About Practical Deep Learning for Coders (a UQ collaboration with fast.ai): Part 2

Deep learning is a computing technique to extract and transform data - with use cases ranging from human speech recognition to animal imagery classification - by using multiple layers of neural networks. Building on Part 1 of the course run from April to June 2022, we invite you to join us for Part 2, where we take you to the next level of detail with deeper dives and more advanced topics. This course aims to equip attendees with the practical skills to develop their own advanced Deep Learning models and apply them to real life problems and data sets. 

 Course Presenter
Honorary Professor Jeremy Howard
Jeremy Howard has been using and teaching machine learning for around 30 years. He started using neural networks 25 years ago. During this time, he has led many companies and projects that have machine learning at their core, including founding the first company to focus on deep learning and medicine, Enlitic, and taking on the role of President and Chief Scientist of the world's largest machine learning community, Kaggle. He is the co-founder - along with Dr. Rachel Thomas - of fast.ai, the organisation behind the deep learning framework on which this course is based.


Topics Covered:

This course will cover more advanced techniques than Part 1 and go into much more detail about how they work "under the hood". We'll be creating many models from scratch, and studying how they behave in detail. Deep learning is a fast moving field, and we may study new papers as they come out -- so we can't guarantee ahead of time exactly what topics will be included. The course will include: 

  • Image and text generative models 

  • Object detection 

  • Clustering 

  • Semi-supervised learning 

  • Image search 

  • Transformers and ResNets from scratch 

Delivery mode:

This 8-week short course will be offered exclusively online as a set of live-streamed lectures in the AEST (UTC/GMT +10) timezone, with recordings available for review and delayed viewing.

Course participation is supported by online discussion forums, and the presenter may also add additional sessions such as topical deep-dives at his discretion. 

Live session dates (also listed at the bottom of this page):

Tuesday evenings | 6:00 - 8:00pm AEST (UTC/GMT+10)

Week 1: 11th October
Week 2: 18th October
Week 3: 25th October
Week 4: 1st November
Week 5: 8th November
Week 6: 15th November
Week 7: 22nd November
Week 8: 29th November

Cost to enrol: 

Professional / external attendee - $525
A 10% discount will be available for group enrolments of 10pax. or more
UQ Staff or Student / concession (proof of ID required) - $175
Students enrolled outside of UQ will also be entitled to this pricing. NB. For UQ Staff, please enrol using your uq.edu.au email address as proof. 

Enrolment and payment will be open until 5pm Friday 7th October 2022.  Late registrations will be considered but cannot be guaranteed.

Learning Outcomes:

After completing this course, participants will be able to:

  1. Train models to achieve state-of-the-art results in computer vision, natural language processing, tabular data and prediction, and recommender systems
  2. Turn your models into web applications and deploy them
  3. Understand why and how deep learning models work, and use that knowledge to improve their accuracy, speed and reliability
  4. Select the most appropriate deep learning techniques for given problems
  5. Implement neural network training methods from scratch
  6. Reflect on the ethical implications of your work - to ensure you’re making the world a better place with your skills

Participant Profile:

  • Familiarity with Python, git and bash/shell basics 

  • Familiarity with content covered in Part 1 - this is available for study and review on http://course.fast.ai


Code of Conduct: 

All course attendees, presenters, staff and volunteers are required to agree with the following Code of Conduct. Organisers (UQ) will enforce this code throughout the event. We are expecting cooperation from all participants to help ensure a safe environment for everybody: Be excellent to each other, show empathy, and help make this a safe space to explore tangible, equitable solutions.

UQ is dedicated to providing a harassment-free experience for everyone, regardless of gender, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, experience, or religion (or lack thereof). We do not tolerate harassment of course participants in any form. Sexual language and imagery is not appropriate for any course venue(s), including anything communicated during the content of the virtual broadcasting of the course, on social media or any other online outlets. Course participants violating these rules may be sanctioned or expelled without a refund at the discretion of the organisers, and appropriate legal action will be taken against violators where applicable.

 

FAQs

1. Will a recording of the course sessions be made available after?
Registered participants will have immediate access to the recordings. Public access to the course lectures and materials is typically available 2 or 3 months after completion.

2. Will the live stream of the course (i.e. weekly sessions) only run in the Australian AEST timezone? 
Yes, the weekly sessions will run between 6:00 - 8:00PM AEST (UTC/GMT +10). Access to the live stream will be provided to all registered and paid students/course attendees closer to the course start date. Please check your inbox using the same email address which you had registered with.

3. Are the fees in AUD?
Yes 

4. Can I enrol if I'm not in Australia?
Yes, but please be aware that your timezone may impact your ability to participate live.

5. Will course materials be shared after the conclusion of the course? 
No slideware or course notes will be distributed. However, participants are able to independently access a free interactive version of the presenter's text book of the same name. As stated above (see: 1. of the FAQs section), a recording of the live stream will also be recorded and can be accessed at a later date.