Last updated 8/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 56m | Size: 1.63 GB
"Exploring Foundation of Generative AI and Chat GPT with Deep Theory and Practical Exercises"
What you’ll learn
Understand the core principles and algorithms behind generative AI, including GANs and VAEs
Develop skills to implement basic generative models using Python and popular AI frameworks.
Explore real-world applications of generative AI in fields like art, music, and data augmentation.
Learn to evaluate the performance and ethical implications of generative AI models.
Gain hands-on experience by building and fine-tuning your own generative AI projects.
Requirements
Here are the requirements or prerequisites for taking the course "Foundation of Generative AI": 1. **Basic Understanding of AI and Machine Learning:** – Familiarity with fundamental AI and machine learning concepts, including supervised and unsupervised learning. 2. **Proficiency in Python Programming:** – Experience with Python programming, including basic libraries such as NumPy and Pandas. 3. **Mathematical Foundations:** – Understanding of basic linear algebra, probability, and statistics. 4. **Familiarity with Deep Learning:** – Knowledge of neural networks, including concepts like backpropagation and gradient descent. 5. **Experience with AI Frameworks:** – Prior experience with AI frameworks such as TensorFlow or PyTorch is recommended but not mandatory. 6. **Curiosity and Enthusiasm:** – A keen interest in exploring and understanding the advancements and applications of generative AI technologies.
Description
### Course Description:Welcome to the "Foundation of Generative AI," a comprehensive course designed for beginners eager to dive into the exciting world of generative models. This course provides a solid foundation in the principles and applications of Generative Artificial Intelligence, covering essential concepts such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).You will start with the basics, learning the core principles of AI and machine learning, before progressing to more advanced topics. The course emphasizes hands-on experience, guiding you through the implementation of generative models using popular AI frameworks like TensorFlow and PyTorch. You’ll explore how these models create art, music, and even realistic human faces, unlocking new creative potentials.Designed for aspiring AI practitioners, data scientists, and curious beginners, this course requires no prior experience in AI, but a basic understanding of Python programming and mathematics will be beneficial. You’ll gain practical skills through real-world projects, learn to evaluate model performance, and understand the ethical implications of generative AI.By the end of this course, you’ll have the knowledge and confidence to build and fine-tune your own generative AI projects, opening doors to innovative applications in various fields. Join us on this journey to unlock the power of generative AI!
Who this course is for
This course is designed for: 1. **Aspiring AI Practitioners:** – Individuals looking to build a solid foundation in generative AI and explore its practical applications. 2. **Data Scientists and Machine Learning Engineers:** – Professionals seeking to enhance their skill set by learning about advanced generative models and their implementations. 3. **Researchers and Academics:** – Students and researchers interested in delving into the theoretical and practical aspects of generative AI for academic pursuits or research projects. 4. **Software Developers and Engineers:** – Developers aiming to integrate generative AI techniques into their projects or develop innovative AI-driven applications. 5. **Tech Enthusiasts and Innovators:** – Enthusiasts who are passionate about AI and wish to stay ahead in the rapidly evolving field of generative technologies. 6. **Creative Professionals:** – Artists, designers, and musicians interested in leveraging generative AI to enhance their creative processes and outputs.
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