Course Overview

Participants will begin with a solid understanding of what deep learning is and how it differs from traditional machine learning approaches. They will then progress through increasingly complex topics and techniques, applying what they've learned to real-world datasets and problems.

Course Outcome

• In-depth knowledge of deep learning concepts and techniques.
• Hands-on experience with leading deep learning frameworks (e.g., TensorFlow, PyTorch).
• Skills in building, training, and deploying various neural network architectures.
• Ability to tackle complex problems in image and speech recognition, natural language understanding, and more.
• Understanding of the ethical and societal implications of deep learning technologies.

Course Structure:

• Total Number of Lessons: 96 lessons
• Frequency: 3 Classes per week and 3 hours per class
• Format: Combination of online theoretical lessons, practical coding labs, and project-based learning

The course concludes with a significant capstone project that allows participants to apply their deep learning skills to a complex, real-world problem. Upon successful completion, participants will receive a certificate of completion or earn credits towards professional development. This course is ideal for those with a solid foundation in machine learning looking to specialize in the cutting-edge field of deep learning.