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Deep Learning With Applications

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

Date and Time

• Start time: 21 Sep 2024 01:30 PM
• End time: 02 Nov 2024 04:30 PM
• All times are (UTC-04:00) Eastern Time (US & Canada)
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Location

• FDU Metropolitan Campus
• 960 River Road
• Teaneck, New Jersey
• United States 07666
• Building: Becton Building
• Room Number: Room 306
• Click here for Map

Hosts

• North Jersey Section Chapter,COM 19
• North Jersey Section
• Contact Event Hosts

• Amit Patel - a.j.patel @ ieee . org  and Kalyan Mondal – k.mondal @ ieee . org

• Co-sponsored by Education Committee

Registration

• Starts 19 August 2024 12:00 AM
• Ends 21 September 2024 12:00 AM
• All times are (UTC-04:00) Eastern Time (US & Canada)
• Admission fee

Speakers

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.