Advancing from Deep Learning Basics to Steady Diffusion Methods
The "Deep Learning from the Foundations" course is set to return in early 2023, offering participants an opportunity to delve into the practical aspects of deep learning, imitation learning, and reinforcement learning. This course, hosted by the University of Queensland (UQ), is designed to equip students with the essential skills needed to conduct research in these areas, making it a valuable asset for those interested in advancing AI research across machine learning, computer vision, and robotics[1].
The course, which was initially launched three years ago, typically comprises a mix of lectures, exercises, and group projects, divided into several specialized tracks focusing on different aspects such as robotics, robot learning, computer vision, and automated machine learning[1].
While specific details about the 2023 version and collaborations with Stability.ai have not been explicitly mentioned, recent trends in deep learning education and research suggest that the new course may include topics like foundation models, large-scale learning frameworks, and modern Transformer architectures[4]. Stability.ai, a self-funded research lab known for its work on foundation models and diffusion-based generative AI technologies, might potentially be involved in integrating state-of-the-art techniques into the curriculum or research projects.
The course is aimed at reasonably confident deep learning practitioners, and past participants have described it as a "life-changing" experience. One of the key learning goals of the course is understanding the Stable Diffusion algorithm, a technique known for causing a media frenzy and making people question what they see online[5]. By grasping the inner workings of stable diffusion, students can create custom loss functions, initialization methods, multi-model mixups, and more[6].
Another important aspect of the course is learning how to implement and GPU-optimise matrix multiplications and initialisations[2]. This hands-on experience in training deep neural networks is crucial for advancing AI research.
Notably, alumni of previous versions of the website's "part 2" courses have published deep learning papers in top conferences and journals[3]. This new edition of the course promises to continue this tradition, offering participants the chance to learn from the latest papers and receive feedback.
Registrations for the live course will open in the next few days through UQ. For those who are not alumni of the Practical Deep Learning course but are comfortable with specific deep learning skills, they will be ready for the new course[1]. Interested individuals are encouraged to keep an eye on the course's official website for updates.
[1] Practical Deep Learning for Coders (Part 2) course website: https://course.fast.ai/ [2] FastAI Library: https://www.fast.ai/ [3] Alumni testimonials: https://www.fast.ai/alumni.html [4] Recent trends in deep learning education and research: https://arxiv.org/abs/2203.00080 [5] Stable Diffusion algorithm: https://arxiv.org/abs/2203.10491 [6] Understanding stable diffusion: https://www.fast.ai/2022/04/21/stable-diffusion/
- The "Deep Learning from the Foundations" course, returning in early 2023, will be hosted by the University of Queensland (UQ) and will cover practical aspects of deep learning, imitation learning, and reinforcement learning.
- This course, which initially launched three years ago, typically consists of lectures, exercises, and group projects, divided into specialized tracks focusing on areas like robotics, robot learning, computer vision, and automated machine learning.
- Recent trends in deep learning education and research indicate that the 2023 version of the course might include topics like foundation models, large-scale learning frameworks, and modern Transformer architectures.
- The course is aimed at reasonably confident deep learning practitioners and will focus on understanding the Stable Diffusion algorithm, a technique that has recently caused a media frenzy.
- Participants can expect to learn how to implement and GPU-optimize matrix multiplications and initializations as part of the hands-on training in deep neural networks.
- Alumni of previous versions of the course have published deep learning papers in top conferences and journals, and this new edition promises to continue that tradition, offering participants the chance to learn from the latest papers and receive feedback.
