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GOTO Book Club: How to Leverage Reinforcement Learning

GOTO Book Club: How to Leverage Reinforcement Learning

Thu Feb 4, 2021, in Reinforcement Learning, Talk

In this episode of GOTO’s book club I speak to Rebecca Nugent, Feinberg professor of statistics and data science at Carnegie Mellon univeristy. We talk, at length, about the application of reinforcment learning, specifically how it could be a way of creating truly personalised teaching curricula. It’s a really interesting discussion and it’s great to get someone of Rebecca’s calibre to bounce ideas off.

A Code-Driven Introduction to Reinforcement Learning

A Code-Driven Introduction to Reinforcement Learning

Wed Nov 11, 2020, in Reinforcement Learning, Talk

Notebook link Abstract Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry. In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it - NDC London

Thu Jan 30, 2020, in Data Science, Talk

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it - GOTO Berlin

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it - GOTO Berlin

Thu Oct 24, 2019, by Phil Winder, in Talk, Data Science

Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it - GOTO Chicago

Tue Apr 30, 2019, in Data Science, Talk

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.

Life and Death Decisions: Testing Data Science

Wed Apr 25, 2018, in Data Science, Talk

Abstract We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But the benefits of automation are clear. If businesses use data science to automate decisions they will become more productive and more profitable. So the question becomes: how can we be sure that these algorithms make the best decisions? How can we prove that an autonomous vehicle will make the right decision when life depends on it?

AI Panel of Experts

Tue Mar 6, 2018, in Data Science, Talk

Join the track speakers and invited guests as they discuss where AI is heading and how it’s affecting software today. Enjoyed fielding questions about #DataScience and #AI today at #QConLondon. Great questions and expert speakers, but SMEs are underrepresented in data science. We need more SMEs speaking! pic.twitter.com/Vasi24z3LY — Phil Winder (@DrPhilWinder) March 6, 2018

The Meaning of (Artificial) Life: A Prelude to What is Data Science? - GOTO Berlin

Fri Nov 17, 2017, in Data Science, Talk

Abstract The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.

Research-Driven Development: Improve the Software You Love While Staying Productive

Mon Oct 16, 2017, in Software Engineering, Talk

Abstract Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy. You’ve probably put strategies in place to mitigate the things you don’t like. An anarchic development process like Agile, to reduce the amount of time in meetings. But have you ever thought about the way in which you approach learning and problem solving?