Hands-On Transfer Learning with Python

Why different architectures are needed

A feedforward multilayered neural network has the capacity to learn a huge hypothesis space and extract complex features in each of the nonlinear hidden layers. So, why do we need different architectures? Let's try to understand this.

Feature engineering is one of the most important aspects in machine learning (ML). With too few or irrelevant features, we may have underfitting; and with too many features, we may be overfitting the data. Creating a good, hand-crafted set of features is a tedious, time-consuming, and iterative task.

Deep learning comes with a promise that, given enough data, the deep learning model is capable of automatically figuring out the right set of features—that is, a hierarchy ...