Showing 6 out of 6 results
The Importance of Reinforcement Learning with Phil Winder
We recently sat down for a short conversation with Phil Winder, multidisciplinary software engineer and data scientist, about his newly released book, Reinforcement Learning.
How a Machine Learning Project Can Go Wrong
Many things that can go wrong in a machine learning project. We had a chat with Phil Winder, data scientist and founder of Winder Research, to dive into what he believes to be the cause of failure in machine learning projects, and whether there’s anything you can do to prevent failing in your own machine learning projects.
Software Technologies that Stand the Test of Time
What software technologies have stood the test of time or have had a massive influence over existing systems? Which do you love or hate? We asked these questions to the GOTO Book Club authors and interviewers that made up the lineup for the second season. Find out what Nicki Watt, CTO/CEO at OpenCredo, Eberhard Wolff, fellow at innoQ, Venkat Subramaniam, founder of Agile Developer, Inc., Liz Rice, chief open source officer at Isovalent, Rebecca Nugent, professor in statistics & data science, Phil Winder, CEO of Winder Research, Hanna Prinz, DevOps & software engineer and Eoin Woods, CTO at Endava, had to say. The conversation was moderated by Rebecca Parsons, CTO at ThoughtWorks.
How to Leverage Reinforcement Learning
Find out what reinforcement learning is, how to leverage its unique features and how to start using it with Phil Winder, author of "Reinforcement Learning" and CEO of Winder Research, and Rebecca Nugent, Stephen E. and Joyce Fienberg Professor of Statistics & Data Science at Carnegie Mellon University.
The Best of GOTO Book Club Part Two
Did you know that your job is probably one of the best out there? You have the opportunity to make change happen in the world. Keep on learning and be considerate of your impact on society as a whole by: Handling security issues wit OAuth 2 Know the 97 Things Every [Java] Programmer Should Know Discover the power of Graph Databases Understand the power of Service Meshes Discover why developers “Love Kotlin” See what you can use “reinforcement learning” for.
Reinforcement Learning - ChatGPT, Playing Games, and More
Reinforcement Learning (RL) trains an agent to maximize a cumulative reward in an environment. It rocketed to fame as the tool to achieve expert level performance in Atari games and the game of Go. It is also used for robotics, autonomous vehicles, process automation, and more recently, making ChatGPT more effective. I will begin with why RL is important and how it supports the applications listed above, including "Reinforcement Learning with Human Feedback", an essential tool used to develop ChatGPT. Then I will discuss how RL requires a variety of computational patterns: data management and processing, large-scale simulations and model training, and even model serving. Finally, I will show how Ray RLlib seamlessly and efficiently supports RL, providing an ideal platform for building Python-based, RL applications with an intuitive, flexible API.