Deep Learning

Introduction to Deep Learning

Target Audience: Students, professionals, and educators interested in deep learning concepts and applications.

This course provides an overview of deep learning fundamentals on modern Intel® architecture. Topics include:

• Types of problems that can be solved with deep learning
• Understanding neural networks and their building blocks
• Fundamentals of building and training deep learning models
• Exploring essential neural network architectures

By the end of this course, learners will have practical knowledge of:

• Fundamental neural network architectures: feedforward, convolutional, and recurrent networks
• Concepts like gradient descent, backpropagation, and activation functions
• Choosing appropriate architectures, tuning hyperparameters, and validating models
• Using pretrained models for transfer learning

The course runs for 12 weeks (3 hours per week) and uses Python for exercises - prior experience is helpful but not required.