Fundamentals Of Genai For Research And Development

Master basics of GenAI for Research and Development, Data Generation and Prototype creation!

What you'll learn

Learn Generative AI for Research and Development

Learn Data optimization and Problem solving in Generative AI

Have knowledge on data generation in Generative AI

Learn how to use Generative AI in various applications for Research and Development

Requirements

You must have basic knowledge of HTML

Description

You will learn Generative AI for Research and Development in this course. Generative Artificial Intelligence (AI) is a branch of AI focused on creating new, original content based on patterns and structures learned from vast datasets. Unlike traditional AI, which classifies data or makes predictions, generative AI actively produces new outputs, such as text, images, audio, and video. By leveraging deep learning models like Generative Adversarial Networks (GANs) and transformers, generative AI is reshaping fields ranging from art and media to business.Generative AI models are designed to capture complex relationships within data, enabling them to create content that closely resembles human work. GANs, for instance, work by pairing two neural networks: a generator that creates new samples and a discriminator that evaluates them. Transformer models, such as those in OpenAI’s GPT (Generative Pretrained Transformer) series, are foundational to language-based generative AI, excelling at tasks like text generation, translation, and summarization by using massive datasets and contextual learning.Generative AI is undeniably transformative, pushing the boundaries of what machines can create and how they integrate with human work. With ongoing research and responsible regulatory frameworks, generative AI has the potential to be a powerful, positive force in fields as diverse as entertainment, business, and medicine, provided its development is aligned with standards.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Fundamentals

Lecture 2 Generative AI Applications and Fremeework

Lecture 3 Recursive Character Splitter

Section 3: Generative AI Application

Lecture 4 Model Input & Output

Lecture 5 Vector Database

Section 4: Vector Database

Lecture 6 Vector Store Retrievers

Lecture 7 Parameters in GEN AI

Section 5: Advanced Fine-tuning

Lecture 8 ChatInterface Stagement

Lecture 9 Document Loader

Product Managers,Product Researchers