Deep Learning With Applications
Room: Room 306, Bldg: Becton Building , FDU Metropolitan Campus, 960 River Road, Teaneck, New Jersey, United States, 07666Week of Events
Deep Learning With Applications
September 21 through November 2, 2024. Six Saturdays 1:30-4:30pm (9/21, 9/28, 10/5, 10/19, 10/26, 11/2). The IEEE North Jersey Section Communications Society Chapter is offering a course entitled "DEEP LEARNING WITH APPLICATIONS". Deep learning is a transformative field within artificial intelligence and machine learning that has revolutionized our ability to solve complex problems in various domains, including computer vision, natural language processing, and reinforcement learning. This hands-on course on deep learning is designed to provide students with an understanding how these amazing successes are made possible by drawing inspiration from the way that brains, both human and otherwise, operate. Students will gain a comprehensive foundation in the principles, techniques, and applications of deep neural networks. Learning how to solve real data-set based applications will teach students how to really apply deep learning with Python programming software. Participants will be asked to design and train deep neural networks to perform tasks such as image classification using commonly available data sets. However, participants are encouraged to apply the techniques from this course to other data sets according to their interests. Discuss with the instructor in order to propose your own project. More importantly, this will set the foundations for understanding and developing Generative AI applications. The IEEE North Jersey Section's Communications Society Chapter can arrange for providing IEEE CEUs - Continuing Education Units (for a $5 charge) upon completion of the course. Course prices: $75 for Undergrad/Grad/Life/ComSoc members, $100 for IEEE members, $150 for non-IEEE members Co-sponsored by: Education Committee Speaker(s): Thomas Long, Agenda: 1. Introduction to Neural Networks: Explore the fundamental concepts of artificial neural networks, backpropagation, activation functions, and gradient descent, laying the groundwork for deep learning understanding. 2. Introduction to PyTorch: Learn how to implement and train neural networks using PyTorch one of the most popular deep learning frameworks. Understand tensors. 3. Computer Vision Applications: Apply deep learning to computer vision problems, including image classification and object detection using Convolutional Neural Networks (CNNs) 4. Training and Optimizing Deep Neural Networks: Study techniques for training deep neural networks effectively, including optimization algorithms, weight initialization, regularization, and dropout. 5. Sequential Data Analysis: Explore how deep learning is used to analyze sequential data using Recurrent Neural Networks (RNNs). In particular, explore how neural networks are used in Natural Language Processing (NLP) tasks such as sentiment analysis and machine translation. 6. Generative AI: Overview of generative ai techniques that leverage the patterns present in a dataset to generate new content. Applications of generative ai include large language models such as ChatGPT and image generation models such as Midjourney and Stable Diffusion. This course assumes a basic understanding of machine learning concepts and programming skills in Python. Familiarity with linear algebra and calculus will be beneficial, but not mandatory. Statistical software (Python, Scikit-learn) and Deep Learning Frameworks (Pytorch, TensorFlow) will be used throughout the course for the exploration of different learning algorithms and for the creation of appropriate graphics for analysis. Learning objectives: Subjects covered include these and other deep learning related materials: artificial neural networks, training deep neural networks, RNN, CNN, image recognition, natural language processing, GANs, data processing techniques, and NN architectures. The course is intended to be subdivided into 3-hour sessions. Each lecture is further subdivided into lecture, guided and independent project based exercises to build experience with hands-on techniques. This course will be held at FDU - Teaneck, NJ campus. Checks should NOT be mailed to this address. Can bring checks in person or use online payments at