Many deep learning frameworks are available to accomplish the same task in slightly different ways. Below, we summarize some key points from the most popular frameworks.
Tensorflow is the most widely used deep learning framework. It is developed by Google. Tensorflow is highly documented and Google offers extensive tensorflow-based machine learning training lessons and exercises for beginners. Tensorflow has its own visualization tool, tensorboard.
Tensorflow is not as fast as some other frameworks and has a reputation for being very complicated for beginners.
Pytorch is the second-most popular framework, after tensorflow. Pytorch is the python based variety of a deep learning framework called Torch and is developed by Facebook. Pytorch is known for being fast and efficient and is popular with researchers. It is more intuitive for new users than tensorflow.
Pytorch documentation is limited, and it does not have any inbuilt visualization tools (e.g. tensorboard)
Keras is a simple, high level library developed primarily by a Google engineer named François Chollet. It works on top of frameworks like tensorflow and makes developing simple neural networks much easier for beginners.
Because Keras is meant to abstract the details of more in-depth libraries, it offers less control in building models.
MXNet is an effective framework, highly scalable framework used by AWS and other industry giants.
Documentation is more limited than other languages, which makes it a poorer choice for beginners.
Other options include Caffe (or Caffe2), Microsoft Cognitive Toolkit, and Deeplearning4j. All of these options are high performing but less well documented.
Most of the canonical choices of machine learning framework have comparable performance. Differences in speed and efficiency do not seem marked enough between the frameworks to strongly prefer one or another on that basis.
Instead, because our lab is not experienced in machine learning, we think it would be best to choose a well documented language with an active support community. We have explored Google’s tensorflow-based machine learning crash course for an introduction to the main concepts of machine learning and an overview of tensorflow use. Using keras with tensorflow may be a good choice for our collective skill level, with the option to transition to regular tensorflow for finer control as we become more experienced.