BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250823T162407Z UID:B4F766F5-3AA9-49CC-BE2D-C8D03F933A92 DTSTART;TZID=America/Los_Angeles:20230602T043000 DTEND;TZID=America/Los_Angeles:20230602T173000 DESCRIPTION:Deep learning (DL) is a subfield of Machine Learning that deals with algorithms inspired by the structure and function of the brain. DL h as revolutionized the world with its advancement in technology\, finding i ts application in every sector of business. Deep Learning models help adve rtisers leverage data to perform real-time bidding and targeted display ad vertising. It is also used to make weather predictions about rain\, earthq uakes\, and tsunamis. With deep learning\, machines can comprehend speech and provide the required output. It enables the machines to recognize peop le and objects in the images fed to it. TensorFlow is currently the most p opular learning library in the world\, based on the concept of tensors\, w hich are vectors or matrices of n dimensions. This workshop will explore s elected programming tools using python and/or Matlab and demonstrates the Deep Learning (DL) steps with examples. The focus will be handling of some of the widely used methods and key issues that students may come across\, suggesting a number of discrete resolutions so as to provide optimal resu lts on the accuracy and validity of Train and Test methodology.\n\nSpeaker (s): Dr. Muqri\n\nAgenda: \n- What is Deep learning and why do we need it ?\n- Deep Learning Libraries\n- Steps in Deep Learning Life Cycle\n- Tenso rs and Neural Networks\n- TensorFlow Computations and Data Flow Graph\n- W hat are Keras and tf.keras\n- Creating Simple Neural network in Keras\n- M NIST Data Set Images\n- How to train a CNN (Convolutional Neural Network ) Model\n- MNIST Handwritten Digit classification using Tensor Flow\n- Fitti ng and Evaluating a CNN Model\n- How to Visualize Deep Learning Models\n- Plotting Model learning Curves\n- Summary & Key Takeaways\n\nVirtual: http s://events.vtools.ieee.org/m/362161 LOCATION:Virtual: https://events.vtools.ieee.org/m/362161 ORGANIZER:nazila_safavi@yahoo.com SEQUENCE:20 SUMMARY:Intro to Neural Networks and Deep Learning by Dr. Mohammad Muqri. H osted by Dr. Nazila Safavi URL;VALUE=URI:https://events.vtools.ieee.org/m/362161 X-ALT-DESC:Description: <br /><p>Deep learning (DL) is a subfield of Machin e Learning that deals with algorithms inspired by the structure and functi on of the brain. DL&nbsp\;has revolutionized the world with its advancemen t in technology\, finding its application in every sector of business.&nbs p\; Deep Learning models help advertisers leverage data to perform real-ti me bidding and targeted display advertising. It is also used to make weath er predictions about rain\, earthquakes\, and tsunamis. With deep learning \, machines can comprehend speech and provide the required output. It enab les the machines to recognize people and objects in the images fed to it. TensorFlow is currently the most popular learning library in the world\, b ased on the concept of tensors\, which are vectors or matrices of <em>n</e m> dimensions. This workshop will explore selected programming tools using python and/or Matlab and demonstrates the Deep Learning (DL) steps with e xamples. The focus will be handling of some of the widely used methods and key issues that students may come across\, suggesting a number of discret e resolutions so as to provide optimal results on the accuracy and validit y of Train and Test methodology.</p><br /><br />Agenda: <br /><ul>\n<li><s trong>What is Deep learning and why do we need it ?</strong></li>\n<li><st rong>Deep Learning Libraries</strong></li>\n<li><strong>Steps in Deep Lear ning Life Cycle </strong></li>\n<li><strong>Tensors and Neural Networks</s trong></li>\n<li><strong>TensorFlow Computations and Data Flow Graph</stro ng></li>\n<li><strong>What are </strong><strong>Keras</strong><strong> and </strong><strong>tf.keras</strong></li>\n<li><strong>Creating Simple Neur al network in </strong><strong>Keras</strong></li>\n<li><strong>MNIST Data Set Images</strong></li>\n<li><strong>How to train a CNN (Convolutional N eural Network )Model</strong></li>\n<li><strong>MNIST Handwritten Digit cl assification using Tensor Flow</strong></li>\n<li><strong>Fitting and Eval uating a CNN Model</strong></li>\n<li><strong>How to Visualize Deep Learni ng Models</strong></li>\n<li><strong>Plotting Model learning Curves</stron g></li>\n<li><strong>Summary &amp\; Key Takeaways</strong></li>\n</ul> END:VEVENT END:VCALENDAR