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CODE: NAI06
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course offers a solid foundation in deep learning principles, techniques, and applications. It covers core architectures such as feedforward networks, CNNs, RNNs, and transformers, along with optimization and regularization methods. Participants will learn to design, train, and optimize neural networks to solve real-world problems. Using TensorFlow and PyTorch, participants will also gain hands-on experience through case studies and projects, building practical skills to develop AI-driven solutions across different domains.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand and apply the core principles of neural networks and deep learning.
Build and train advanced models such as CNNs, RNNs, and transformers for real-world applications.
Implement deep learning solutions using industry standard frameworks like TensorFlow and PyTorch.
Optimize models for performance, scalability, and deployment in production environments.
Critically evaluate deep learning research and adapt techniques to domain-specific problems.
At the end of this course, you’ll understand:
This specialization is designed for data scientists, machine learning engineers, software developers, researchers, and professionals seeking to expand their expertise in artificial intelligence. It is equally valuable for business and technology leaders who want to understand the strategic impact of deep learning in industries such as healthcare, finance, manufacturing, retail, and autonomous systems. A basic background in programming, linear algebra, and machine learning concepts is recommended.
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✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
