• Understand fundamental neural network concepts and architectures.
• Apply neural network optimization and hyperparameter tuning techniques.
• Work with neural network architectures for NLP and computer vision.
• Evaluate AI models using performance metrics.
• Understand AI infrastructure and deployment strategies.
• Apply responsible AI and ethical design principles.
• Explore generative AI models and applications.
• Analyze AI research and emerging AI design techniques.
• Develop and present a capstone AI architecture project.

Course Introduction

Module 1: Fundamentals of Neural Networks

1.1 Introduction to Neural Networks

• Basic Concepts of Neural Networks
• Types of Neural Networks
• Limitations of Neural Networks
• Applications of Neural Networks

1.2 Neural Network Architecture

• Architecture Components
• Implementation Steps

1.3 Hands-on: Implement a Basic Neural Network

Module 2: Neural Network Optimization

2.1 Hyperparameter Tuning

• Importance of Hyperparameters
• Tuning Techniques
• Building Neural Networks
• Common Design Patterns

2.2 Optimization Algorithms

• Types of Optimization Algorithms
• Choosing the Right Algorithm

2.3 Regularization Techniques

• Preventing Overfitting
• Other Methods for Model Robustness

2.4 Hands-on: Hyperparameter Tuning and Optimization

• Practical Activities
• Evaluation and Analysis

Module 3: Neural Network Architectures for NLP

3.1 Key NLP Concepts

• NLP Fundamentals
• Tokenization and Embedding

3.2 NLP-Specific Architectures

• RNNs and LSTMs
• Transformer-Based Architectures

3.3 Hands-on: Implementing an NLP Model

• Implementation Steps
• Practical Exercises

Module 4: Neural Network Architectures for Computer Vision

4.1 Key Computer Vision Concepts

• Computer Vision Fundamentals
• Convolutional Neural Networks (CNNs)

4.2 Computer Vision-Specific Architectures

• Specialized Architectures for Computer Vision
• Techniques for Object Detection and Image Segmentation

4.3 Hands-on: Building a Computer Vision Model

• Implementation Steps
• Additional Exercises

Module 5: Model Evaluation and Performance Metrics

5.1 Model Evaluation Techniques

• Evaluation Metrics for AI Models

5.2 Improving Model Performance

• Addressing Overfitting and Underfitting
• Techniques for Performance Optimization
• Cross-Validation and Model Selection

5.3 Hands-on: Evaluating and Optimizing AI Models

• Implementation Steps

Module 6: AI Infrastructure and Deployment

6.1 Infrastructure for AI Development

• Hardware Requirements
• Cloud-Based AI Services

6.2 Deployment Strategies

• Model Deployment Techniques
• Monitoring and Maintenance

6.3 Hands-on: Deploying an AI Model

• Implementation Steps
• Practical Exercises

Module 7: AI Ethics and Responsible AI Design

7.1 Ethical Considerations in AI

• Bias, Fairness, and Accountability

7.2 Best Practices for Responsible AI Design

• Ensuring Ethical AI Development
• Case Studies in AI Ethics
• Explainability and Transparency

7.3 Hands-on: Analyzing Ethical Considerations in AI

• Practical Exercises

Module 8: Generative AI Models

8.1 Overview of Generative AI Models

• Generative Adversarial Networks (GANs)
• Transformer-Based Models

8.2 Generative AI Applications in Various Domains

• GANs for Visual and Multimedia Artifacts
• Transformer-Based Models for Text Generation

8.3 Hands-on: Exploring Generative AI Models

• Building a Simple GAN
• Text Generation with GPT
• Style Transfer and Text-to-Image

Module 9: Research-Based AI Design

9.1 AI Research Techniques

• Research Methodologies in AI
• Interpreting Research Papers

9.2 Cutting-Edge AI Design

• Exploring Recent AI Research

9.3 Hands-on: Analyzing AI Research Papers

• Practical analysis of recent AI research papers
• Applying Research to AI Design

Module 10: Capstone Project and Course Review

10.1 Capstone Project Presentation

• Presentation of Capstone Projects

10.2 Course Review and Future Directions

• Comprehensive Course Review
• Exploring Future Directions in AI

10.3 Hands-on: Capstone Project Development

• Capstone Project Development
• Practical Exercises

Optional Module: AI Agents for Architect

• Understanding AI Agents
• Case Studies
• Hands-On Practice with AI Agents

5 Days

Certification: AI+ Architect Practitioner™

Exam Code: AT-320

Duration: 90 minutes

Questions: 50 multiple-choice/multiple-response questions

• Implement a Basic Neural Network
• Hyperparameter Tuning and Optimization
• Implementing an NLP Model
• Building a Computer Vision Model
• Evaluating and Optimizing AI Models
• Deploying an AI Model
• Analyzing Ethical Considerations in AI
• Exploring Generative AI Models
• Building a Simple GAN
• Text Generation with GPT
• Style Transfer and Text-to-Image
• Analyzing AI Research Papers
• Capstone Project Development
• Optional hands-on practice with AI Agents for Architect

• Architecture Professionals
• Systems Architects & Engineers
• IT Infrastructure Managers
• Business Leaders
• Students & New Graduates
• AI Architects
• AI Solutions Architects
• Cloud AI Architects
• AI System Integrators

• Foundational knowledge of neural networks, including optimization and architecture.
• Ability to evaluate models using performance metrics.
• Willingness to learn about AI infrastructure and deployment processes.