Course Format: Live Webinar, 4.0 hours of instruction! Series Overview: From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing.” This Part 1 and the planned Part 2 (to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning. This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book. More from the book introduction: Reference book: “Neural Networks and Deep Learning” by Michael Nielsen, http://neuralnetworksanddeeplearning.com/ “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network, with just a few tens of lines of code, and no special libraries.” “But you don’t need to be a professional programmer.” The code provided is in Python, which even if you don’t program in Python, should be easy to understand with just a little effort. Benefits of attending the series: * Learn the core principles behind neural networks and deep learning. * See a simple Python program that solves a concrete problem: teaching a computer to recognize a handwritten digit. * Improve the result through incorporating more and more core ideas about neural networks and deep learning. * Understand the theory, with worked-out proofs of fundamental The demo Python program (updated from version provided in the book) can be downloaded from the speaker’s GitHub account. The demo program is run in a Docker container that runs on your Mac, Windows, or Linux personal computer; we plan to provide instructions on doing that in advance of the class. (That would be one good reason to register early if you plan to attend, in order that you can receive the straightforward instructions and leave yourself with plenty of time to prepare the Git and Docker software that are widely used among software professionals.) Course Background and Content: This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book. Outline: - Feedforward Neural Networks - Simple (Python) Network to classify a handwritten digit - Learning with Stochastic Gradient Descent - How the backpropagation algorithm work - Improving the way neural networks learn: - - Cross-entropy cost function - SoftMax activation function and log-likelihood cost function - Rectified Linear Unit - Overfitting and Regularization: - - L2 regularization - Dropout - Artificially expanding data set Pre-requisites: There is some heavier mathematics in learning the four fundamental equations behind backpropagation, so a basic familiarity with multivariable calculus and matrix algebra is expected, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided Python code for the simple network just make use of the equations.) Basic familiarity with Python or similar computer language. Speaker(s): CL Kim, Boston, Massachusetts, United States, Virtual: https://events.vtools.ieee.org/m/450631

We are Looking for 10-15 judges Judges can be University students, Young Professionals, IEEE members, Senior Members, Life Members, Fellows What is TNJSF? The TNJSF (formerly the NJRSF) is a science fair competition for high school students (grades 9-12) for students in ten counties of northern New Jersey. The mission of the TNJSF is to support, encourage, and recognize student involvement in scientific researh and engineering design. It is our belief that students can only truly appreciate the creative nature of the process if they have actually experienced it themselves. In addition, we endeavor to provide resources which further this overarching goal, including giving students various opportunities to interact with professional scientists and engineers. The opportunity to partake of the TNJSF itself as well as other resources we offer is intended to be open to all high school students in our northern NJ region. IEEE volunteers are needed for Special Awards Judging at the Fair Special awards judging takes place on Sunday morning. Judges are asked to arrive at the judges' room by 9:15 a.m. to determine their project assignments with their team and to receive instructions from the Judging Coordinator. Judging of projects takes place from 10:00 a.m. - 12:00 p.m. During this time, judges will meet with students at their project displays and evaluate their work. At least 2 judges on the award team should see each project under consideration. At 12:00 p.m., the judges reconvene in the judges' room to discuss the projects and determine which projects will be awarded. Judging of special awards is usually completed by 1:00 p.m. Volunteers use the registration link in vTools so we have your email logged. You also need to register as a judge at https://tnjsf.org/ (find the "Register as a Judge for 2025!" in "Judges" menu drop down) - IMPORTANT NOTE: When registering on the TNJSF site, remember to select that you are judging for the "special" IEEE award that is taking place on Sunday. *** Sunday 23 March Schedule *** Sunday morning, March 23 - Special Awards Judging Please arrive at the judges' room by 9:15 a.m. 9:15 - 10:00 a.m. Judges meet their team and receive instructions. Continental breakfast provided. 10:00 a.m. - 12:00 p.m. Judging for special awards and ISEF Trip Award finalists. Students at projects. 12:00 p.m. - 1:00 p.m. Judges conference to evaluate projects and determine award winners. Agenda: [] Kean University, 1000 Morris Ave, Union, New Jersey, United States, 07083

IEEE NJ Coast Section - Awards Banquet - Save the date, more information to come around February 2025! Agenda: TBD 210 Jumping Brook Rd, Neptune, New Jersey, United States, 07753

Special Event 9 April 2025 8pm – 10pm Careers in Technology George Hurlburt on Ethics & Generative AI: the latest, a how-to demonstration, implications, standards, paper publications, impacts on humans, cyber physical systems, and more. Author of: “What If Ethics Got in the Way of Generative AI?” IEEE Computer Society EDGE November 2024; originally in: IT Professional vol. 25, no.2, 2023. George Hurlburt is the uncompensated chief scientist at the STEMCorp Foundation and serves on the Board of Advisors for the University System of Maryland at Southern Maryland. With Computer Society, SIGHT, COPE, AP-S, ComSoc, FutureNetworks, Standards, DIITA Dignity Identity Inclusion Trust and Agency Virtual: https://events.vtools.ieee.org/m/455308