AI+ Architect

Description

AI+ Architect (5 Days)

Program Detailed Curriculum

Executive Summary
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 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.

Module 1

Fundamentals of Neural Networks

1.1 Introduction to Neural Networks
Basic Concepts of Neural Networks: This unit discusses basic neural network components, including nodes
(neurons), layers, and connections (synapses). It also explores the role of activation functions and how they impact
the behavior of the network.
Types of Neural Networks: This unit explores Feedforward Neural Networks (FNNs), focusing on the one-way data
flow from input to output. It also discusses Convolutional Neural Networks (CNNs), outlining key concepts like
convolutional layers and pooling layers used in image processing.
Limitations of Neural Networks: Neural networks require substantial computational resources and large datasets
for effective training. They can suffer from overfitting, making them less generalizable.
Applications of Neural Networks: Neural networks are widely used in image and speech recognition, natural
language processing, autonomous vehicles, medical diagnosis, and financial forecasting.

1.2 Neural Network Architecture
Architecture Components: This unit examines essential architecture elements like weights, biases, activation
functions, loss functions, and gradients. It also explores common activation functions such as ReLU, Sigmoid, and
Tanh, and their roles in non-linear learning.

Building Neural Networks: This unit discusses the process of building a simple neural network, addressing key
considerations like layer arrangement, node count, and activation function choices.
Common Design Patterns: This unit highlights popular design patterns for neural networks, such as ResNet,
DenseNet, and U-Net. It examines where these patterns are used, with examples like image classification with
ResNet or image segmentation with U-Net.

1.3 Hands-on: Implement a Basic Neural Network
Task Description: This unit involves implementing a simple neural network for a basic task, such as image
classification on the MNIST dataset or text processing for sentiment analysis.
Implementation Steps: It guides participants through the setup process, explaining how to construct, train, and
evaluate a basic neural network. It discusses common evaluation metrics, such as accuracy, loss, and confusion
matrix.

Module 2

Neural Network Optimization

2.1 Hyperparameter Tuning
Importance of Hyperparameters: This unit describes the key hyperparameters that influence neural network
performance, including learning rate, batch size, number of layers, and number of epochs. It discusses how these
impact model performance and training times.
Tuning Techniques: This unit explores different hyperparameter tuning methods, including grid search and random
search, highlighting their pros and cons. It also introduces advanced tuning approaches like Bayesian optimization
and genetic algorithms.

2.2 Optimization Algorithms
Types of Optimization Algorithms: This unit discusses popular optimization algorithms like Stochastic Gradient
Descent (SGD), Adam, and RMSprop, explaining their key characteristics, strengths, and weaknesses.
Choosing the Right Algorithm: This unit explores factors to consider when selecting an optimization algorithm,
such as convergence rate, memory requirements, and stability. It provides examples where certain algorithms are
preferred, like using Adam for faster convergence or SGD for larger datasets.

2.3 Regularization Techniques
Preventing Overfitting: This unit discusses the concept of overfitting and how it affects neural network
generalization. It describes common regularization techniques like dropout, L1/L2 regularization, and early stopping.
Other Methods for Model Robustness: This unit explores additional methods to improve model robustness, such as
data augmentation, ensembling, and model checkpoints. It describes practical steps to implement these techniques
in a neural network training pipeline.

2.4 Hands-on: Hyperparameter Tuning and Optimization
Task Description: This unit involves experimenting with hyperparameter tuning and optimization algorithms to
improve a basic neural network’s performance.
Practical Activities: It includes hands-on exercises focused on tuning key hyperparameters (e.g., learning rate, batch
size) and trying different optimization algorithms.
Evaluation and Analysis: It guides participants in evaluating the impact of tuning and optimization on network
performance and suggests strategies for further improvement.

