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Sean Moriarity, creator of the Axon deep learning framework, co-creator of the Nx library, and author of\xa0Machine Learning in Elixir\xa0and\xa0Genetic Algorithms in Elixir,\xa0published by the Pragmatic Bookshelf, speaks with SE Radio host\xa0Gavin 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 \u201cneural network.\u201d They discuss the need for deep learning, its history, how it offers a good fit for many of today\u2019s 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\u2019s 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.