Generative Ai: From Foundations To Advanced Applications

Master the Essentials and Dive Deep into Advanced Generative AI Techniques

What you'll learn

The fundamental principles of generative AI, including key concepts like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs),

Practical experience in building, training, and deploying generative AI models using popular frameworks like TensorFlow and PyTorch, allowing to create AI

Conditional generation, style transfer, and multimodal AI, enabling students to apply generative AI in complex real-world scenarios across various industries.

Explore the ethical implications of generative AI, including bias mitigation, data privacy, and the responsible use of AI technologies,implement AI solutions

Requirements

No AI experience needed. You will learn everything you need to know

Description

Welcome to "Generative AI: From Foundations to Advanced Applications," your comprehensive guide to mastering one of the most transformative fields in artificial intelligence. This course is meticulously crafted to cater to both beginners who are eager to grasp the fundamentals of Generative AI and seasoned professionals looking to deepen their expertise and explore advanced applications.We begin by laying a strong foundation, introducing you to the core concepts that underpin Generative AI. You’ll delve into the mechanics of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models—three of the most influential architectures in the field. Through clear explanations and intuitive examples, you’ll gain a solid understanding of how these models work, what sets them apart, and how they can be applied to generate realistic images, text, and other data types.But understanding the theory is just the beginning. This course places a strong emphasis on practical, hands-on learning. You’ll be guided through the process of building, training, and deploying your own generative AI models using popular frameworks like TensorFlow and PyTorch. Each module includes detailed coding exercises and projects designed to reinforce your learning and give you real-world experience in applying generative AI techniques. By the end of the foundational section, you’ll have a portfolio of projects that showcase your ability to create functional AI applications.As you progress, the course takes you deeper into the more advanced and nuanced aspects of Generative AI. You’ll explore cutting-edge techniques such as conditional generation, which allows for more controlled and targeted outputs; style transfer, which enables you to blend artistic styles with content seamlessly; and multimodal AI, which integrates different data types to create richer, more complex outputs. These advanced topics will equip you with the skills needed to tackle complex challenges and innovate in fields ranging from digital art and media to natural language processing and beyond.In addition to technical skills, this course also addresses the critical ethical considerations that come with developing and deploying AI technologies. You’ll learn about the importance of bias mitigation, data privacy, and transparency in AI, and how to implement best practices that ensure your AI solutions are not only effective but also fair and responsible. This holistic approach ensures that you are well-prepared to create AI applications that contribute positively to society while adhering to ethical standards.By the end of this course, you will have a deep understanding of Generative AI, practical experience with both foundational and advanced techniques, and the ethical insight necessary to navigate the complexities of AI development. Whether you’re aiming to enhance your career, embark on a new project, or simply satisfy your curiosity, this course will provide you with the tools and knowledge to succeed in the exciting world of Generative AI.Join us on this journey and unlock the full potential of Generative AI—your gateway to creating the future of intelligent technology.

Overview

Section 1: Introduction to Generative AI

Lecture 1 Introduction to Generative AI

Section 2: Deep Learning Foundations

Lecture 2 Deep Learning Foundations

Section 3: Variational Autoencoders (VAEs)

Lecture 3 Variational Autoencoders (VAEs)

Section 4: Generative Adversarial Networks (GANs)

Lecture 4 Generative Adversarial Networks (GANs)

Section 5: Transformer-Based Generative Models

Lecture 5 Transformer-Based Generative Models

Section 6: Advanced Topics in Generative AI

Lecture 6 Advanced Topics in Generative AI

Section 7: Ethical Considerations and Real-World Applications

Lecture 7 Ethical Considerations and Real-World Applications

Beginners who are eager to learn the fundamentals of Generative AI