"Introduction to Neural Networks and Deep Learning (Part 2 – Convolutional Neural Networks, Basic Language Modeling)” 

Series Overview: Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, natural language processing, and generative AI.
Registration Fees:
Members Early Rate (by April 4) $115.00
Members Rate after (April 4) $130.00
Non-Member Early Rate (April 4) $135.00
Non-Member Rate after (April 4): $150.00
Decision to run or cancel the course is: Friday, April 11, 2025
The Part 1 class and this Part 2 class will teach many of the core concepts behind neural networks and deep learning, and basic language modeling.
The planned Part 3 class (to be confirmed) will teach a simple Generative Pre-trained Transformer (GPT), based on the seminal Attention is All You Need paper and OpenAI's GPT-2/GPT-3.
In this Part 2 class, in the first section, we again use a neural network in teaching a computer to recognize handwritten digits. Here we introduce the convolutional neural network. They are predominantly used in computer vision applications, such as for recognizing objects in images.
The second section of the Part 2 class introduces basic language modeling, and simple generation of text based on prior learned text, in this case, baby names.
But you don’t need to be a professional programmer. The demo code provided is in Python, and should be easy to understand with just a little effort.
Benefits of attending this Part 2 class of the series:
• Build upon the core principles behind neural networks and deep learning in the Part 1 class to learn about convolutional neural networks.
• 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 basic language modeling.
• Implement a simple language model that generates baby names from existing names.
• Get introduced to the popular PyTorch library.
• Run straightforward Python demo code examples.
Part 2 class Pre-requisites: The material in the Part 1 class, which requires some basic familiarity with multivariable calculus and matrix algebra, but nothing advanced. Basic familiarity with Python or similar computer language.
Speaker(s): CL Kim,
Agenda:
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 equations of backpropagation for those interested.
• Understand basic language modeling.
• Run straightforward Python demo code example.
Virtual: https://events.vtools.ieee.org/m/465524