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Sean Moriarity, creator of the Axon deep studying framework, co-creator of the Nx library, and writer of Machine Studying in Elixir and Genetic Algorithms in Elixir, printed by the Pragmatic Bookshelf, speaks with SE Radio host Gavin Henry about what deep studying (neural networks) means immediately. Utilizing a sensible instance with deep studying for fraud detection, they discover what Axon is and why it was created. Moriarity describes why the Beam is good for machine studying, and why he dislikes the time period “neural community.” They talk about the necessity for deep studying, its historical past, the way it presents a great match for a lot of of immediately’s advanced issues, the place it shines and when to not use it. Moriarity goes into depth on a variety of subjects, together with how one can get datasets in form, supervised and unsupervised studying, feed-forward neural networks, Nx.serving, choice timber, gradient descent, linear regression, logistic regression, assist vector machines, and random forests. The episode considers what a mannequin seems to be like, what coaching is, labeling, classification, regression duties, {hardware} sources wanted, EXGBoost, Jax, PyIgnite, and Explorer. Lastly, they take a look at what’s concerned within the ongoing lifecycle or operational facet of Axon as soon as a workflow is put into manufacturing, so you possibly can safely again all of it up and feed in new knowledge.
This episode sponsored by Miro.
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