Open Enrollment for Deep Learning Foundations, Availability of Scholarships for Open Source Learning, Plus Additional Opportunities
The highly anticipated course, "From Deep Learning Foundations to Stable Diffusion," is now available for sign-ups. This in-depth program is part 2 of the Practical Deep Learning for Coders series and promises to provide participants with a comprehensive understanding of deep learning concepts, culminating in the implementation of the Stable Diffusion algorithm.
The course is designed for confident deep learning practitioners, those who have completed part 1 of the series, and individuals with some coding experience and familiarity with other machine learning approaches. The 6 to 8-week program starts on October 11th, running weekly on Tuesdays from 6:00 - 8:00PM AEST.
The course covers essential topics, starting with the fundamentals of deep learning, moving on to Convolutional Neural Networks (CNNs), and delving into Generative Models. The course then focuses on Diffusion Models and their mathematical foundations before diving into the practical implementation of Stable Diffusion. Advanced applications and fine-tuning techniques are also explored, providing a well-rounded understanding of this cutting-edge technique in generative modeling.
Participants can expect a blend of lectures, coding labs, assignments, case studies, project ideation, implementation workshops, hands-on coding sessions, and capstone project work. The course also includes access to a community forum where students can ask questions, interact with each other, and engage with alumni. Lessons will be held online, and recordings will be available immediately after each lesson.
It's worth noting that the "Learning Outcomes" section of the signup page may not accurately reflect the course content, as it appears to be copied from the part 1 course. However, an introduction to every topic encountered in the course will be provided for those with less deep learning background but are confident coders.
Scholarships for this course aim to promote inclusivity and diversity in AI education. Eligibility criteria often include students and early-career professionals from underrepresented groups in AI and technology, individuals demonstrating financial need, candidates who have a strong passion or background in AI, machine learning, or related fields, and those from developing countries or regions with limited access to advanced AI training.
In early 2023, free access to the complete recorded course will be opened up to everyone. Groups with free access to the live course include core contributors to open source projects with 50+ stars on GitHub, study group organizers with 6+ members, academics with 10+ citations, open source users with 5+ stars, providers of transcriptions or translations, forum experts with 45 or more "likes" in the last 2 years or 90 at any time, diversity scholars from previous courses, and others.
If you are interested in applying for the course or scholarships, it's best to check the specific institution or platform offering the course for their exact criteria and application deadlines.
- This upcoming course, "From Deep Learning Foundations to Stable Diffusion," is a part of the Practical Deep Learning for Coders series, delivered by fastai, and it promises a comprehensive understanding of deep learning concepts, including Generative Models and the Stable Diffusion algorithm.
- The course, starting on October 11th, is designed for confident deep learning practitioners, those with completion of part 1 of the series, and individuals with coding experience and familiarity with other machine learning approaches.
- The program runs for 6 to 8 weeks, offering a blend of lectures, coding labs, assignments, case studies, project ideation, implementation workshops, hands-on coding sessions, and capstone project work, all accessible online with lesson recordings.
- To promote inclusivity and diversity in AI education, the course offers scholarships to students and early-career professionals from underrepresented groups, individuals demonstrating financial need, candidates with a strong passion for AI, machine learning, or related fields, and those from developing countries or regions with limited access to advanced AI training.