Module 3

Neural Network Architectures for NLP

3.1 Key NLP Concepts
NLP Fundamentals: This unit discusses natural language processing (NLP) basics, focusing on typical tasks such as
text classification, sentiment analysis, named entity recognition, and language translation.
Tokenization and Embedding: This unit describes tokenization techniques, like word-based and subword-based
tokenization, and how they prepare text data for processing in neural networks. It explores word embeddings,
discussing methods like Word2Vec, GloVe, and FastText. The unit also highlights how embeddings represent words
in dense vector forms, allowing AI models to understand semantic relationships.

3.2 NLP-Specific Architectures
RNNs and LSTMs: This unit explores Recurrent Neural Networks (RNNs) and their applications in NLP tasks. It
examines how RNNs handle sequential data and maintain context across sequences. It discusses Long Short-Term
Memory (LSTMs) and Gated Recurrent Units (GRUs), explaining how these architectures address the vanishing
gradient problem in RNNs and improve learning over long sequences.
Transformer-Based Architectures: This unit delves into Transformer-based models, explaining their attention
mechanisms and architectural features. It discusses models like BERT, GPT, and TransformerXL, emphasizing their
impact on NLP tasks.

3.3 Hands-on: Implementing an NLP Model
Task Description: This unit involves implementing a Transformer-based NLP model for a simple text-based task,
such as text classification or sentiment analysis.
Implementation Steps: It guides participants through setting up the environment, tokenization, and building a
basic NLP model. It discusses how to train the model, evaluate its performance, and understand key metrics in NLP.
Practical Exercises: Participants build and train the model, experimenting with hyperparameters and applying
different Transformer-based architectures.

Module 4

Neural Network Architectures for Computer Vision

4.1 Key Computer Vision Concepts
Computer Vision Fundamentals: This unit introduces common computer vision tasks, like image classification,
object detection, and image segmentation. It explains why neural networks are well-suited for these tasks due to
their ability to learn complex patterns from visual data.
Convolutional Neural Networks (CNNs): This unit discusses Convolutional Neural Networks (CNNs), highlighting
key components such as convolutional layers, pooling layers, and fully connected layers. It describes how these
components work together to process and understand images.

4.2 Computer Vision-Specific Architectures
Specialized Architectures for Computer Vision: This unit examines popular computer vision architectures like
ResNet, DenseNet, and MobileNet. It discusses their unique features and explains how they achieve high
performance in various tasks.
Techniques for Object Detection and Image Segmentation: This unit discusses methods for object detection,
including algorithms like Faster R-CNN, YOLO, and SSD. It explains how these methods detect and localize objects
within images. It also explores techniques for image segmentation, including U-Net and Mask R-CNN, discussing
how these architectures segment images into distinct regions.

4.3 Hands-on: Building a Computer Vision Model
Task Description: This unit involves building a simple CNN for an image classification task, such as recognizing
handwritten digits (MNIST) or classifying images in CIFAR-10.
Implementation Steps: It guides participants through setting up the environment, building the CNN, and training it
on a computer vision dataset. It discusses evaluation techniques for computer vision, like accuracy and confusion
matrices.
Additional Exercises: Participants explore object detection and image segmentation techniques, using popular
architectures like YOLO or Mask R-CNN.

Module 5

Model Evaluation and Performance Metrics

5.1 Model Evaluation Techniques
Evaluation Metrics for AI Models: This unit describes common evaluation metrics for AI models, including accuracy,
precision, recall, and F1-score. It discusses when to use each metric, depending on the context and type of task.
Cross-Validation and Model Selection: This unit explains the importance of cross-validation in ensuring a model’s
robustness and avoiding overfitting. It discusses various cross-validation techniques, like k-fold and stratified cross-
validation. It explores strategies for model selection, including the use of validation sets and the concept of model
ensembles.

5.2 Improving Model Performance
Addressing Overfitting and Underfitting: This unit examines common causes of overfitting and underfitting in AI
models. It discusses strategies to mitigate these issues, such as data augmentation, dropout, L1/L2 regularization,
and early stopping.
Techniques for Performance Optimization: This unit discusses methods to improve model performance and
efficiency. It explores techniques like hyperparameter tuning, batch normalization, and the use of more efficient
architectures.

