Talk - Winder.AI Blog

Industrial insight and articles from Winder.AI, focusing on the topic Talk

CloudNativeX Interview: Reinforcement Learning

CloudNativeX Interview: Reinforcement Learning

Apr 2021, in Reinforcement Learning, Talk

Join Lee Razo and Phil Winder for this comprehensive introduction to Reinforcement Learning, an area of machine learning in which problems are tackled with intelligent agents which take actions to maximize a specified reward. Phil (quite literally) wrote the book on this topic and he takes us through the fundamentals of RL, some common use cases as well as tips on how even a small or mid-sized company can get started with and benefit from RL.

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

Mar 2021, in Reinforcement Learning, Talk

InfoQ · Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning Charles Humble, friend and editor of InfoQ, was kind enough to ask me for an interview to talk more about my new book, in podcast format. From the blurb: In this episode of the InfoQ podcast Dr Phil Winder, CEO of Winder.AI, sits down with InfoQ podcast co-host Charles Humble. They discuss: the history of Reinforcement Learning (RL); the application of RL in fields such as robotics and content discovery; scaling RL models and running them in production; and ethical considerations for RL.

DataTalksClub - Industrial Applications of Reinforcement Learning

DataTalksClub - Industrial Applications of Reinforcement Learning

Feb 2021, in Reinforcement Learning, Talk

Reinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in many practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal. Following the theme of my new book (https://rl-book.com​), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors.

GOTO Book Club: How to Leverage Reinforcement Learning

GOTO Book Club: How to Leverage Reinforcement Learning

Feb 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

Nov 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

Jan 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

Oct 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

Apr 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

Apr 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?