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SE Radio 594: Sean Moriarity on Deep Learning with Elixir and Axon

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Manage episode 389505492 series 215
Inhalt bereitgestellt von SE-Radio Team. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von SE-Radio Team oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

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.

  continue reading

992 Episoden

Artwork
iconTeilen
 
Manage episode 389505492 series 215
Inhalt bereitgestellt von SE-Radio Team. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von SE-Radio Team oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

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.

  continue reading

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