Certification in Applied Generative AI and Agentic Systems

Objective: Align learners with the essential building blocks of AI/ML, focusing on areas that are foundational to Generative AI.

Deep Learning Overview

• Neural Networks, Activation Functions, Loss Functions, Gradient Descent, Backpropagation.
• Understanding the training process in deep learning.

Convolutional Neural Networks (CNNs) & Recurrent Neural Networks (RNNs)

• Key concepts in image and sequence processing.
• Applications in NLP (word embeddings, sentiment analysis) and vision (image classification).

Transformers & Attention Mechanism

• Core concepts behind the Transformer architecture
• Self-Attention, Multi-Head Attention, and Positional Encoding
• BERT vs. GPT: Transfer learning, fine-tuning for task-specific applications

Autoencoders & Variational Autoencoders (VAEs)

• Introduction to unsupervised learning and data representation
• Generating new data by learning latent representations

Evaluation Metrics for Generative Models

• Precision, Recall, BLEU, Perplexity
• Advanced metrics such as Fréchet Inception Distance (FID) for evaluating image quality

Objective: Dive deep into key generative AI models and their applications

2.1 Generative Adversarial Networks (GANs)

Objective: Understand the fundamentals of GANs, how they work, and their applications

GAN Architecture

• Generator vs. Discriminator
• Adversarial Training: How the generator improves by being challenged by the discriminator

Advanced GAN Architectures

• DCGAN, CycleGAN, StyleGAN, BigGAN, and Progressive GANs
• Use cases in super-resolution, image-to-image translation, and data augmentation

Hands-on Project

• Build a basic GAN using TensorFlow or PyTorch
• Generate synthetic images or data and apply GANs in creative domains like art generation

2.2 Diffusion Models

Objective: Understand the new state-of-the-art diffusion models for generating high-quality content.

Diffusion Process

• Theoretical foundation of Denoising Diffusion Probabilistic Models (DDPM)
• How noise is gradually removed to generate realistic images from random noise

Popular Diffusion Models

• DALL·E 2, Stable Diffusion, Imagen
• Use cases for AI-generated art, text-to-image, and high-resolution media generation

Hands-on Project

• Implement Stable Diffusion to generate images based on text input
• Fine-tune a model to create unique, domain-specific outputs

2.3 Large Language Models (LLMs) & Transformers

Objective: Explore how transformers enable generative text models and how to use them effectively

Overview of Large Language Models

• GPT series, LLaMA, Claude, Falcon, Mistral
• Fine-tuning and task-specific LLM applications: text generation, summarization, and translation

NLP Techniques

• Word Embeddings (Word2Vec, GloVe), Named Entity Recognition (NER), and Text Classification
• The role of fine-tuning vs. prompt engineering

LangChain and RAG Architecture

• LangChain: Use of LangChain for integrating LLMs with external data sources, APIs, and databases
• Retriever-Augmented Generation (RAG): Enhance model output by retrieving relevant information from an external knowledge base
• Combining LLMs with retrieval mechanisms to improve response quality

Hands-on Project

• Build a text generation system using OpenAI API or Hugging Face
• Integrate LangChain for external data retrieval and enhance the LLM's responses

2.4 Multimodal AI – s

Objective: Explore AI systems that process multiple modalities (text, image, video, and audio)

Vision-Language Models

• CLIP, BLIP, GPT-4V: Integrating language and vision for richer understanding

Applications in Content Creation

• Text-to-Image, Text-to-Video, and Text-to-Audio generation (e.g., OpenAI Sora, DALL·E 2)
• Benefits and challenges of multimodal AI

Hands-on Project

• Generate high-quality images using Stable Diffusion or DALL·E 2 API
• Experiment with multimodal applications like text-to-video or text-to-audio generation

Objective: Implement practical applications and advanced generative AI models

3.1 Fine-tuning an LLM on Domain-Specific Data –

• Fine-tune an LLM on domain-specific content (e.g., legal, healthcare, or technical domains)
• Use LangChain to connect the model to external knowledge sources

3.2 Building Autonomous Chatbots with Retrieval-Augmented Generation (RAG) –

• Build an intelligent, context-aware chatbot using LangChain and RAG for data retrieval
• Implement multi-turn conversations and integrate APIs to handle domain-specific queries

3.3 AI-Generated Media Projects –

• Use Stable Diffusion, OpenAI Sora, or DALL·E 2 to generate creative media.
• Implement RAG-based models to generate context-aware media or art (e.g., images or videos) by pulling relevant data from knowledge bases

Objective: Focus on building autonomous agents capable of decision-making based on their environment.

4.1 Introduction to Agentic AI & Autonomous Systems

• Autonomous Systems: What makes an AI system agentic?
• Core concepts: Self-driven behavior, decision-making, autonomy

4.2 Reinforcement Learning for Autonomous Decision-Making –

• Reinforcement Learning (RL): Core principles of RL, including agents, environments, states, actions, and rewards.
• RL algorithms: Q-Learning, Deep Q Networks (DQNs), Proximal Policy Optimization (PPO).
• **Hands-on Project:**Build an RL agent to perform simple tasks like game playing or robotic control

4.3 Multi-Agent Systems & Collaboration

• Understanding multi-agent systems (MAS): Agents working collaboratively or competitively.
• Game Theory: Strategic decision-making in multi-agent systems.
• Hands-on Project: Implement a basic multi-agent system using RL for agents to interact and collaborate in an environment.

4.4 LangChain for Autonomous Agents

• Using LangChain for autonomous decision-making: Enable an agent to dynamically retrieve external data to make decisions.
• Create a system that integrates external APIs for real-time decision-making (e.g., an autonomous assistant).

Objective: Address key ethical, security, and deployment issues in generative and autonomous AI systems.

5.1 Ethical AI

• Ethical concerns in generative AI and autonomous systems: Bias mitigation, misinformation, and hallucinations
• AI Safety: Handling adversarial attacks and ensuring fairness

5.2 Deployment Techniques

• Model Optimization: Techniques for reducing model size and improving inference speed.
• Cloud Deployment: How to deploy models at scale using cloud services (AWS, GCP, Azure).
• Edge Deployment: Deploying lightweight models on edge devices (e.g., mobile, IoT devices).
• Hands-on Project: Deploy an LLM-powered application with LangChain on a cloud platform.