Intro#
Install and import feedback gadget#
Show code cell source
# @title Install and import feedback gadget
!pip install vibecheck datatops --quiet
from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
return DatatopsContentReviewContainer(
"", # No text prompt
notebook_section,
{
"url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
"name": "neuromatch_neuroai",
"user_key": "wb2cxze8",
},
).render()
feedback_prefix = "W1D3_Intro"
Prerequisites#
Though it is not required, you will benefit from these tutorials more if you have had the opportunity to take linear algebra and multivariate statistics courses in the past, as most of the computations and expressed ideas use the tools from these disciplines.
The intro video for this day in the last iteration of the course (2024) was relatively long and for this year (2025) it has been split into two videos. Please watch the video below to guide you through the tutorials. The additional part of the video is available in the first bonus tutorial (Tutorial 4), which goes into more detail related to the topics you will cover if you complete Tutorials 1-3. You are also kindly advised to revisit the bonus tutorials in your own time. We have added a new (non-Bonus) tutorial all about the exciting topic of Dynamic Similarity Analysis in this year’s iteration of the course. This complements the topic of geometric (spatial) similarity with further similarity measures that take into account temporal information.
We hope you enjoy the topics today. We’ll now move on to Heiko and Niko to guide you through today’s material.
Video#
Intro Video#
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_intro_video")