in commercial production

c) Consistent, orderly evolution

d) Good documentation

Cons:

a) Harder to get started

b) More knobs to manage

c) Step function between Keras and TF in terms of complexity