Intensive Teaching · October 2026

The most important things

Before: Join the Iliad Discord and check you can see #intensive-october. That is where you post any material you need to share with participants. If you can't see it, tell Leonard (@leonardbereska on Discord).
During: At 17:10, make sure everyone fills out the feedback form.
After: Fill out the end-of-day log: rate the day and tell us anything that went wrong.
Fill out Who stood out: name anyone who made a positive impression.

66 project proposals are open for review. Stars, a sentence, or both. Read them.

Your days

[B.6]Physics of Deep Learning Wed 14 Oct, leading 26 responses · energy 4.0/5

Overall +1.13 better than expected n = 24

Content +0.88 better than expected n = 24

Clarity +0.63 better than expected n = 24

Motivation +0.42 better than expected n = 24

Exercises +0.54 better than expected n = 24

Readings +0.67 better than expected n = 24

Other activities +0.47 better than expected n = 19

Pace +0.25 too fast n = 24

Difficulty +0.33 too hard n = 24

Novelty +0.58 mostly new n = 24

Read what they wrote28

Worked well10

Pacing ws excellent
I enjoyed the lecture format.
The lecture was super super clear, well explained, and nicely paced.
the structure was really good
This content was exactly what I came to the intensive to learn. I wish we had more lectures on exactly these topics. Readings and exercises were well selected.
HELL YES!! clear, structured, well paced lecture on fascinating and difficult material. this is all i wanted from this day and i wished more of the days were like this. perfect mix of lectures and exercises and discussion too
The schedule and lecture materials were well planned.
The excercises.
quality and clarity of presentation
The format of the delivery was excellent: excellent choices were made about what to cover. Also, the exercises generated productive discussion and advanced my understanding of what is going on.

Would improve12

Was excellent
This was interesting in the sandbox/toy setups used to study mathematical properties of NNs/LLMs but I didn't end up with much optimism about its application/potential to better understanding actual applications to LLM research.
nothing.
There are sessions that feel too open-ended, leaving too many things to explore. Would be nice to switch back to structured lectures.
This was the best lecture of the intensive so far. I wish we had this Tudor teaching for another few days. There is literally nothing I could suggest to improve it.
nothing
It felt like the first two lectures were very dense but the third lecture was actually pretty light. I think the time could be distributed so that there was a little more time spent on the same material in the first two lectures and less on the third.
It would be helpful if lectures included explanations of relevance to AI safety & interpretability, both short-term and in the long-term.
The lecture, exercises, and notes were very well organized. Can't think of anything else to add within the time limit for the topic.
It was wonderful lecture!
nothing comes to mind
More time to digest the many ideas present here.

Changed my mind6

I had no prior knowledge of physics inspired neural nets of deep learning. This lecture inspired me to learn deeper into it.
I enjoyed the content today.
I have more hope that a theory of learning mechanics is possible.
Haven't changed my mind on anything really.
I'm slightly more convinced about learning mechanics being achievable at some point in the future.
unsure of how i can fit in when ive only done my undergrad

The day

10:00 Lecture starts

12:30 Lunch at Mox

13:30 Afternoon session

17:10 Feedback form

17:30 Day ends. Dinner

Resources

Elsewhere