Language: English | Size: 1.78 GB | Duration: 3h 36m
Master the Basics of Generative AI and Explore Real-World Applications
What you'll learn
Understand the fundamentals of Generative AI and its real-world applications
Differentiate between various types of Generative AI models, including GANs and other architectures
Set up an environment to experiment with and implement Generative AI models
Train, evaluate, and fine-tune Generative AI models for tasks like image generation and text creation
Requirements
No prior experience in AI or machine learning is required. This course is beginner-friendly
Basic understanding of Python programming is recommended, but not mandatory.
Description
Unlock the potential of Generative AI with this comprehensive course designed to take you from foundational concepts to advanced, real-world applications. In this course, you'll dive deep into the cutting-edge field of Generative AI, exploring everything from basic AI models to sophisticated frameworks like GANs, VAEs, and RNNs, while gaining hands-on experience building your own generative models.Throughout the course, you'll learn how to implement popular models, train them for various tasks, and fine-tune pre-trained models using advanced techniques like transfer learning and attention mechanisms. Whether you're interested in generating realistic images, text, or even pioneering research, this course will equip you with the skills and knowledge to excel.Key topics include:Fundamentals of Generative AI: What it is, types of models, and real-world applications.Generative Adversarial Networks (GANs): Architecture, training, and image generation applications.Variational Autoencoders (VAEs): Building models for data generation and real-world use cases.Recurrent Neural Networks (RNNs): Sequence generation using RNNs and LSTMs, with hands-on text generation.Transfer Learning in Generative AI: Fine-tuning pre-trained models for generative tasks in image and text.Attention Mechanisms: Implementing self-attention in generative models and understanding Transformer models.Ethical Considerations: Exploring fairness, bias, privacy, and responsible use of Generative AI.This course is packed with hands-on projects and exercises, allowing you to apply the knowledge in real-world contexts. By the end of the course, you'll have a robust portfolio of AI projects and the ability to develop and implement your own Generative AI solutions, setting you up for success in fields like AI research, machine learning engineering, and data science.Who is this course for:AI enthusiasts looking to understand Generative AI from the ground up.Developers, data scientists, and machine learning engineers seeking to enhance their skills.Professionals interested in the practical applications of AI in industries like healthcare, finance, and media.Join now and become a master in Generative AI, ready to solve real-world challenges and contribute to the future of AI innovation!
