AT-320
AI+ Architect
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Price:
Duration:
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5 Days

Prerequisites
• 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
What you’ll learn in this course
The AI+ Architect certification offers comprehensive training in advanced neural network techniques and architectures. It covers the fundamentals of neural networks, optimization strategies, and specialized architectures for natural language processing (NLP) and computer vision. Participants will learn about model evaluation, performance metrics, and the infrastructure required for AI deployment.
The course emphasizes ethical considerations and responsible AI design, alongside exploring cutting-edge generative AI models and research-based AI design methodologies. A capstone project and course review consolidate learning, ensuring participants can apply their skills effectively in real-world scenarios. This certification equips learners with the knowledge and practical experience to excel in AI architecture and development.
Course Objectives
• Acquire a comprehensive understanding of the fundamental principles behind neural networks and various architectures, including their design and applications across different domains.
• Build a strong mathematical foundation necessary for understanding and developing neural network models, focusing on key concepts that underpin network operations and optimizations.
• Learn advanced techniques for training neural networks effectively, including optimization methods, and gain expertise in evaluating model performance through various metrics.
• Master AI deployment and ethical design, ensuring responsible and effective implementation of AI technologies and use generative AI models to create innovative and ethical AI solutions.
Course Outline
Module 1: Fundamentals of Neural Networks
• 1.1 Introduction to Neural Networks
• 1.2 Neural Network Architecture
• 1.3 Hands-on: Implement a Basic Neural Network
Module 2: Neural Network Optimization
• 2.1 Hyperparameter Tuning
• 2.2 Optimization Algorithms
• 2.3 Regularization Techniques
• 2.4 Hands-on: Hyperparameter Tuning and Optimization
Module 3: Neural Network Architectures for NLP
• 3.1 Key NLP Concepts
• 3.2 NLP-Specific Architectures
• 3.3 Hands-on: Implementing an NLP Model
Module 4: Neural Network Architectures for Computer Vision
• 4.1 Key Computer Vision Concepts
• 4.2 Computer Vision-Specific Architectures
• 4.3 Hands-on: Building a Computer Vision Model
Module 5: Model Evaluation and Performance Metrics
• 5.1 Model Evaluation Techniques
• 5.2 Improving Model Performance
• 5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: AI Infrastructure and Deployment
• 6.1 Infrastructure for AI Development
• 6.2 Deployment Strategies
• 6.3 Hands-on: Deploying an AI Model
Module 7: AI Ethics and Responsible AI Design
• 7.1 Ethical Considerations in AI
• 7.2 Best Practices for Responsible AI Design
• 7.3 Hands-on: Analyzing Ethical Considerations in AI
Module 8: Generative AI Models
• 8.1 Overview of Generative AI Models
• 8.2 Generative AI Applications in Various Domains
• 8.3 Hands-on: Exploring Generative AI Models
Module 9: Research-Based AI Design
• 9.1 AI Research Techniques
• 9.2 Cutting-Edge AI Design
• 9.3 Hands-on: Analyzing AI Research Papers
Module 10: Capstone Project and Course Review
• 10.1 Capstone Project Presentation
• 10.2 Course Review and Future Directions
• 10.3 Hands-on: Capstone Project Development
Further information
If you would like to know more about this course please contact us
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