AI+ Architect™
About Course
Executive Summary
The AI+ Architect™ program provides in-depth training in neural networks and AI architecture.
– Master fundamental and advanced neural network concepts, optimization strategies, and model evaluation.
– Gain expertise in NLP, Computer Vision, and Generative AI architectures.
– Learn AI deployment, infrastructure, and ethical AI design principles.
– Complete a capstone project to apply AI architecture skills in real-world scenarios.
Learning Outcomes
Upon completion, participants will be able to:
– Build, optimize, and deploy neural networks for real-world AI applications.
– Implement CNNs, RNNs, LSTMs, and Transformer-based models for image and text processing.
– Apply hyperparameter tuning, regularization, and optimization algorithms to improve AI model performance.
– Evaluate AI models using accuracy, precision, recall, F1-score, and advanced evaluation metrics.
– Deploy AI solutions on cloud platforms such as AWS, Azure, and GCP.
– Design responsible AI systems with fairness, transparency, accountability, and ethical considerations.
– Explore advanced Generative AI models and emerging AI research trends.
Course Modules
Module 1 – Neural Network Fundamentals
– Introduction to neural networks and AI architectures
– Nodes, layers, activation functions, and learning processes
– Types of neural networks: Feedforward, CNNs, and Deep Learning basics
– Hands-on: Build a basic neural network for image and text applications
Module 2 – Neural Network Optimization
– Hyperparameter tuning techniques
– Optimization algorithms: SGD, Adam, RMSprop
– Regularization methods: Dropout, L1/L2, early stopping
– Data augmentation and model robustness strategies
– Hands-on: Optimize and improve neural network performance
Module 3 – Neural Networks for NLP
– NLP fundamentals and text processing
– Text classification, sentiment analysis, and named entity recognition
– RNNs, LSTMs, GRUs, and Transformer-based models (BERT, GPT)
– Hands-on: Build and test a Transformer NLP model
Module 4 – Neural Networks for Computer Vision
– CNN architectures: ResNet, DenseNet, MobileNet
– Object detection techniques: YOLO, SSD
– Image segmentation models: U-Net, Mask R-CNN
– Hands-on: Develop a CNN-based image classification system
Module 5 – Model Evaluation & Performance
– AI evaluation metrics: Accuracy, precision, recall, F1-score
– Cross-validation and model selection techniques
– Hyperparameter tuning and performance enhancement
– Hands-on: Evaluate and optimize AI models
Module 6 – AI Infrastructure & Deployment
– AI infrastructure using GPUs and TPUs
– AI deployment on AWS, Azure, and Google Cloud Platform
– Monitoring AI model performance in production environments
– Hands-on: Deploy AI applications on cloud platforms
Module 7 – AI Ethics & Responsible Design
– Ethical AI principles and responsible AI development
– Bias, fairness, transparency, and accountability in AI systems
– AI governance and monitoring strategies
– Hands-on: Analyze ethical implications in AI solutions
Module 8 – Generative AI Models
– Introduction to GANs and generative models
– CycleGAN, StyleGAN, and DCGAN architectures
– Transformer-based text generation models
– Hands-on: Build a simple GAN and GPT-based text generator
Module 9 – Research-Based AI Design
– AI research methodologies and paper analysis
– Applying research findings to AI architecture design
– Emerging trends in AI innovation and development
– Hands-on: Analyze and discuss recent AI research papers
Module 10 – Capstone Project & Review
– Develop and present a complete AI architecture project
– Apply concepts from all course modules in a practical scenario
– Review key concepts and explore future AI trends
– Hands-on: Final capstone project presentation
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