What is machine learning?

Brief introduction to deep learning

Machine learning basic concepts

  • Representation: the type of the knowledge (e.g. decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others).
  • Evaluation: the way to evaluate hypotheses (e.g. accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence and others).
  • Optimization: the search process or the way to generate candidate programs (e.g. combinatorial optimization, convex optimization, constrained optimization).

How we get machines to learn

  • Supervised learning: (also called inductive learning) Training data is labeled i.e. includes desired outputs. The example is classification, to predict predefined class of input data.
  • Unsupervised learning: Training data does not include desired outputs. Example is clustering, to divide input into different classes.
  • Semi-supervised learning: Training data includes a few desired outputs.
  • Reinforcement learning: Rewards from a sequence of actions.

An overview of the challenges and limitations of ML

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We solve real-life challenges with innovative, tech-savvy solutions. https://chisw.com/

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