Generative AI Fundamentals

Explore core principles, tools, and ethical use of Generative AI, and discover its real-world impact and foundational models powering creative applications.

Explore the fundamentals of generative AI, its key modalities, advanced capabilities, and essential ethical considerations shaping responsible AI development.

Explore real-world applications of Generative AI, including LLM-assisted coding, and learn to prompt, validate, and improve AI-generated code and tests.

Discover foundation models: large, versatile AI systems trained on massive datasets that generalize across tasks, surpassing traditional models in scalability and adaptability.

Learn to build text classifiers with foundation models, using zero-shot and few-shot prompt engineering for tasks like sentiment and spam detection, and evaluate classifier accuracy.

Learn how generative AI creates new data with architectures like Transformers and diffusion models, and how training enables creativity, reasoning, and task-specific abilities.

Learn how to assess generative AI using human evaluation, exact metrics, AI judges, and benchmarks, ensuring robust performance for open-ended, probabilistic model outputs.

Learn practical techniques to evaluate generative AI models, from Exact Match to ROUGE, semantic similarity, code correctness, Pass@k, and LLM-as-a-Judge scoring.

Explore neural networks from perceptrons to multilayer perceptrons, learning how they adapt via training, gradient descent, and backpropagation to solve complex AI tasks.

Learn to implement neural networks in PyTorch by mastering tensors, model building, loss functions, optimizers, data loading, and complete training loops for practical machine learning.

Explore AI model interpretability and ethics, including bias, misinformation, environmental impact, and fairness for responsible development and deployment of AI technologies.

Discover how LLMs generate text token by token using Hugging Face's Transformers, from tokenization to model use, and explore hands-on demos with efficient generation methods.

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Provides hands-on practice in iteratively developing a role-based prompt to create a believable historical figure persona.

Learn to adapt foundation models for specialized tasks using prompt engineering, RAG, fine-tuning, model compression, and agentic AI tools for efficient, tailored AI solutions.

Learn to efficiently customize foundation models with PEFT and SFT, using LoRA to teach LLMs new skills like spelling via hands-on data preparation and fine-tuning.

Explore post-training for foundation models, including supervised and preference fine-tuning, to align AI with human values, improve usability, and ensure responsible interactions.

Learn to fine-tune LLMs for structured tasks like counting and spelling using GRPO and LoRA, applying reinforcement-based reward functions for targeted skill improvements.