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this week's favorite
As artificial intelligence continues its rapid progress, equaling or surpassing human performance on benchmarks in an increasing range of tasks, researchers in the field are directing more effort to the interaction between humans and AI in domains where both are active. Chess stands as a model system for studying how people can collaborate with AI, or learn from AI, just as chess has served as a leading indicator of many central questions in AI throughout the field’s history.
We want to make deep learning more efficient and we want to help popularize the use of self-supervised methods to understand and filter raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to analyze and visualize datasets as well as for selecting a core set of samples.
In this article, we cover 18 machine learning practices that we think will help you achieve that. These practices are divided into 5 sections. Each section is composed of several tips and tricks that may help you build awesome machine learning applications.
We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of domains: what personalized treatment should be administered to a patient, what discount should be offered to an online customer, and other important decisions that can greatly impact people’s lives.
In this article I will be discussing some of the challenges I faced as a beginning researcher in quantum machine learning (QML) and how TensowFlow Quantum (TFQ) and Cirq enabled me and can help other researchers investigate the field of quantum computing and QML. I have previous experience with TensorFlow, which made the transition to using TensorFlow Quantum seamless. TFQ proved instrumental in enabling my work and ultimately my work utilizing TFQ culminated in my first publication on quantum reinforcement learning in the 16th AIIDE conference. I hope that this article helps and inspires other researchers, neophytes and experts alike, to leverage TFQ to help advance the field of QML.