Sean Moriarity, creator of the Axon deep
learning framework, co-creator of the Nx library, and author
of Machine Learning in Elixir and Genetic
Algorithms in Elixir, published by the Pragmatic Bookshelf,
speaks with SE Radio host Gavin Henry about what deep
learning (neural networks) means today. Using a practical example
with deep learning for fraud detection, they explore what Axon is
and why it was created. Moriarity describes why the Beam is ideal
for machine learning, and why he dislikes the term "neural
network." They discuss the need for deep learning, its history,
how it offers a good fit for many of today's complex problems,
where it shines and when not to use it. Moriarity goes into depth
on a range of topics, including how to get datasets in shape,
supervised and unsupervised learning, feed-forward neural
networks, Nx.serving, decision trees, gradient descent, linear
regression, logistic regression, support vector machines, and
random forests. The episode considers what a model looks like,
what training is, labeling, classification, regression tasks,
hardware resources needed, EXGBoost, Jax, PyIgnite, and Explorer.
Finally, they look at what's involved in the ongoing lifecycle or
operational side of Axon once a workflow is put into production,
so you can safely back it all up and feed in new data. Brought to
you by IEEE Computer Society and IEEE Software magazine. This
episode sponsored by Miro.
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