Faisalsardar1
On this page, you find all documents, package deals, and flashcards offered by seller faisalsardar1.
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machine learning course
stanford machine learning course (half course + summary)
- Package deal
- • 16 items •
- 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 ….
stanford machine learning course (half course + summary)
Python/Numpy Tutorial.
Text editor/IDE options.. (don’t settle with notepad) • PyCharm (IDE) • Visual Studio Code (IDE) • Sublime Text (IDE) • Atom • Notepad ++/gedit • Vim (for Linux)
- Package deal
- Presentation
- • 39 pages •
Text editor/IDE options.. (don’t settle with notepad) • PyCharm (IDE) • Visual Studio Code (IDE) • Sublime Text (IDE) • Atom • Notepad ++/gedit • Vim (for Linux)
Neural Networks
Deep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation
- Package deal
- Summary
- • 6 pages •
Deep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation
Neural Networks
Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples
- Package deal
- Class notes
- • 21 pages •
Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples
Kernels, SVM.
summary of Kernel Methods SVMs
- Package deal
- Summary
- • 8 pages •
Probability Theory
Outline 1 Basics 2 Random Variables 3 Expectation-Variance 4 Joint Distributions 5 Covariance 6 RV Conditionals 7 Random Vectors 8 Multivariate Gaussian
- Package deal
- Presentation
- • 100 pages •
Outline 1 Basics 2 Random Variables 3 Expectation-Variance 4 Joint Distributions 5 Covariance 6 RV Conditionals 7 Random Vectors 8 Multivariate Gaussian
More on Multivariate Gaussians
1 Definition 2 Gaussian facts 3 Closure properties 4 Summary 5 Exercise
- Package deal
- Class notes
- • 11 pages •
1 Definition 2 Gaussian facts 3 Closure properties 4 Summary 5 Exercise
The Multivariate Gaussian Distribution
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
- Package deal
- Class notes
- • 10 pages •
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
Probability Theory Review
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...
- Package deal
- Class notes
- • 12 pages •
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...