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. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.
Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
Spinning objects have strange instabilities known as The Dzhanibekov Effect or Tennis Racket Theorem - this video offers an intuitive explanation. Part of th...
The center of the technology world may not lie in California's Silicon Valley, but in the bustling marketplace of Huaqiangbei, a subdistrict of Shenzhen...
The wave of standing together for Mauna Kea swept across the ocean, all the way from Hawai'i to Europe. We dance as one. We stand as one. Mahalo a nui loa to...
I. Orsolic, L. Skorin-Kapov, and T. Hoßfeld. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019), (June 2019)
M. Seufert, N. Wehner, P. Casas, and F. Wamser. 14th International Conference on Network and Service Management (CNSM), Best Paper Award, Rome, Italy, (November 2018)