This textbook provides a compact but comprehensive treatment that
provides analytical and design steps to recurrent neural networks
from scratch. It provides a treatment of the general recurrent
neural networks with principled methods for training that render
the (generalized) backpropagation through time (BPTT). This author
focuses on the basics and nuances of recurrent neural networks,
providing technical and principled treatment of the subject, with a
view toward using coding and deep learning computational
frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural
networks are treated holistically from simple to gated
architectures, adopting the technical machinery of adaptive
non-convex optimization with dynamic constraints to leverage its
systematic power in organizing the learning and training processes.
This permits the flow of concepts and techniques that provide
grounded support for design and training choices. The author's
approach enables strategic co-training of output layers, using
supervised learning, and hidden layers, using unsupervised
learning, to generate more efficient internal representations and
accuracy performance. As a result, readers will be enabled to
create designs tailoring proficient procedures for recurrent neural
networks in their targeted applications.
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