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

Many physics laws and mathematical rules are insensitive to order. For example, the addition of numbers disregards the sequence order, e.g., 1+2+3=3+1+2. However, such a commutative property does not always hold. When the outcomes of a set of operations depend on the execution order, they can become “non-Abelian.” In the 20th century, non-Abelian mathematical frameworks have played profound roles in formulating many fundamental laws of modern physics. Famous examples include the classification of hadrons and the unification of electro-weak interactions. Classical physics, such as mechanics, electromagnetism, and optics, were well established before non-Abelian theories came into play. However, this does not mean non-Abelian effects are absent in the classical world. One prominent example is a Rubik’s Cube—the moves made on the cube do not always commute: two sequential moves done in different orders do not necessarily get the color palettes to the same layout. We then ask: how and when non-Abelian phenomena arise in classical waves? Delving into this question, our recent works leverage Berry-phase matrices, which capture the adiabatic evolution of multiple states, to realize non-Abelian braiding in acoustics and photonics . Here, the braiding operations are implemented using coupled waveguide arrays, which are adiabatically modulated along the guiding direction to enforce a multi-state Berry-phase matrix that swaps the modal dwell sites. The evolution of the guiding modes maps to the generators of braid groups. The non-Abelian characteristics are revealed by switching the order of two distinct braiding operations involving at least three modes. Our results offer new perspectives in exploring novel wave-controlling schemes for future technological applications . Z.-G. Chen, R.-Y. Zhang, C. T. Chan, and G. Ma, Classical Non-Abelian Braiding ofAcoustic Modes, Nat. Phys. 18, 179 (2022). X.-L. Zhang, F. Yu, Z.-G. Chen, Z.-N. Tian, Q.-D. Chen, H.-B. Sun, and G. Ma, Non-Abelian Braiding on Photonic Chips, Nat. Photon. 16, 390 (2022). Y. Yang, B. Yang, G. Ma, J. Li, S. Zhang, and C. T. Chan, Non-Abelian Physics in Light and Sound, Science 383, eadf9621 (2024). Co-sponsored by: Advanced Science Research Center - the Graduate Center - City University of New York Speaker(s): Guancong Ma 85 St. Nicholas Terrace 2.325, ASRC Auditorium, New York, New York, United States, NY 10031, Virtual: https://events.vtools.ieee.org/m/434235

Are you just out of school looking for a job? Are you looking to make a career change? Or just looking for work? Then this FREE Mini Career Symposium is one you should not miss! Just register and attend on-line. It's that simple. This is NJ Coast Sections' First Mini Career Symposium. It's being offered FREE of charge to any IEEE Member of any Grade including IEEE Student Members. NJ Coast Consultant's Network Affinity Group and the Education Chapter are co-sponsors. This symposium consists of 4 GREAT Talks from 4 different highly qualified speakers across two evenings; 5-8 PM. This 2 day or 2 evenings symposium kicks off with our very own Region 1 Director, Bala Prasanna. Bala is not only our Region 1 Director, he is also a member of The New Jersey Coast Section. His talk will speak to personal development and career security. Next Helen Godfrey, Career and Life Coach, will speak on how make to your interviews successful. Everyone has a story. Helen explains how to tell your story to get that job. The next evening starts off with Elizabeth Lions, Custom-crafted Job Coach. Elizabeth will teach how ChatGPT will transform your job search. This talk is followed by a talk from Robert Danielle, Job Coach, on personal branding. Robert will speak to how to highlight your personal skills as an asset to your next employer. PDH will be offered to anyone who attends the entire Symposium upon request. The PDH $5.00 Fee will be paid by the section for anyone who attended the entire 4 Talks and only upon request. This means it's FREE to you! Co-sponsored by: New Jersey Coast Consulants Network Affinity Group Speaker(s): Bala Prasanna, Robert Danielle, Elizabeth Lions, Helen Godfrey Agenda: Agenda Day 1 - 30 September 2024 (below times are EDT / New York) 5:00 - 5:10 PM Welcome 5:10 - 6:25 PM Bala Prasanna, IEEE Region 1 Director and NJ Coast Member Level Up Your Career: Skills, Strategies, and Impact A few Tips to Manage Professional Development & Career Security 6:25 - 7:40 PM Robert Danielle, Job Coach Personal Branding 7:40 - 7:50 PM IEEE USA Resources 7:50 - 8:00 PM Closing Day 2 - 1 October 2024 (below times are EDT / New York) 5:00 - 5:10 PM Welcome 5:10 - 6:25 PM Elizabeth Lions, Custom-Crafted Job Coach Discover How Chat GPT Can Transform Your Job Search! 6:25 - 7:40 PM Helen Godfrey, Career & Life Coash Interviews That Get Results 7:40 - 7:50 PM IEEE USA Resources 7:50 - 8:00 PM Closing Virtual: https://events.vtools.ieee.org/m/427432