Training Machine Learning Models
Working with data is often the most time-consuming aspect of machine learning, overshadowing the excitement of model training and deployment. The model itself is defined in code, while parameters are treated as data. Creating a training pipeline involves implementing a training loop that applies gradient descent, updating model parameters iteratively and tracking metrics, though some metrics may not always be useful.In this clip
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SE Radio 594: Sean Moriarity on Deep Learning with Elixir and Axon
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