5.3 Hands-on: Evaluating and Optimizing AI Models
Task Description: This unit involves evaluating the performance of a given AI model using various metrics.
Participants apply optimization techniques to improve its accuracy and robustness.
Implementation Steps: It guides participants through model evaluation, discussing how to interpret key metrics. It
demonstrates performance optimization methods, allowing participants to experiment with different strategies to
enhance their models.

Module 6

AI Infrastructure and Deployment

6.1 Infrastructure for AI Development
Hardware Requirements: This unit discusses the hardware requirements for AI development, focusing on the
importance of GPUs and TPUs in accelerating neural network training. It explores the benefits and trade-offs of
different hardware options.
Cloud-Based AI Services: This unit provides an overview of popular cloud-based AI platforms, such as Google Cloud
AI, AWS, and Microsoft Azure. It discusses their features, cost structures, and how they facilitate AI development and
deployment.

6.2 Deployment Strategies
Model Deployment Techniques: This unit explores various strategies for deploying AI models in production
environments. It discusses deployment options like containerization (e.g., Docker), serverless computing, and edge
deployment.
Monitoring and Maintenance: This unit examines techniques for monitoring deployed AI models, discussing how to
ensure they perform consistently over time. It explores the use of monitoring tools, retraining strategies, and
mechanisms for detecting model drift or performance degradation.

6.3 Hands-on: Deploying an AI Model
Task Description: This unit involves deploying a simple AI model on a cloud-based platform and monitoring its
performance over time.
Implementation Steps: It guides participants through the deployment process, including setting up a cloud-based
environment, deploying the model, and implementing basic monitoring techniques.
Practical Exercises: Participants deploy a model using a cloud platform and experiment with various deployment
strategies and monitoring tools.

Module 7

AI Ethics and Responsible AI Design

7.1 Ethical Considerations in AI
Bias, Fairness, and Accountability: This unit discusses the potential for bias in AI models and explores strategies to
ensure fairness and accountability. It examines how biases can arise from data collection, model design, or other
factors, and the impacts these biases can have on various groups.
Explainability and Transparency: This unit describes the importance of explainable AI and discusses various
techniques to improve model transparency. It explains why explainability is critical for building trust and
accountability in AI systems.

7.2 Best Practices for Responsible AI Design
Ensuring Ethical AI Development: This unit discusses guidelines for responsible AI development and strategies to
mitigate ethical risks. It addresses ethical concerns in AI design, like privacy, data protection, and human impact. It
explores principles for responsible AI design, such as fairness, accountability, transparency, and safety. It also
discusses frameworks for ethical AI development from organizations like IEEE and AI4People.
Case Studies in AI Ethics: This unit examines real-world case studies illustrating ethical issues in AI development. It
explores scenarios where AI systems caused harm or controversy due to biases or lack of transparency, and how
these issues were addressed. It discusses lessons learned from these case studies and how they can inform
responsible AI design and deployment.

7.3 Hands-on: Analyzing Ethical Considerations in AI
Task Description: This unit involves analyzing a given AI model or system for ethical considerations and proposing
solutions to address potential biases or fairness issues.
Practical Exercises: Participants identify potential biases in an AI system, discuss their impact, and propose
strategies for mitigating them. This could involve analyzing a pre-trained model, examining its outputs, and
suggesting improvements for fairness and transparency.

Module 8

Generative AI Models

8.1 Overview of Generative AI Models
Generative Adversarial Networks (GANs): This unit discusses the basic architecture of GANs, explaining the roles of
the generator and discriminator and how they work together to create realistic synthetic data. It explores various
types of GANs, including CycleGAN, StyleGAN, and DCGAN.
Transformer-based Models: This unit introduces the Transformer architecture and its key components, such as
multi-head attention and position encoding. It explores the application of Generative Pre-Trained (GPT) models for
text generation and natural language processing tasks.

