Deep Learning
The BICI Lab does extensive work with deep learning in our research. For more details, see the Techniques we use.
Deep Learning Network
The purpose of the Deep Learning Network is to unite researchers across Imperial College London working on deep learning by facilitating the sharing of knowledge and experience, and expanding this to the wider deep learning community. There is no formal membership and all are welcome to attend meetings. You may subscribe to the mailing list (for events and general-interest postings) or Slack (for reading groups and general chat).
The group was founded by Kai in 2014 and is run with the help of volunteers both inside and outside of the university; it is not officially endorsed by Imperial College London. For more information about current events or to be added to Slack please contact Pierre.
Reading Group
The reading group happens regularly on Tuesdays at 18:00 in the Data Science Institute, South Kensington campus; we utilise room 1004 or 1009f depending on availability. All reading groups are listed in the calendar below (which can also indicate when the reading group is not running).
Events
Talks
| Date | Speaker | Title | Content |
|---|---|---|---|
| 2017-04-12 | Josh Gordon (Google) | What’s New in TensorFlow? | Slides |
| 2017-09-15 | Andrew Brock (Heriot-Watt University/University of Edinburgh) | SMASH: One-Shot Model Architecture Search through Hypernetworks | Slides |
| 2017-09-08 | Andrew Trask (University of Oxford/DeepMind) | Proposing Encryption and Federation for Safe AI | Slides |
| 2017-09-01 | Katja Hofmann (Microsoft Research) | Minecraft as Playground and Laboratory for Artificial Intelligence | Slides |
| 2017-08-18 | Yang You (UC Berkeley) | Large-Batch DNN Training | Slides |
| 2017-08-08 | Wenzhe Shi (Magic Pony Twitter) | Neural Networks for Image and Video Super-Resolution | Slides |
| 2017-07-03 | Alex Kendall (University of Cambridge) | Geometry in Deep Learning for Computer Vision | Slides |
| 2017-05-26 | Martín Arjovsky (New York University) | On Different Distances Between Distributions and Generative Adversarial Networks Slides (organised with the Creative AI Meetup) | Slides |
Presentations
These are currently being updated…
Links
- Courses:
- Neural Networks for Machine Learning (Geoffrey Hinton)
- Machine Learning (Nando de Freitas)
- Unsupervised Feature Learning and Deep Learning Tutorial (Andrew Ng et al.)
- Deep Learning Summer School 2015 (Yoshua Bengio, Roland Memisevic, Yann LeCun)
- Convolutional Neural Networks for Visual Recognition (Fei-Fei Li, Andrej Karpathy, Justin Johnson)
- Deep Learning for Natural Language Processing (Richard Socher)
- Natural Language Processing (Alexander Rush)
- Réseau Neuronaux (Hugo Larochelle)
- Books:
- Articles:
- Deep Learning (Yann LeCun, Yoshua Bengio, Geoffrey Hinton)
- A Brief Overview of Deep Learning (Ilya Sutskever)
- Hacker’s guide to Neural Networks (Andrej Karpathy)
- Neural Networks, Manifolds and Topology (Christopher Olah)
- A Statistical View of Deep Learning (Shakir Mohamed)
- Deep Learning in Neural Networks: An Overview (Jürgen Schmidhuber)