Neural Structured Learning - Part 2: Training with natural graphs

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Welcome to episode 2 of our short series on Neural Structured Learning! In this episode, Software Engineer Arjun Gopalan discusses what natural graphs are and how they can be used to train neural networks.

Graphs are powerful data structures and can be used to represent data that most of us interact with every day. In this video, you’ll learn how to leverage graph-based data to effectively train neural networks for any machine learning task.

Links:

  1. TensorFlow
  2. Neural structured learning
  3. training with natural graphs
  4. natural graphs
  5. co-occurrence graph
  6. citation graph
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Комментарии
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I wanted to solve a simple classification problem, but the documentation is not as simple as the code shown in the video. "make_datasets" function does not even exist. Doc examples are so problem specific that I cannot understand what to apply to solve my problem.
Poorly documented...

hayleecs
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I only experienced performance decline using this regularization method on my data. Anyone has similar or opposite experience?

ThePritt
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Thanks for the video. Very informative video . Just wanted to know, during the inference is it possible to obtain the inferred graph as an output ? For example, in the form of adjacency matrix ?

hegdelabs