8.2 Generative AI Applications in Various Domains
GANs for Visual and Multimedia Artifacts: This unit discusses use cases for GANs in creating realistic images,
videos, and artwork. It explores how GANs are used in industries like fashion, film, and video game design, and the
creative possibilities they offer.
Transformer-based Models for Text Generation: This unit explores applications of GPT models in generating textual
content from diverse sources, including website articles, press releases, and whitepapers. It discusses the potential
for automation and creative uses in content generation.

8.3 Hands-on: Exploring Generative AI Models
Building a Simple GAN: This unit involves constructing a basic GAN from scratch to generate synthetic images from
a simple dataset (e.g., MNIST, CIFAR-10). It introduces popular frameworks and libraries for GAN development, such
as TensorFlow and PyTorch.
Text Generation with GPT: This unit involves using a pre-trained GPT model to generate text based on given
prompts. Participants experiment with text generation to create different types of content, such as stories, articles, or
dialogues.
Style Transfer and Text-to-Image: This unit involves implementing a basic style transfer model with GANs, allowing
participants to blend artistic styles with image content. It explores popular style transfer techniques and their
applications in art and design.

Module 9

Research-Based AI Design

9.1 AI Research Techniques
Research Methodologies in AI: This unit discusses common research methodologies in AI architecture design,
including experimental studies, case studies, and theoretical analyses. It explains the process of conducting AI
research and how to approach experimentation and hypothesis testing.
Interpreting Research Papers: This unit explores techniques for reading and understanding AI research papers. It
discusses how to extract key information from research papers, including the introduction, methodology, results,
and discussion sections.

9.2 Cutting-Edge AI Design
Exploring Recent AI Research: This unit explores recent AI research in the context of architecture design. It reviews
recent trends and advancements in AI, discussing their implications for AI design and development. It discusses new
developments in neural network architectures, optimization techniques, and applications of AI in various domains.

Applying Research to AI Design: This unit discusses how research informs the design and evolution of AI
architectures. It explores practical applications of research findings and how to apply them in real-world AI projects.
It encourages participants to stay updated with the latest AI research and to apply new concepts and techniques to
their AI projects.

9.3 Hands-on: Analyzing AI Research Papers
Task Description: This unit involves analyzing recent research papers on AI architecture design and discussing their
findings in a group setting. Participants work in teams to explore the implications of the research and suggest
applications for AI design.
Practical Exercises: Participants select a recent AI research paper, analyze its methodology and findings, and
present their interpretations and conclusions to the class. The exercise encourages discussion and collaborative
analysis of AI research.

Module 10

Capstone Project and Course Review

10.1 Capstone Project Presentation
Presentation of Capstone Projects: This unit involves participants presenting their capstone projects,
demonstrating their understanding of AI architecture design and their ability to apply concepts from the course.
Participants explain their projects, describe their approach, and discuss the results.

10.2 Course Review and Future Directions
Comprehensive Course Review: This unit reviews key concepts covered throughout the course. It summarizes the
critical aspects of AI architecture design, optimization, computer vision, NLP, generative AI, ethics, and research.
Exploring Future Directions in AI: This unit discusses emerging trends and the future of AI architecture design. It
explores new directions in AI research, technological advancements, and their potential impact on AI development.

10.3 Hands-on: Capstone Project Development
Task Description: This unit involves participants working on their capstone project, incorporating the knowledge
and skills gained throughout the course. It provides an opportunity for participants to apply concepts and
techniques from previous modules and receive feedback from peers and instructors.
Practical Exercises: Participants develop their capstone projects, with guidance from instructors, and work on
refining their presentations. They receive feedback and suggestions for improvement, allowing them to finalize their
projects for presentation.
AI+ Architect Detailed Curriculum
Date Issued: 20/01/2024
Version: 1.1