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, revealed 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 right for machine studying, and why he dislikes the time period “neural community.” They focus on the necessity for deep studying, its historical past, the way it gives match for a lot of of immediately’s complicated issues, the place it shines and when to not use it. Moriarity goes into depth on a spread of matters, together with the right way to get datasets in form, supervised and unsupervised studying, feed-forward neural networks, Nx.serving, determination bushes, gradient descent, linear regression, logistic regression, help vector machines, and random forests. The episode considers what a mannequin appears to be like like, what coaching is, labeling, classification, regression duties, {hardware} assets wanted, EXGBoost, Jax, PyIgnite, and Explorer. Lastly, they have 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’ll be able to safely again all of it up and feed in new knowledge.
This episode sponsored by Miro.
