Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…

  • Deep learning is a subset of machine learning, which is a subset of AI.
  • Machine learning, as others have said, is a subset of AI.
  • The “learning” part of machine learning means that ML algorithms attempt to optimize along a certain dimension; i.e. they usually try to minimize error or maximize the likelihood of their predictions being true.
  • Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol at Go in early 2016.
  • The initial guesses are quite wrong, and if you are lucky enough to have ground-truth labels pertaining to the input, you can measure how wrong your guesses are by contrasting them with the truth, and then use that error to modify your algorithm.

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@Ronald_vanLoon: “Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…”


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Artificial Intelligence, Machine Learning and Deep Learning

[1606.07792] Wide & Deep Learning for Recommender Systems

Combining wide linear features with deep neural nets - Paper:  Code:

  • Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
  • Deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.
  • In the paper, we present Wide & Deep learning—jointly trained wide linear models and deep neural networks—to combine the benefits of memorization and generalization for recommender systems.
  • Deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank.
  • Abstract: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs.

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@graphific: “Combining wide linear features with deep neural nets – Paper: Code:”


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[1606.07792] Wide & Deep Learning for Recommender Systems