Applications of Deep Neural Networks with Keras

Deep learning is a group of exciting new technologies for neural networks.Through a combination of advanced training techniques and neural networkarchitectural components, it is now possible to create neural networks that canhandle tabular data, images, text, and audio as both input and output. Deeplearning allows a neural network to learn hierarchies of information in a waythat is like the function of the human brain. This course will introduce thestudent 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 ofthese architectures to computer vision, time series, security, natural languageprocessing (NLP), and data generation will be covered. High-PerformanceComputing (HPC) aspects will demonstrate how deep learning can be leveragedboth on graphical processing units (GPUs), as well as grids. Focus is primarilyupon the application of deep learning to problems, with some introduction tomathematical foundations. Readers will use the Python programming language toimplement deep learning using Google TensorFlow and Keras. It is not necessaryto know Python prior to this book; however, familiarity with at least oneprogramming language is assumed.

Further reading#

• Access Paper in arXiv.org