21 Aug Generative AI Tutorial

Generative AI is a subset of Deep Learning. It uses AI neural networks and can process both the labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods. It refers to a class of artificial intelligence models and algorithms designed to create new content. These models can generate text, images, music, and other forms of data that mimic human-created content.

Generative AI Masterclass – 5 hours

Generative AI Tutorial (English)

Generative AI Tutorial (Hindi)

Generative AI applications are built on top of large language models (LLMs) and foundation models. LLMs are deep learning models.

What you’ll learn

Viewers

The Generative AI tutorial is prepared for students, engineers, and professionals. This tutorial will be useful for understanding the AI concepts for Machine Learning and Data Science enthusiasts.

FAQs

Q1: What is Generative AI and how does it differ from traditional AI?

Ans: Generative AI is a branch of artificial intelligence that creates original, synthetic content—such as text, images, code, audio, video, and 3D models—by learning structural patterns from existing datasets. Traditional (or Discriminative) AI focuses on analyzing, classifying, or predicting outcomes based on static data (e.g., identifying spam emails), whereas Generative AI generates entirely new data samples that resemble human output.

Q2: What are Large Language Models (LLMs) and how do Transformer architectures power them?

Ans: Large Language Models (LLMs) are deep learning models trained on vast text corpora to understand, translate, summarize, and generate human language. They are powered by the Transformer architecture, which uses self-attention mechanisms to analyze relationships between words across long textual sequences simultaneously, enabling highly coherent and context-aware responses.

Q3: What is the difference between Fine-Tuning and Retrieval-Augmented Generation (RAG)?

Ans: Both techniques adapt foundation models for specific enterprise use cases:

• Fine-Tuning: Retrains an existing LLM on custom datasets to permanently update its internal neural parameters, altering its tone, style, or task-specific task execution.
• Retrieval-Augmented Generation (RAG): Dynamically retrieves relevant facts from external databases or document repositories at inference time and feeds them to the LLM as context, providing up-to-date, verifiable facts without re-training the base model.

Q4: What are the primary model architectures used for AI image and video generation?

Ans: Visual Generative AI relies heavily on Diffusion Models (e.g., Stable Diffusion, Midjourney, DALL-E 3) and Generative Adversarial Networks (GANs). Diffusion models generate imagery by taking pure random Gaussian noise and iteratively removing noise step-by-step under text prompt guidance until a sharp, high-resolution image emerges.

Q5: What core concepts, tools, and application workflows are covered in Studyopedia’s Generative AI tutorial?

Ans: Studyopedia’s Generative AI tutorial covers foundation models, Transformer mechanics, Prompt Engineering techniques (Few-Shot, Chain-of-Thought), fine-tuning approaches (PEFT, LoRA), RAG architectures, image diffusion models, API integration with OpenAI and Hugging Face, vector databases (Pinecone, ChromaDB), and responsible Generative AI practices.

Generative AI Tutorial – Index