Generative AI Foundations

Edureka via Coursera

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Overview

Welcome to the "Generative AI Foundations" course, a learning journey designed to equip you with a deep understanding of Generative AI, its principles, methodologies, and applications across various domains. By the end of this course, you will have acquired the knowledge and skills to: - Grasp the foundational concepts and technical intricacies of Generative AI, including its advantages and limitations. - Apply Generative AI for code generation, enhancing your programming efficiency and creativity in Python and other languages. - Master...

Syllabus

Gen AI Foundations
- This module is designed to equip learners with a solid understanding of Generative AI principles, models, and applications, setting the stage for more advanced exploration. Through engaging lessons that include videos on the overview of Generative AI, its principles, understanding its models, and the advantages and disadvantages, along with practical applications like code generation and prompt engineering, participants will gain valuable insights. This module also emphasizes ethical considerations and includes practice assignments and discussion prompts to encourage active learning and application of concepts. Whether you're new to AI or looking to enhance your understanding of Generative AI's capabilities, this module provides the essential knowledge base to start your journey.
Autoencoders and GANs
- This module is crafted to provide an in-depth understanding of how these models function, their architectural nuances, and their wide array of applications in the tech industry. Starting with the basics of Autoencoders, learners will explore the workings and variations of these networks, including Variational Autoencoders (VAEs), and understand their significance in data compression and generative tasks. The journey continues with an exploration of GANs, from their foundational architecture to the nuances of training and the exploration of their diverse variants. Through practical assignments, engaging video content, and focused readings, participants will gain hands-on experience working with these models, culminating in a deeper comprehension of their capabilities and limitations.
Language Models and Transformer-based Generative Models
- This module provides an in-depth exploration of Language Models and Transformer-based Generative Models, foundational elements in natural language processing and artificial intelligence. Starting with an overview of language models, it progresses to cover the revolutionary transformer architecture, detailing its attention mechanism and various advanced models. The module then shifts focus to groundbreaking models such as GPT and BERT, examining their development, capabilities, and the wide array of applications they enable in the AI domain. Concluding with comprehensive assessments, including practice and graded assignments on cutting-edge topics like VAEs and GANs, the module offers a holistic understanding of how these technologies drive innovation in AI research and applications.
Course Wrap-up and Assessment
- This final module is designed to consolidate the knowledge and skills learners have acquired throughout the course. It starts with a Practice Project, encouraging learners to apply their understanding in a hands-on manner, thus bridging the gap between theoretical knowledge and practical application. Following this, the module offers a Graded Assignment on Gen AI Fundamentals, aimed at rigorously evaluating the learners' grasp of the key concepts, techniques, and applications explored in the course.