BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260206T184425Z UID:CB61F566-2995-4859-9A3F-FB13CD45415F DTSTART;TZID=America/Chicago:20260212T173000 DTEND;TZID=America/Chicago:20260212T190000 DESCRIPTION:Deep Learning: Overview\, Applications and Optimization\n\nDr. Mohamed Shaban\n\nECE\, University of South Alabama\n\nDeep learning (DL) has emerged as a transformative paradigm within artificial intelligence\, driving significant advancements across diverse sectors\, including comput er vision\, natural language processing\, healthcare\, and autonomous syst ems. By utilizing hierarchical\, multi-layer artificial neural networks— such as CNNs\, RNNs\, and Transformers—DL models possess the capability to automatically extract complex patterns and high-level features directly from raw\, large-scale data. This presentation explores the foundational techniques of deep learning\, highlighting its applications in critical do mains such as medical image analysis. Despite their high predictive accura cy\, the significant computational\, memory\, and energy requirements of m odern deep learning models restrict their deployment on resource-constrain ed edge devices. To address this\, the presentation surveys various comple xity reduction techniques—including model pruning\, quantization\, low-r ank approximation\, and knowledge distillation—designed to accelerate tr aining and inference speeds. It will also be shown that these optimization approaches can substantially reduce model size and computational operatio ns (FLOPs) while maintaining acceptable performance levels.\n\n[]\n\nCo-sp onsored by: Mobile chapter of IEEE\n\nSpeaker(s): Mohamed Shaban\, \n\nRoo m: 2119\, Bldg: Shelby Hall\, University of South Alabama\, 150 Student Se rvices Dr.\, Mobile\, Alabama\, United States\, 36688\, Virtual: https://e vents.vtools.ieee.org/m/537598 LOCATION:Room: 2119\, Bldg: Shelby Hall\, University of South Alabama\, 150 Student Services Dr.\, Mobile\, Alabama\, United States\, 36688\, Virtual : https://events.vtools.ieee.org/m/537598 ORGANIZER:IEEE@southalabama.edu SEQUENCE:16 SUMMARY:IEEE Mobile Section Hybrid Technical Meeting URL;VALUE=URI:https://events.vtools.ieee.org/m/537598 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="mso-pagination: n one\; text-align: center\;">&nbsp\;</p>\n<p class="MsoNormal" style="mso-p agination: none\; text-align: center\;"><span lang="en-US" style="font-siz e: 16.0pt\; font-family: Arial\; mso-default-font-family: Arial\; mso-asci i-font-family: Arial\; mso-latin-font-family: Arial\; mso-greek-font-famil y: Arial\; mso-cyrillic-font-family: Arial\; mso-armenian-font-family: Ari al\; mso-hebrew-font-family: Arial\; mso-arabic-font-family: Arial\; mso-c urrency-font-family: Arial\; mso-latinext-font-family: Arial\; color: blue \; mso-style-textfill-type: solid\; mso-style-textfill-fill-color: blue\; mso-style-textfill-fill-alpha: 100%\; font-weight: bold\; language: en-US\ ; mso-ansi-language: en-US\; mso-ligatures: none\;">Deep Learning: Overvie w\, Applications and Optimization </span></p>\n<p class="MsoNormal" style= "mso-pagination: none\; text-align: center\;"><span lang="en-US" style="fo nt-size: 16.0pt\; font-family: Arial\; mso-default-font-family: Arial\; ms o-ascii-font-family: Arial\; mso-latin-font-family: Arial\; mso-greek-font -family: Arial\; mso-cyrillic-font-family: Arial\; mso-armenian-font-famil y: Arial\; mso-hebrew-font-family: Arial\; mso-arabic-font-family: Arial\; mso-currency-font-family: Arial\; mso-latinext-font-family: Arial\; color : red\; mso-style-textfill-type: solid\; mso-style-textfill-fill-color: re d\; mso-style-textfill-fill-alpha: 100%\; font-weight: bold\; language: en -US\; mso-ansi-language: en-US\; mso-ligatures: none\;">Dr. Mohamed Shaban &nbsp\; </span></p>\n<p class="MsoNormal" style="mso-pagination: none\; te xt-align: center\;"><span lang="en-US" style="font-size: 16.0pt\; font-fam ily: Arial\; mso-default-font-family: Arial\; mso-ascii-font-family: Arial \; mso-latin-font-family: Arial\; mso-greek-font-family: Arial\; mso-cyril lic-font-family: Arial\; mso-armenian-font-family: Arial\; mso-hebrew-font -family: Arial\; mso-arabic-font-family: Arial\; mso-currency-font-family: Arial\; mso-latinext-font-family: Arial\; color: red\; mso-style-textfill -type: solid\; mso-style-textfill-fill-color: red\; mso-style-textfill-fil l-alpha: 100%\; font-weight: bold\; language: en-US\; mso-ansi-language: e n-US\; mso-ligatures: none\;">ECE\, University of South Alabama</span></p> \n<p class="MsoNormal" style="mso-pagination: none\; text-align: center\;" ><span lang="en-US" style="font-size: 5.0pt\; font-family: Arial\; mso-def ault-font-family: Arial\; mso-ascii-font-family: Arial\; mso-latin-font-fa mily: Arial\; mso-greek-font-family: Arial\; mso-cyrillic-font-family: Ari al\; mso-armenian-font-family: Arial\; mso-hebrew-font-family: Arial\; mso -arabic-font-family: Arial\; mso-currency-font-family: Arial\; mso-latinex t-font-family: Arial\; color: red\; mso-style-textfill-type: solid\; mso-s tyle-textfill-fill-color: red\; mso-style-textfill-fill-alpha: 100%\; font -weight: bold\; language: en-US\; mso-ansi-language: en-US\; mso-ligatures : none\;">&nbsp\;</span></p>\n<p class="MsoNormal" style="mso-pagination: none\; text-align: justify\; text-justify: newspaper\; text-kashida-space: 50%\;"><span lang="en-US" style="font-size: 13.5pt\; line-height: 119%\; font-family: Arial\; mso-default-font-family: Arial\; mso-ascii-font-famil y: Arial\; mso-latin-font-family: Arial\; mso-greek-font-family: Arial\; m so-cyrillic-font-family: Arial\; mso-armenian-font-family: Arial\; mso-heb rew-font-family: Arial\; mso-arabic-font-family: Arial\; mso-currency-font -family: Arial\; mso-latinext-font-family: Arial\; color: blue\; mso-style -textfill-type: solid\; mso-style-textfill-fill-color: blue\; mso-style-te xtfill-fill-alpha: 100%\; font-weight: bold\; language: en-US\; mso-ansi-l anguage: en-US\; mso-ligatures: none\;">Deep learning (DL) has emerged as a transformative paradigm within artificial intelligence\, driving signifi cant advancements across diverse sectors\, including computer vision\, nat ural language processing\, healthcare\, and autonomous systems. By utilizi ng hierarchical\, multi-layer artificial neural networks&mdash\;such as CN Ns\, RNNs\, and Transformers&mdash\;DL models possess the capability to au tomatically extract complex patterns and high-level features directly from raw\, large-scale data. This presentation explores the foundational techn iques of deep learning\, highlighting its applications in critical domains such as medical image analysis.&nbsp\;Despite their high predictive accur acy\, the significant computational\, memory\, and energy requirements of modern deep learning models restrict their deployment on resource-constrai ned edge devices. To address this\, the presentation surveys various compl exity reduction techniques&mdash\;including model pruning\, quantization\, low-rank approximation\, and knowledge distillation&mdash\;designed to ac celerate training and inference speeds. It will also be shown that these o ptimization approaches can substantially reduce model size and computation al operations (FLOPs) while maintaining acceptable performance levels. </s pan></p>\n<p class="MsoNormal" style="mso-pagination: none\;"><span lang=" en-US" style="language: en-US\; mso-ligatures: none\;">&nbsp\;</span></p>\ n<p class="MsoNormal"><img style="display: block\; margin-left: auto\; mar gin-right: auto\;" src="https://events.vtools.ieee.org/vtools_ui/media/dis play/c7bd1716-731d-44aa-b644-385ef2b7aa8e" alt="" width="922" height="1193 "></p>\n<p class="MsoNormal" style="mso-pagination: none\; text-align: jus tify\; text-justify: newspaper\; text-kashida-space: 50%\;"><span lang="en -US" style="font-size: 13.5pt\; line-height: 119%\; font-family: Arial\; m so-default-font-family: Arial\; mso-ascii-font-family: Arial\; mso-latin-f ont-family: Arial\; mso-greek-font-family: Arial\; mso-cyrillic-font-famil y: Arial\; mso-armenian-font-family: Arial\; mso-hebrew-font-family: Arial \; mso-arabic-font-family: Arial\; mso-currency-font-family: Arial\; mso-l atinext-font-family: Arial\; color: blue\; mso-style-textfill-type: solid\ ; mso-style-textfill-fill-color: blue\; mso-style-textfill-fill-alpha: 100 %\; font-weight: bold\; language: en-US\; mso-ligatures: none\;">&nbsp\;</ span></p>\n<p class="MsoNormal" style="mso-pagination: none\;"><span lang= "en-US" style="language: en-US\; mso-ligatures: none\;">&nbsp\;</span></p> END:VEVENT END:VCALENDAR