BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241106T131357Z UID:3C6705DE-0FBC-405C-BB6E-B25253042E80 DTSTART;TZID=America/New_York:20240921T133000 DTEND;TZID=America/New_York:20241102T163000 DESCRIPTION:September 21 through November 2\, 2024. Six Saturdays 1:30-4:30 pm (9/21\, 9/28\, 10/5\, 10/19\, 10/26\, 11/2).\n\nThe IEEE North Jersey S ection Communications Society Chapter is offering a course entitled "DEEP LEARNING WITH APPLICATIONS".\n\nDeep learning is a transformative field wi thin artificial intelligence and machine learning that has revolutionized our ability to solve complex problems in various domains\, including compu ter vision\, natural language processing\, and reinforcement learning. Thi s hands-on course on deep learning is designed to provide students with an understanding how these amazing successes are made possible by drawing in spiration from the way that brains\, both human and otherwise\, operate. S tudents will gain a comprehensive foundation in the principles\, technique s\, and applications of deep neural networks.\n\nLearning how to solve rea l 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 class ification using commonly available data sets. However\, participants are e ncouraged to apply the techniques from this course to other data sets acco rding to their interests. Discuss with the instructor in order to propose your own project. More importantly\, this will set the foundations for und erstanding and developing Generative AI applications.\n\nThe IEEE North Je rsey Section's Communications Society Chapter can arrange for providing IE EE CEUs - Continuing Education Units (for a $5 charge) upon completion of the course. Course prices: $75 for Undergrad/Grad/Life/ComSoc members\, $1 00 for IEEE members\, $150 for non-IEEE members\n\nCo-sponsored by: Educat ion Committee\n\nSpeaker(s): Thomas Long\, \n\nAgenda: \n1. Introduction t o Neural Networks: Explore the fundamental concepts of artificial neural n etworks\, backpropagation\, activation\nfunctions\, and gradient descent\, laying the groundwork for deep learning understanding.\n2. Introduction t o PyTorch: Learn how to implement and train neural networks using PyTorch one of the most popular deep\nlearning frameworks. Understand tensors.\n3. Computer Vision Applications: Apply deep learning to computer vision prob lems\, including image classification and object detection using Convoluti onal Neural Networks (CNNs)\n4. Training and Optimizing Deep Neural Networ ks: Study techniques for training deep neural networks effectively\, inclu ding optimization algorithms\, weight initialization\, regularization\, an d dropout.\n5. Sequential Data Analysis: Explore how deep learning is used to analyze sequential data using Recurrent Neural Networks (RNNs). In par ticular\, explore how neural networks are used in Natural Language Process ing (NLP) tasks such as sentiment analysis and machine translation.\n6. Ge nerative AI: Overview of generative ai techniques that leverage the patter ns present in a dataset to generate new content. Applications of generativ e ai include large language models such as ChatGPT and image generation mo dels such as Midjourney and Stable Diffusion.\n\nThis course assumes a bas ic understanding of machine learning concepts and programming skills in Py thon. Familiarity with linear algebra and calculus will be beneficial\, bu t not mandatory. Statistical software (Python\, Scikit-learn) and Deep Lea rning 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.\n\nLearning objectives: Subjects co vered include these and other deep learning related materials: artificial neural networks\, training deep neural networks\, RNN\, CNN\, image recogn ition\, natural language processing\, GANs\, data processing techniques\, and NN architectures.\n\nThe course is intended to be subdivided into 3-ho ur sessions. Each lecture is further subdivided into lecture\, guided and independent project based exercises to build experience with hands-on tech niques. This course will be held at FDU - Teaneck\, NJ campus. Checks shou ld NOT be mailed to this address. Can bring checks in person or use online payments at registration. Email the organizer for any questions about cou rse\, registration\, or other issues.\n\nTechnical Requirements: Students will need access to the Python programming language. In addition to a stan dard Python installation\, most programming exercises will use the package Scikit-learn. Basic programming skills and some familiarity with the Pyth on language are assummed.\nStudents are expected to be able to bring a lap top onto which most of these libraries can be pre-installed using python's pip install.\n\nMost of the coding in this course will use the Python pro gramming language. Coding examples and labs will be distributed in the for m of Juypter notebooks. In addition to standard Python\, most programming exercises will use either the PyTorch or TensorFlow libraries. Books and o ther resources will be referenced.