Artificial Neural Networks (Deep Learning)

NOTE: This course is divided into four (4) parts over three (3) days.

Part I and Part II Description:

Introduction of neural network programming concepts, theory, and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts. (The Keras neural network framework will be used for neural network programming but no experience with Keras will be expected.)

Part III Description:

This part will continue the development of neural network programming approaches from Parts I and II. This part will focus on generative methods used to create images: variational auto-encoders, generative adversarial networks, and diffusion networks.

Part IV Description:

This part will continue the development of neural network programming approaches from Parts I through III. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers.

Level: Intermediate

Length: Four 3-Hour Sessions (3 Days)

Format: Lecture + Hands-on

Prerequisites:

• Experience with Python (version 3.10) is assumed.
• Each part assumes what was covered in the previous parts of this course.
• Parts III and IV assume experience with neural network programming, per the first two neural network programming sessions in this course.