Today’s episode will be about deep learning and reasoning. There has been a lot of discussion about the effectiveness of deep learning models and their capability to generalize, not only across domains but also on data that such models have never seen.
But there is a research group from the Department of Computer Science, Duke University that seems to be on something with deep learning and interpretability in computer vision.
References
Prediction Analysis Lab Duke University https://users.cs.duke.edu/~cynthia/lab.html
This looks like that: deep learning for interpretable image recognition https://arxiv.org/abs/1806.10574
Training neural networks faster without GPU [RB] (Ep. 77)
How to generate very large images with GANs (Ep. 76)
[RB] Complex video analysis made easy with Videoflow (Ep. 75)
[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74)
How to cluster tabular data with Markov Clustering (Ep. 73)
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)
Training neural networks faster without GPU (Ep. 71)
Validate neural networks without data with Dr. Charles Martin (Ep. 70)
Complex video analysis made easy with Videoflow (Ep. 69)
Episode 68: AI and the future of banking with Chris Skinner [RB]
Episode 67: Classic Computer Science Problems in Python
Episode 66: More intelligent machines with self-supervised learning
Episode 65: AI knows biology. Or does it?
Episode 64: Get the best shot at NLP sentiment analysis
Episode 63: Financial time series and machine learning
Episode 62: AI and the future of banking with Chris Skinner
Episode 61: The 4 best use cases of entropy in machine learning
Episode 60: Predicting your mouse click (and a crash course in deeplearning)
Episode 59: How to fool a smart camera with deep learning
Episode 58: There is physics in deep learning!
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