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louiskirsch.com | ||
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niklasriewald.com
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| | | | | The last post was about playing optimal Pokemon games by calculating the value function V(x) using Bellmann equations. Unfortunately, this was impractical, so we need another way to calculate the likelihood of winning games. One solution to this problem is called Value Function Approximation. The idea behind it is to not calculate the value function... | |
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www.assemblyai.com
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| | | | | In this video, we learn about Reinforcement Learning and (Deep) Q-Learning. | |
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blog.research.google
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blog.fastforwardlabs.com
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| | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | ||
