machine learning ml

Stanford University

Here are the best resources to pass machine learning ml. Find machine learning ml study guides, notes, assignments, and much more.

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Python/Numpy Tutorial.
  • Python/Numpy Tutorial.

  • Presentation • 39 pages • 2022
  • Available in package deal
  • Text editor/IDE options.. (don’t settle with notepad) • PyCharm (IDE) • Visual Studio Code (IDE) • Sublime Text (IDE) • Atom • Notepad ++/gedit • Vim (for Linux)
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  • $5.49
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Neural Networks
  • Neural Networks

  • Summary • 6 pages • 2022
  • Available in package deal
  • Deep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation
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  • $5.99
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Neural Networks
  • Neural Networks

  • Class notes • 21 pages • 2022
  • Available in package deal
  • Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples
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  • Free
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Kernels, SVM.
Kernel Methods
Probability Theory
  • Probability Theory

  • Presentation • 100 pages • 2022
  • Available in package deal
  • Outline 1 Basics 2 Random Variables 3 Expectation-Variance 4 Joint Distributions 5 Covariance 6 RV Conditionals 7 Random Vectors 8 Multivariate Gaussian
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  • $11.49
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More on Multivariate Gaussians
  • More on Multivariate Gaussians

  • Class notes • 11 pages • 2022
  • Available in package deal
  • 1 Definition 2 Gaussian facts 3 Closure properties 4 Summary 5 Exercise
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  • $5.39
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The Multivariate Gaussian Distribution
  • The Multivariate Gaussian Distribution

  • Class notes • 10 pages • 2022
  • Available in package deal
  • a multivariate normal (or Gaussian) distribution 1 Relationship to univariate Gaussians 2 The covariance matrix 3 The diagonal covariance matrix case 4 Isocontours 5 Linear transformation interpretation
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 Probability Theory Review
  • Probability Theory Review

  • Class notes • 12 pages • 2022
  • Available in package deal
  • Probability theory is the study of uncertainty. Through this class, we will be relying on concepts from probability theory for deriving machine learning algorithms. These notes attempt to cover the basics of probability theory at a level appropriate for CS 229. The mathematical theory of probability is very sophisticated, and delves into a branch of analysis known as measure theory. In these notes, we provide a basic treatment of probability that does not address these finer details. 1 Elem...
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