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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

We are excited to invite you to a Career Development Seminar, "Job-search Strategies and Interview Skills", hosted by the IEEE Control Systems Society Student Branch at UConn! Our speaker, Theo Menounos (Assistant Director, College of Engineering Career Readiness Lead, UConn Center for Career Readiness & Life Skills), will provide valuable insights on how students like you can develop effective job-search strategies and learn tips for excelling in interviews. Speaker(s): Theo Menounos Agenda: Date: October 21st, Monday Time: 3:00 PM to 4:00 PM Location: ITE Building, Room 301, 371 Fairfield Way, Storrs, CT 06268 Speaker: Theo Menounos (Assistant Director, College of Engineering Career Readiness Lead, UConn Center for Career Readiness & Life Skills) Plus, there will be an interactive Q&A session, where you can ask Theo anything about career development! Room: 301, Bldg: ITE, 371 Fairfield Way, Storrs, Connecticut, United States, 06268

The next meeting of the Worcester Section IEEE Consultants' Network will take place on Monday October 21, 6:00PM at the Imperial Buffet at 380 Maple Ave, Shrewsbury, MA 01545. We will start gathering at 6:00 PM for dinner and mingle informally. The Buffet meal is will have a co-pay of $20 cash at the restaurant. The co-pay is due at arrival between 6:00 and 6:30. Any arrivals after 6:30PM will be on their own bill without the group covering any part of the meal. Any alcohol is cash bar. We will start the Networking proceedings promptly at 6:30 PM and we will discuss the latest fee survey results. All are welcome to attend but you need to sign up as we need an accurate count. We will be organizing future meetings and topics of interest. I hope to see everyone there. Agenda: Introductions Meal Networking Planning for future events Fee Survey results discussion 380 Maple Ave, Shrewsbury, Massachusetts, United States, 01545

IEEE CIS Distinguished Lecturer Professor Dipankar Dasgupta will give a presentation on Computational Intelligence in Cybersecurity. Co-sponsored by: IEEE North Jersey Computer Society Chapter Speaker(s): Dr. Dipankar Dasgupta, Agenda: The presentation by the speaker is followed by a Q&A session. Virtual: https://events.vtools.ieee.org/m/438726

[] Discussion about Delaware Bay Computer Chapter, Meeting, & Lecture "Geniuses at War" with Guest Speaker David Bondurant. There will be a discussion about the vitality, activities, and officers of the Delaware Bay Computer Chapter, followed by a Guest Speaker. Following the exploits of the motley collection of geniuses installed at Bletchley Park during the Second World War, author David Price focuses mainly on the events and people involved in the invention of Colossus—the first programmable, electronic, digital computer, which was designed to break the German army’s Lorenz cipher. Speaker Bio: David Bondurant has been involved with the computer and semiconductor industry for 50-years. He was a computer architect at Control Data, Sperry-Univac, and Honeywell. He was involved with the government-sponsored advanced semiconductor program called VHSIC (Very High Speed Integrated Circuits) at Univac & Honeywell where he developed microprocessor and ASIC semiconductor products in bipolar CML, CMOS, and radiation hard CMOS. He was involved with emerging non-volatile RAM marketing at industry leading companies, Ramtron (FRAM), Simtek (non-volatile SRAM), and Freescale Semiconductor/Everspin Technologies Agenda: Opening Remarks and Introductions Discussion about the Delaware Bay Computer Chapter Vitality, Officers, Meetings, Activities Guest Lecture "Geniuses at War" with Guest Speaker, David Bondurant Virtual: https://events.vtools.ieee.org/m/437478