Deep learning is a group of exciting new technologies for neural networks.
Through a combination of advanced training techniques and neural network
architectural components, it is now possible to create neural networks that can
handle tabular data, images, text, and audio as both input and output. Deep
learning allows a neural network to learn hierarchies of information in a way
that is like the function of the human brain. This course will introduce the
student to classic neural network structures, Convolution Neural Networks
(CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU),
General Adversarial Networks (GAN), and reinforcement learning. Application of
these architectures to computer vision, time series, security, natural language
processing (NLP), and data generation will be covered. High-Performance
Computing (HPC) aspects will demonstrate how deep learning can be leveraged
both on graphical processing units (GPUs), as well as grids. Focus is primarily
upon the application of deep learning to problems, with some introduction to
mathematical foundations. Readers will use the Python programming language to
implement deep learning using Google TensorFlow and Keras. It is not necessary
to know Python prior to this book; however, familiarity with at least one
programming language is assumed.