#ICYMI An Overview of Python Deep Learning Frameworks
  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.

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