Deep Learning with PyTorch Quick Start Guide

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Most of the time you will spend on a deep learning project will be spent working with data and one of the main reasons that a deep learning project will fail is because of bad, or poorly understood data. This issue is often overlooked when we are working with well-known and well-constructed datasets. The focus here is on learning the models. The algorithms that make deep learning models work are complex enough themselves without this complexity being compounded by something that is only partially known, such as an unfamiliar dataset. Real-world data is noisy, incomplete, and error prone. These axes of confoundedness mean that if a deep learning algorithm is not giving sensible results, after errors of logic in the code are eliminated, ...