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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

These two distinguished speakers are from European Space Agency, Netherlands. They will present the state of art work in the following areas. The objective of the two IEEE Distinguished Lectures consists in presenting the state of the art and the on-going developments in Multi-Beam Antennas (MBAs) and Beam-Forming Networks (BFNs). They find application in several fields including communications, remote sensing (e.g. radars, radiometers, etc.), electronic surveillance and defense systems, science (e.g. multibeam radio telescopes), RF navigation systems, etc. They may be installed on board satellites, airplanes, trains, buses, buildings, cars etc. MBAs and BFNs are becoming also fundamental elements in emerging MIMO and 5G communications. The BFN plays an essential role in any antenna system relaying on a set of radiating elements to generate a beam. The lectures cover both theoretical and practical aspects for the following topics: • Overview of system applications • Multibeam Antenna Architectures (based on Reflectors, Arrays, Lenses) • Beamforming Networks Analogue BFNs (Corporate, Blass, Nolen, Butler matrices, Digital BFNs) • Overview of some Operational Multibeam Antennas/BFNs • On-going European Developments • Current Design and Technological Challenges Co-sponsored by: Antennas and Propagation, Microwave Theory and Techniques and Communication Society Speaker(s): Giovanni, , Room: Holmdel Township meeting room, Bldg: Bell Works, Holmdel Library, 101 Crawfords Corner road, Holmdel, New Jersey, United States, 07733, Virtual: https://events.vtools.ieee.org/m/437493

This webinar is a collaboration between IEEE-USA and IEEE Women in Engineering (WIE). Trying to re-enter the workforce after an extended absence presents challenges, and can be a daunting task. Changes have happened in the workplace and in technologies used. The atmosphere and expectations may have changed (for example, the impacts COVID had on remote work for many jobs!) You have changed… and self-doubt may have crept in after being away! However, preparing in advance is the key to easing the challenges. In this talk, you will hear about my own return to work after 12 years of minimal part-time employment in my field, and also information from my own research into what experts recommend. You will learn: - What is an extended absence (and the impact of how long “extended” is!) - Why it is important to prepare for returning - When to start preparing - How to prepare - Who to seek out for support / assistance - Where to look for opportunities Speaker(s): Jill Gostin Agenda: IEEE-USA's free webinars/events are designed to help you find your next job, maintain your career, negotiate an appropriate salary, understand ethical considerations in the workplace and learn about other career-building strategies and public policy developments that affect your profession. Learn about our sponsor: the IEEE Member Group Insurance Program - Powered by AMBA. AMBAspecializes in providing tailored insurance solutions for IEEE members. Whether you’re seeking health, life, or disability coverage, AMBA has you covered. Visit the IEEE Member Group Insurance Program website to explore the benefits and options available to you: (https://www.ieeeinsurance.com/) For information regarding upcoming webinars or to visit our vast webinar archive, please visit: (https://ieeeusa.org/careers/webinars/) (https://newsletter.smartbrief.com/rest/sign-up/2479DAB0-4089-43E7-925D-86AE0C1E6244?campaign=e0d52cef) Virtual: https://events.vtools.ieee.org/m/427453

Oct 23rd, 4:30 to 6:00 PM (+30 min to 6:30 for questions): Advanced topic 1 - high-speed/mixed signal layout considerations. Co-sponsored by: IEEE Cornell Student Section Room: 155, Bldg: Olin Hall, 113 Ho Plaza, Ithaca, New York, United States, 14853

Optical frequency combs, with their unique features in both the time and frequency domains, have transformed precision science and engineering over the last two decades. In this lecture, I will present on the latest progress in the ultralow-noise frequency combs and their applications with an emphasis on precision timing, synchronization, and microwave/mm-wave photonics. Both mode-locked laser combs and chip-scale micro-combs can reach quantum-limited timing jitter performances, which allows for various timing applications with unprecedented precision. I will present innovative comb timing applications including attosecond optical timing, on-chip clock distribution networks, ultralow-noise microwave/mm-wave signal generation, photonic analog-to-digital conversion, ranging, imaging and vibration sensing, and timing and synchronization for ultrafast X-ray/electron science and radio astronomy. Speaker(s): Jungwon Agenda: This TALK IS ONLINE ONLY. Please register for the talk. There is no fee for this talk. 6:30 - 7:30 PM: Speaker Introduction and Distinguished lecture Virtual: https://events.vtools.ieee.org/m/438066