\n\nRoom: Room 306\, Bldg: Becton Buildi ng \, FDU Metropolitan Campus\, 960 River Road\, Teaneck\, New Jersey\, Un ited States\, 07666 LOCATION:Room: Room 306\, Bldg: Becton Building \, FDU Metropolitan Campus\ , 960 River Road\, Teaneck\, New Jersey\, United States\, 07666 ORGANIZER:a.j.patel@ieee.org SEQUENCE:43 SUMMARY:Deep Learning With Applications URL;VALUE=URI:https://events.vtools.ieee.org/m/431023 X-ALT-DESC:Description: <br /><p>September 21 through November 2\, 2024. Si x Saturdays 1:30-4:30pm (9/21\, 9/28\, 10/5\, 10/19\, 10/26\, 11/2).&nbsp\ ;&nbsp\;</p>\n<p>The IEEE North Jersey Section Communications Society Chap ter is offering a course entitled "DEEP LEARNING WITH APPLICATIONS".&nbsp\ ;</p>\n<p>Deep learning is a transformative field within artificial intell igence and machine learning that has revolutionized our ability to solve c omplex problems in various domains\, including computer vision\, natural l anguage processing\, and reinforcement learning. This hands-on course on d eep learning is designed to provide students with an understanding how the se amazing successes are made possible by drawing inspiration from the way that brains\, both human and otherwise\, operate. Students will gain a co mprehensive foundation in the principles\, techniques\, and applications o f deep neural networks.</p>\n<p>Learning how to solve real data-set based applications will teach students how to really apply deep learning with Py thon programming software.&nbsp\; 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 t heir interests. Discuss with the instructor in order to propose your own p roject.&nbsp\; More importantly\, this will set the foundations for unders tanding and developing Generative AI applications.</p>\n<p>The IEEE North Jersey Section's Communications Society Chapter can arrange for providing IEEE CEUs - Continuing Education Units (for a $5 charge) upon completion o f the course.&nbsp\; Course prices: $75 for Undergrad/Grad/Life/ComSoc mem bers\, $100 for IEEE members\, $150 for non-IEEE members</p><br /><br />Ag enda: <br /><p>1. Introduction to Neural Networks: Explore the fundamental concepts of artificial neural networks\, backpropagation\, activation<br> functions\, and gradient descent\, laying the groundwork for deep learning understanding.<br>2. Introduction to PyTorch: Learn how to implement and train neural networks using PyTorch one of the most popular deep<br>learni ng frameworks. Understand&nbsp\;tensors.<br>3. Computer Vision Application s: Apply deep learning to computer vision problems\, including image class ification and object detection using Convolutional Neural Networks (CNNs)< br>4. Training and Optimizing Deep Neural Networks: Study techniques for t raining deep neural networks effectively\, including optimization algorith ms\, weight initialization\, regularization\, and dropout.<br>5. Sequentia l Data Analysis: Explore how deep learning is used to analyze sequential d ata using Recurrent Neural Networks (RNNs). In particular\, explore how ne ural networks are used in Natural Language Processing (NLP) tasks such as sentiment analysis and machine translation.<br>6. Generative AI: Overview of generative ai techniques that leverage the patterns present in a datase t to generate new content. Applications of generative ai include large lan guage models such as ChatGPT and image generation models such as Midjourne y and Stable Diffusion.</p>\n<p>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.& nbsp\;&nbsp\;Statistical software (Python\, Scikit-learn) and Deep Learnin g 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.</p>\n<p>Learning objectives:&nbsp\; Sub jects covered include these and other deep learning related materials: art ificial neural networks\, training deep neural networks\, RNN\, CNN\, imag e recognition\, natural language processing\, GANs\, data processing techn iques\, and NN architectures.</p>\n<p>The course is intended to be subdivi ded into 3-hour sessions. Each lecture is further subdivided into lecture\ , guided and independent project based exercises to build experience with hands-on techniques.&nbsp\; This course will be held at FDU - Teaneck\, NJ campus.&nbsp\; Checks should NOT be mailed to this address.&nbsp\; Can br ing checks in person or use online payments at registration.&nbsp\; Email the organizer for any questions about course\, registration\, or other iss ues.</p>\n<p>Technical Requirements: Students will need access to the Pyth on programming language. In addition to a standard Python installation\, m ost programming exercises will use the package Scikit-learn. &nbsp\;Basic programming skills and some familiarity with the Python language are assum med.<br>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.</p>\n <p>Most of the coding in this course will use the Python programming langu age. Coding examples and labs will be distributed in the form of Juypter n otebooks. In addition to standard Python\, most programming exercises will use either the PyTorch or TensorFlow libraries.&nbsp\; Books and other re sources will be referenced.</p> END:VEVENT END:VCALENDAR