Deep Learning Architectures for Defense and Security

This 3-day course provides a broad introduction to classical neural networks (NN) and its current evolution to deep learning (DL) technology. This course introduces the well-known deep learning architectures and their applications in defense and security for object detection, identification, verification, action recognition, scene understanding and biometrics using a single modality or multimodality sensor information. This course will describe the history of neural networks and its progress to current deep learning technology. It covers several DL architectures such the classical multi-layer feed forward neural networks, convolutional neural networks (CNN), generative adversarial networks (GAN), restricted Boltzmann machines (RBM), auto-encoders and recurrent neural networks such as long term short memory (LSTM). Use of deep learning architectures for feature extraction and classification will be described and demonstrated. Examples of popular CNN-based architectures such as AlexNet, VGGNet, GooGleNet (inception modules), ResNet, DeepFace, Highway Networks, FractalNet and their applications to defense and security will be discussed. Advanced architectures such as Siamese deep networks, coupled neural networks, conditional adversarial generative networks , fusion of multiple CNNs and their applications to object verification and classification will also be covered. The course is for scientists, engineers, technicians, or managers who wish to learn more about deep learning architectures and their applications in defense and security. Dr. Nasser M. Nasrabadi is a professor in the Lane Computer Science and Electrical Engineering Department at West Virginia University. He was senior research scientist (ST) at US Army Research Laboratory (ARL). He is actively engaged in research in deep learning, image processing, automatic target recognition and hyperspectral imaging for defense and security. He has published over 300 papers in journals and conference proceedings. He has been an associate editor for the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions for Neural Networks. He is a Fellow of IEEE and SPIE. REGISTRATION: There is no obligation or payment required to enter the Registration for an actively scheduled course. We understand that you may need approvals but please register as early as possible or contact us so we know of your interest in this course offering. SCHEDULING: If this course is not on the current schedule of open enrollment courses and you are interested in attending this or another course as an open enrollment, please contact us at (410)956-8805 or [email protected]. Please indicate the course name, number of students who wish to participate. and a preferred time frame. ATI typically schedules open enrollment courses with a 3-5 month lead-time. To express your interest in an open enrollment course not on our current schedule, please email us at [email protected]. For on-site pricing, you can use the request an on-site quote form, call us at (410)956-8805, or email us at [email protected]. We use cookies to improve your experience on our site. By using our site, you consent to cookies. Manage your cookie preferences below: Essential cookies enable basic functions and are necessary for the proper function of the website. These cookies are needed for adding comments on this website. These cookies are used for managing login functionality on this website. Statistics cookies collect information anonymously. This information helps us understand how visitors use our website. Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions. Service URL: policies.google.com (opens in a new window) Vimeo is a video hosting platform for high-quality content, ideal for creators and businesses to showcase their work. Service URL: vimeo.com (opens in a new window) You can find more information in our Cookie Policy and .