2020/09/11 by Jeff Heaton, Heaton, Jeff · 2 voices · 9 citations
Computer Science · #Artificial intelligence #Artificial neural network #Computational Physics and Python Applications #Computer science #Convolutional neural network #Deep learning #Machine learning #Programming language #Python (programming language) #Recurrent neural network #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2009.05673
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:1610.02357, arXiv:1603.05027, arXiv:1801.04381, arXiv:2001.02394, arXiv:1704.04861 by other authors
openalex publication_date 2020/09/11 · arxiv created 2022/05/17 · arxiv updated 2022/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.