registration. Email the organizer for any questions about course, registration, or other issues. Technical Requirements: Students will need access to the Python programming language. In addition to a standard Python installation, most programming exercises will use the package Scikit-learn. Basic programming skills and some familiarity with the Python language are assummed. Students are expected to be able to bring a laptop onto which most of these libraries can be pre-installed using python's pip install. Most of the coding in this course will use the Python programming language. Coding examples and labs will be distributed in the form of Juypter notebooks. In addition to standard Python, most programming exercises will use either the PyTorch or TensorFlow libraries. Books and other resources will be referenced. Room: Room 306, Bldg: Becton Building , FDU Metropolitan Campus, 960 River Road, Teaneck, New Jersey, United States, 07666
AI Past, Present and Future
AI Past, Present and Future
This presentation provides an introductory talk to the field of Artificial Intelligence (AI), tracing its evolution through key milestones and breakthroughs. We will explore the foundational requirements for AI, including the critical role of big data, and high-performance computing (HPC). The presentation will highlight AI's potentials, such as natural language processing (NLP), image processing, and diverse applications in scientific research. Additionally, we will address the inherent limitations of AI, focusing on security challenges, and the risks of data leakage. This introduction aims to equip participants with an understanding of AI's capabilities and constraints, fostering informed discussions about its future impact Co-sponsored by: Ali Daneshmand Speaker(s): Ifana, Agenda: Virtual: https://events.vtools.ieee.org/m/428481
Careers in Technology Fall Series 2024 – Florence Hudson and William Harding, PhD 08 October 8pm EST / 7 pm CST
Careers in Technology Fall Series 2024 – Florence Hudson and William Harding, PhD 08 October 8pm EST / 7 pm CST
Careers in Technology and Standards in Action: IEEE 2933 Clinical IoT Data and Device Interoperability with TIPPSS - Trust, Identity, Privacy, Protection, Safety, Security a new Standard -- Selected for the 2024 Award Advanced technologies enable digital transformation … and can increase RISK. Connected healthcare leveraging advanced technologies and data can improve insights and outcomes. In the context of this important rapidly developing Clinical IoT industry, the IEEE 2933 Working Group has been selected as a recipient of the IEEE SA Emerging Technology Award “For the development of IEEE 2933-2024, IEEE Standard for Clinical Internet of Things (IoT) Data and Device Interoperability with TIPPSS - Trust, Identity, Privacy, Protection, Safety, Security.” The IEEE SA Emerging Technology Award is awarded for the initiation, advancement or progression of a new technology through the IEEE SA open consensus process. Leadership of this team Florence Hudson and William Harding, PhD will share their extensive knowledge and experience, and provide an excellent deep dive into careers in this field. Speaker(s): Florence Hudson, William C. Harding, PhD Virtual: https://events.vtools.ieee.org/m/434307
38th Annual Symposium and Mini-Show – Exhibitor Registration Site
38th Annual Symposium and Mini-Show – Exhibitor Registration Site
SELECTED TOPICS IN RF AND MICROWAVE TECHNOLOGIES FOR COMMERCIAL AND MILITARY APPLICATIONS Agenda: Table Preference (Not Guranteed): Hanover Manor, 16 Eagle Rock Ave, East Hanover, New Jersey, United States, 07936
38th ANNUAL SYMPOSIUM AND MINI-SHOW – Attendee
38th ANNUAL SYMPOSIUM AND MINI-SHOW – Attendee
SELECTED TOPICS IN RF AND MICROWAVE TECHNOLOGIES FOR COMMERCIAL AND MILITARY APPLICATIONS TECHNICAL SESSIONS 10 –12 LECTURES FEATURING SPEAKERS FROM LEADING COMPANIES, WITH EMPHASIS ON MILITARY ELECTRONICS, WIRELESS TECHNOLOGIES AND MICROWAVE COMMUNICATIONS. MINI SHOW FEATURING MANUFACTURERS WITH LATEST PRODUCTS IN INSTRUMENTATION, COMPONENTS, SUBSYTEMS AND INTERCONNECTS (APPX. 40 plus EXHIBITORS) (COMPLIMENTARY LUNCH SERVED) Details of the schedule, speakers, and topics will be posted on the IEEE North Jersey section website by September 2024. Hanover Manor , 16 Eagle Rock Ave, E. Hanover, New Jersey, United States, 07936