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afshinea/stanford-cs-229-machine-learning

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TLDR

Concise, printable cheat sheets covering Stanford's CS 229 machine learning course, quick references for supervised learning, unsupervised learning, deep learning, and math prerequisites.

Mindmap

mindmap
  root((repo))
    What it covers
      Supervised learning
      Unsupervised learning
      Deep learning
      Math refreshers
    Format
      Printable PDFs
      Multiple languages
      Quick reference
    Use cases
      Exam prep
      Course study
      Interview review
    Audience
      Students
      Self-learners
      Job candidates

Things people build with this

USE CASE 1

Study for a machine learning exam or CS 229 course by reviewing key formulas and algorithm summaries.

USE CASE 2

Refresh your understanding of supervised and unsupervised learning concepts before a technical job interview.

USE CASE 3

Use as a quick desk reference while working on machine learning projects to look up probability, linear algebra, or neural network concepts.

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

This repository contains a set of concise, printable cheat sheets covering the material taught in Stanford's CS 229 Machine Learning course. CS 229 is a well-known university course that covers the mathematical and conceptual foundations of machine learning, how algorithms learn from data, recognize patterns, and make predictions. The cheat sheets distill each major topic into a compact reference that is easy to review quickly, especially during study or before an exam. The collection includes four main cheat sheets, supervised learning (teaching a model using labeled examples), unsupervised learning (finding structure in unlabeled data), deep learning (neural networks), and tips and tricks for training models, plus two refresher sheets covering the math prerequisites: probabilities and statistics, and linear algebra and calculus. All of them are also combined into a single "Super VIP" compilation. The sheets are available as PDFs in over ten languages. You would use this repository if you are studying machine learning at any level and want a quick-reference summary of the key concepts, formulas, and algorithms, whether you are enrolled in CS 229 itself, taking a similar course elsewhere, or refreshing your knowledge before a job interview.

Copy-paste prompts

Prompt 1
I'm studying machine learning and need a quick summary of supervised learning algorithms, what are the main types and when do you use each one?
Prompt 2
Help me understand the difference between supervised and unsupervised learning with concrete examples I can remember during an interview.
Prompt 3
I'm reviewing for a machine learning exam, what are the most important formulas and concepts I should memorize from linear algebra and probability?
Prompt 4
Create a study plan for learning the fundamentals of deep learning and neural networks using key concepts and formulas.
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