Identifying the number of hidden units in a fully connected layer is considered a heuristically-guided craft. A PyTorch library, Delve, was developed that allows identifying the degree of over-parameterization of a layer, thus guiding architecture selection. The library compares the intrinsic dimensionality of the layer over training, providing the user with live feedback during training.

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Further reading

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Deep Learning A-Z™: Hands-On Artificial Neural Networks

☞ A Beginners Guide for Building Neural Networks in Tensorflow

☞ Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

☞ Create a Simple Neural Network in Python from Scratch

☞ Deep Learning With TensorFlow 2.0

☞ Deep Learning vs. Conventional Machine Learning

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