Faisalsardar1

$25.99

stanford machine learning course (half course + summary)

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• 16 items •
by faisalsardar1 •
uploaded 2022

i x

Supervised learning setup • Class notes
Dataset split; Exponential family. Generalized Linear Models. • Summary
Linear Algebra Review • Class notes
Linear Algebra • Presentation
Gaussian discriminant analysis. Naive Bayes.Laplace Smoothing. • Class notes
And more ….

Python/Numpy Tutorial.

$5.99

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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Presentation
• 39 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning ml
machine learning course • By faisalsardar1

Neural Networks

$6.49

Deep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation

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Summary
• 6 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning ml
machine learning course • By faisalsardar1

Neural Networks

Free

Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples

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Class notes
• 21 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning ml
machine learning course • By faisalsardar1

Kernels, SVM.

$6.99

summary of Kernel Methods SVMs

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Summary
• 8 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning ml
machine learning course • By faisalsardar1

Kernel Methods

Kernels. SVM.

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Class notes
• 30 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning ml
machine learning course • By faisalsardar1

Probability Theory

$11.99

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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Presentation
• 100 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning-ml
machine learning course • By faisalsardar1

The Multivariate Gaussian Distribution

$4.99

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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Class notes
• 10 pages •
by faisalsardar1 •
uploaded 2022

i x

Stanford University • machine learning-ml
machine learning course • By faisalsardar1

Probability Theory Review

Free

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...