Machine Learning essentials: Best practices, categories and misconceptions - JAXenter

Several choices can be considered with some geared towards a research-focused approach whilst others are built with an aim for use in industrial applications. Much like choosing a programming language, the best candidate often depends on what it will be used for. For example, while using MATLAB or Octave may be easier to experiment with Machine Learning and Deep Learning architectures, they are poorly suited for deploying to production. Ultimately, the choice of language will hinge on the problem that needs to be solved. The most popular language used in ML is Python, a lot of examples you will see in tutorials will be Python, and several popular deep learning libraries primarily support Python (Google's TensorFlow, Keras, Theano) and a large number of Data Scientists seem to favor Python. Moreover, this year's IEEE Spectrum ranking of top programming has Python at the top spot with C and Java following closely.