Certification in Applied Generative AI and Agentic Systems

Neural Networks, Activation Functions, Loss Functions, Gradient Descent, Backpropagation.

Understanding the training process in deep learning.

Key concepts in image and sequence processing.

Applications in NLP (word embeddings, sentiment analysis) and vision (image classification).

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

Introduction to unsupervised learning and data representation

Generating new data by learning latent representations

Precision, Recall, BLEU, Perplexity

Advanced metrics such as Fréchet Inception Distance (FID) for evaluating image quality

Generator vs. Discriminator

Adversarial Training: How the generator improves by being challenged by the discriminator

DCGAN, CycleGAN, StyleGAN, BigGAN, and Progressive GANs

Use cases in super-resolution, image-to-image translation, and data augmentation

Build a basic GAN using TensorFlow or PyTorch

Generate synthetic images or data and apply GANs in creative domains like art generation

Theoretical foundation of Denoising Diffusion Probabilistic Models (DDPM)

How noise is gradually removed to generate realistic images from random noise

DALL·E 2, Stable Diffusion, Imagen

Use cases for AI-generated art, text-to-image, and high-resolution media generation

Implement Stable Diffusion to generate images based on text input

Fine-tune a model to create unique, domain-specific outputs

GPT series, LLaMA, Claude, Falcon, Mistral

Fine-tuning and task-specific LLM applications: text generation, summarization, and translation

Word Embeddings (Word2Vec, GloVe), Named Entity Recognition (NER), and Text Classification

The role of fine-tuning vs. prompt engineering

**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

Build a text generation system using OpenAI API or Hugging Face

Integrate LangChain for external data retrieval and enhance the LLM's responses

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

Text-to-Image, Text-to-Video, and Text-to-Audio generation (e.g., OpenAI Sora, DALL·E 2)

Benefits and challenges of multimodal AI

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

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

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

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

**Autonomous Systems:** What makes an AI system agentic?

**Core concepts:** Self-driven behavior, decision-making, autonomy

**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

**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.

**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).

Ethical concerns in generative AI and autonomous systems: Bias mitigation, misinformation, and hallucinations

**AI Safety**: Handling adversarial attacks and ensuring fairness

**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.