Length: 2 Days
The Certified Neuro-symbolic AI Specialist (CNAIS) Certification by Tonex provides in-depth knowledge and practical skills in neuro-symbolic AI, an emerging field that integrates neural networks with symbolic reasoning techniques.
This course offers participants a comprehensive understanding of how to develop and deploy AI models that combine the strengths of both symbolic AI and deep learning.
Through interactive sessions and hands-on exercises, attendees will learn to create advanced AI systems capable of complex reasoning and decision-making, enhancing their capabilities in AI research, development, and application.
Learning Objectives:
- Understand the fundamental principles of neuro-symbolic AI and its applications.
- Learn to integrate neural networks with symbolic reasoning for enhanced AI solutions.
- Develop skills in designing and implementing neuro-symbolic AI models.
- Explore use cases and applications of neuro-symbolic AI in various industries.
- Gain proficiency in using tools and frameworks for neuro-symbolic AI development.
- Enhance problem-solving skills using neuro-symbolic AI approaches.
Audience:
- AI researchers and developers
- Data scientists and machine learning engineers
- Professionals in AI and machine learning fields seeking advanced knowledge
- Technical managers and team leads overseeing AI projects
- Academics and students specializing in AI and computer science
- Technology consultants and strategists in AI domains
Program Modules:
Module 1: Introduction to Neuro-symbolic AI
- Overview of AI paradigms
- History and evolution of neuro-symbolic AI
- Key concepts and terminology
- Comparison with traditional AI approaches
- Benefits and challenges of neuro-symbolic AI
- Current trends and future outlook
Module 2: Neural Networks and Deep Learning Foundations
- Basics of neural networks
- Deep learning architectures
- Training and optimization techniques
- Role of neural networks in neuro-symbolic AI
- Handling unstructured data
- Performance evaluation metrics
Module 3: Symbolic Reasoning and Knowledge Representation
- Fundamentals of symbolic AI
- Knowledge graphs and ontologies
- Logic-based reasoning techniques
- Integrating symbolic reasoning with neural networks
- Tools for symbolic reasoning
- Case studies in symbolic AI
Module 4: Developing Neuro-symbolic AI Models
- Designing hybrid AI architectures
- Combining neural and symbolic components
- Implementation strategies
- Training neuro-symbolic models
- Testing and validation techniques
- Real-world application examples
Module 5: Tools and Frameworks for Neuro-symbolic AI
- Overview of popular AI frameworks
- Introduction to neuro-symbolic toolkits
- Platform selection criteria
- Integration with existing AI systems
- Open-source and proprietary options
- Hands-on practice with frameworks
Module 6: Applications and Future Directions in Neuro-symbolic AI
- Industry-specific use cases
- AI for complex decision-making
- Neuro-symbolic AI in robotics and automation
- Ethical considerations and AI governance
- Future research directions
- Building a career in neuro-symbolic AI