Filter:

Accelerate's 2021 Annual Symposium

2021’s Annual Symposium convened researchers from across Cambridge to explore how AI is advancing their work, and what action is needed to support its wider deployment for scientific discovery. Check out videos and Symposium visuals here.

Read More

AI and Large Language Models

A collection of resources for researchers interested in using Large Language Models (LLMs) in their research.

Accelerate has released code for working with Large Language Models in research; you can find it in our large-language-models GitHub repository. This code covers a range of ways to use and tune LLMs, including calling APIs, finetuning models, and creating more complex solutions like RAG. This code is freely available for researchers to build on in their work.

Read More

Data Pipelines for Science

Well-curated and managed data is central to the effective use of AI, in science and elsewhere. How can scientists build the data pipelines they need to accelerate their research with AI? Accelerate Science’s ‘Data Pipelines for Science’ School helps scientists overcome such data pipeline challenges by equipping them with the latest best-practice software techniques.

Read More

Designing Machine Learning For Real-World Challenges

This course - Machine Learning and the Physical World - is focused on how to build machine learning systems that interact directly with the real world. It explores how to create models with a principled treatment of uncertainty, allowing researchers to leverage prior knowledge and provide decisions that can be interrogated.

Read More

Doing Data Science In The Real World

This course - Advanced Data Science - looks at the real world challenges of data science, separating them into three stages: access, assess and address. The stages help in understanding that the data science pipeline is not just about the machine learning methods, but the ethical concerns, the challenges of data management as well as model fitting.

Read More

Introducing Data Science for Research

The Accelerate Programme offers PhDs and postdocs disciplines across Cambridge University the opportunity to participate in a ‘Data for Science’ training course. This structured Accelerate-Cambridge Spark Introducing Data Science for Research will equips scientists with modern practical data analysis skills using Python in a virtual instructor-led accelerated masterclass.

Read More

Machine Learning Accelerator

2021’s Accelerate Science winter school brought together researchers at the interface of machine learning and the sciences to share insights and methods in machine learning that can support scientific discovery.

Read More

Machine Learning for Science Jupyter Notebooks

This gallery brings together Jupyter notebooks produced by participants in the Data Science for Science Residency. They contain information and code about the data science techniques that participants have used in research areas that range from genetics to astronomy.

Read More

NETTS - Networks of Transcript Semantics

The algorithms in this toolbox create a semantic speech graph from transcribed speech. Speech transcripts are short paragraphs of largely raw, uncleaned speech-like text.

Read More

Python Programming For Science

This self-learning module introduces the fundamentals of Python, some of the kinds of data it can handle, and how to store that data. Designed for researchers across disciplines, it supports learners to rapidly learn how to code in the context of working with real-world data. To access the module, register at the link.

Read More

Strategic Research Agenda for AI for science

Accelerate is developing a strategic research agenda in AI for science, which we hope will help drive further progress across projects and institutions. This page gives an overview of our recent workshops convened with the AI for Science community and outputs from these discussions contributing towards an emerging strategic research agenda for the field.

Read More

Understanding Machine Learning and AI

This course - Machine Learning and Adaptive Intelligence - was originally delivered at the University of Sheffield (2011-2015), but has been updated with current material to introduce key concepts and methods in machine learning.

Read More