AI+ Architect™

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 design principles.

• Complete a capstone project to apply skills in real-world scenarios.

Learning Outcomes

After completing the program, 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 data.

• Apply hyperparameter tuning, regularization, and optimization algorithms to enhance model performance.

• Evaluate models using accuracy, precision, recall, F1-score, and other metrics.

• Deploy AI models on cloud platforms like AWS, Azure, and GCP.

• Design AI solutions responsibly, considering ethics, fairness, transparency, and accountability.

• Explore cutting-edge research and Generative AI models for practical and creative applications.

Course Modules

Module 1 – Neural Network Fundamentals

• Basics: Nodes, layers, activation functions

• Types: Feedforward, CNNs

• Hands-on: Build a basic neural network for image/text tasks

Module 2 – Neural Network Optimization

• Hyperparameter tuning: learning rate, batch size, layers

• Optimization algorithms: SGD, Adam, RMSprop

• Regularization & robustness: dropout, L1/L2, early stopping, data augmentation

• Hands-on: Optimize a neural network model

Module 3 – Neural Networks for NLP

• NLP Fundamentals: Text classification, sentiment analysis, NER

• RNNs, LSTMs, GRUs, Transformer-based architectures (BERT, GPT)

• Hands-on: Build a Transformer NLP model

Module 4 – Neural Networks for Computer Vision

• CNNs, ResNet, DenseNet, MobileNet

• Object Detection: YOLO, SSD

• Image Segmentation: U-Net, Mask R-CNN

• Hands-on: Build a CNN for image classification

Module 5 – Model Evaluation & Performance

• Metrics: Accuracy, precision, recall, F1-score

• Techniques: Cross-validation, hyperparameter tuning, model selection

• Hands-on: Evaluate and optimize AI models

Module 6 – AI Infrastructure & Deployment

• Hardware: GPUs, TPUs

• Cloud platforms: AWS, Azure, GCP

• Hands-on: Deploy AI models and monitor performance

Module 7 – AI Ethics & Responsible Design

• Ethical considerations: Bias, fairness, transparency, accountability

• Deployment strategies & monitoring

• Hands-on: Analyze ethical implications in AI systems

Module 8 – Generative AI Models

• GANs: CycleGAN, StyleGAN, DCGAN

• Transformer-based models for text generation

• Hands-on: Build a simple GAN & GPT-based text generator

Module 9 – Research-Based AI Design

• AI research methodologies & paper analysis

• Applying research to AI design and architecture

• Hands-on: Analyze recent AI research papers

Module 10 – Capstone Project & Review

• Develop and present a capstone project applying all course modules

• Review key concepts and explore future directions in AI architecture

• Hands-on: Complete and present your final AI project