AI+ Architect
The AI+ Architect course offers in-depth training on designing and optimizing neural networks for various applications, including NLP, computer vision, and generative AI. Participants will learn key concepts such as hyperparameter tuning, model evaluation, and AI deployment strategies, with a strong focus on responsible AI design. The course combines theoretical knowledge with hands-on labs to build, optimize, and deploy AI models effectively. It’s ideal for those seeking AI architect certification and aiming to advance their skills in AI infrastructure and design. Perfect for professionals looking for comprehensive AI training for architects.
All students receive:
- One-Year Subscription (with all updates)
- High-Quality E-Book
- Al Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Exam Study Guide
- Proctored Exam with one Free Retake
Check out our full list of training locations and learning formats. Please note that the location you choose may be an Established HD-ILT location with a virtual live instructor.
Train face-to-face with the live instructor.
Access to on-demand training content anytime, anywhere.
Attend the live class from the comfort of your home or office.
Interact with a live, remote instructor from a specialized, HD-equipped classroom near you. An SLI sales rep will confirm location availability prior to registration confirmation.
All Sunset Learning dates are guaranteed to run!
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Prerequisites:
Required:
- A foundational knowledge on neural networks, including their optimization and architecture for applications.
- Ability to evaluate models using various performance metrics to ensure accuracy and reliability.
- Willingness to know about AI infrastructure and deployment processes to implement and maintain AI systems effectively.
Recommended:
- AI+ Executive or AI+ Everyone
Target Audience:
- Technical Architect
- IT Professional
Course Objectives:
- Gain a solid foundation in neural network principles, including architecture, implementation, and optimization techniques such as hyperparameter tuning, regularization, and various optimization algorithms.
- Develop expertise in applying neural network models to specific domains like natural language processing (NLP) and computer vision, through hands-on projects and practical applications.
- Learn to assess model performance using various evaluation techniques, and improve model accuracy and efficiency by implementing advanced optimization strategies.
- Acquire knowledge of the infrastructure required for AI development and deployment, and practice deploying AI models in real-world scenarios.
- Understand ethical considerations in AI design, implement responsible AI practices, and explore generative AI models and contemporary AI research techniques through practical, hands-on activities.
Course Outline:
Fundamentals of Neural Networks
- Introduction to Neural Networks
- Neural Network Architecture
- Hands-on: Implement a Basic Neural Network
Neural Network Optimization
- Hyperparameter Tuning
- Optimization Algorithms
- Regularization Technique
- Hands-on: Hyperparameter Tuning and Optimization
Neural Network Architectures for NLP
- Key NLP Concepts
- NLP-Specific Architectures
- Hands-on: Implementing an NLP Model
Neural Network Architectures for Computer Vision
- Key NLP Concepts
- NLP-Specific Architectures
- Hands-on: Implementing an NLP Model
Model Evaluation and Performance Metrics
- Model Evaluation Techniques
- Improving Model Performance
- Hands-on: Evaluating and Optimizing AI Models