Understanding Generative AI and large language models (LLMs) is key to grasping the future of human-computer interaction. Generative AI refers to systems that can create content—such as text, images, or code—by learning patterns from vast datasets. LLMs, like OpenAI’s GPT models, are a type of generative AI trained on enormous amounts of text data to understand and generate human-like language. These models predict the next word in a sequence, enabling them to write essays, answer questions, translate languages, and more. As their capabilities grow, so does their potential to revolutionize industries from education to entertainment.

Course taught by an expert Artificial Intelligence coder.

2 days - $1,295.00

Prerequisites:

Some basic knowledge of personal computers.

Course Outline 

Foundations of Generative AI
What is Generative AI?
Distinctions: Generative vs. discriminative AI, LLMs vs. traditional ML.
Evolution: Transformer architecture, GPT/DALL-E breakthroughs.
Key Components: Tokens, embeddings, attention mechanisms.

Large Language Models (LLMs) Deep Dive
Transformer Architecture: Self-attention, encoder-decoder frameworks.
Training LLMs: Pre-training (e.g., masked language modeling), fine-tuning, RLHF.
Scaling Challenges: Compute costs, multi-GPU strategies, quantization.

Practical Applications & Tools
Prompt Engineering: Techniques for precise outputs (few-shot, chain-of-thought).
RAG (Retrieval-Augmented Generation): Enhance accuracy with external knowledge.
Toolkit: Text: GPT-4, Claude. Multimodal: Gemini, Stable Diffusion. Deployment: AWS Bedrock, Google Vertex AI.

Ethics, Risks, and Deployment
Bias & Hallucinations: Mitigation strategies (e.g., guardrails).
Regulations: GDPR, AI Act, watermarking.
Use Cases:
Healthcare (diagnostic support).
Finance (fraud detection).
Media (content generation).