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.
Lectures of the Photogrammetry I & II courses for BSc students taught by Cyrill Stachniss at the University of Bonn, Germany in the summer and winter term 2015.
Lecture Recordings from my winter 2013/14 course on SLAM taught in Freiburg. Lecture material can be found here: http://ais.informatik.uni-freiburg.de/teachi...
View the complete course: http://ocw.mit.edu/RES-18-009F15 Instructor: Gilbert Strang, Cleve Moler Gilbert Strang and Cleve Moler provide an overview to thei...
This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: diff...