Overview
Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive results in a series of traditional and creative applications. For learners with basic deep learning and Python programming knowledge, it is critical to complete their knowledge towards implementation of deep learning systems in the real-world ecosystems, as well as to have more information about advanced deep learning models, how they work, what are their advantages, and applications. Again, since the implementation of robust natural language processors is one of the abilities of deep neural networks, and Natural Language Processing (NLP) has got many applications in a diverse set of businesses, practicing that application of deep learning is advisable and rational.
Course Description & Learning Outcomes
At the completion of the course, the participants will be able to:
Articulate advanced deep learning models, their strengths and constraints, and their applications.
Cultivate advanced deep learning systems in different businesses.
Have basic skills in using deep learning algorithms to implement simple Natural Learning Processing (NLP) systems.
Schedule
End Date: 14 Mar 2025, Friday
Location: 11 Research Link, COM 3 Singapore 119391, 119391
Pricing
Skills Covered
PROFICIENCY LEVEL GUIDE
Beginner: Introduce the subject matter without the need to have any prerequisites.
Proficient: Requires learners to have prior knowledge of the subject.
Expert: Involves advanced and more complex understanding of the subject.
- Deep Learning (Proficiency level: Beginner)
