Heaton J. Applications of Deep Neural Networks

ArXiv, 2021. — 569 p.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.Python Preliminaries.
Python for Machine Learning.
Introduction to TensorFlow.
Training for Tabular Data.
Regularization and Dropout.
Convolutional Neural Networks (CNN) for Computer Vision.
Generative Adversarial Networks.
Kaggle Data Sets.
Transfer Learning.
Time Series in Keras.
Natural Language Processing and Speech Recognition.
Reinforcement Learning.
Advanced/Other Topics.
Other Neural Network Techniques.True PDF