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5 de jul. de 2023 · One of the essential components of deep learning is the choice of the loss function and performance metrics used to train and evaluate models. This paper reviews the most prevalent loss...
5 de jul. de 2023 · Loss Functions and Metrics in Deep Learning. One of the essential components of deep learning is the choice of the loss function and performance metrics used to train and evaluate models. This paper reviews the most prevalent loss functions and performance measurements in deep learning.
By Jason Brownlee on August 27, 2020 in Deep Learning 159. The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when ...
We start by detailing the main performance metrics for dif- ferent tasks (classi cation, regression), and how they may be interpreted, including in the face of class imbalance, varying prevalence, or asymmet- ric cost-bene t trade-o s. We then explain how to estimate these metrics in a unbiased manner using training, validation, and test sets.
Using metrics to assess the performance. In addition to a loss function, Keras lets us also use metrics to help judge the performance of a model. While minimizing loss is good, it's not especially obvious how we expect the model to perform given some loss function.
With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics. In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection.
12 Monitoring Metrics: Precision, Recall, and Pretty Pictures - Deep Learning with PyTorch. This chapter covers: Defining precision, recall, true/false positives/negatives, how they relate to one another, and what they mean in terms of our model’s performance.
