Tag: supervised learning

Machine learning makes use of multiple mathematical formulas and relations to implement the different tasks it can handle. Gathered in the following “cheat sheets” by Afshine and Shervine Amidi, the concepts for supervised and unsupervised learning, deep learning together with machine learning tips and tricks, probabilities, statistics algebra and calculus reminders, are all presented in details with the underlying math.

Based on the Stanford course on Machine Learning (CS 229), the cheat sheets summarize the important concepts of each branch with simple explanations and diagrams, such as the following table cover underfitting and overfitting.

	Underfitting	Just right	Overfitting
Symptoms	• High training error • Training error close to test error • High bias	• Training error slightly lower than test error	• Very low training error • Training error much lower than test error • High variance
Regression illustration
Classification illustration
Deep learning illustration
Possible remedies	• Complexify model • Add more features • Train longer		• Perform regularization • Get more data

The main machine learning cheat sheets can be found here:

• Supervised Learning
  Results about linear models, generative learning, support vector machines and kernel methods
• Unsupervised Learning
  Formulas about clustering methods and dimensionality reduction
• Deep Learning
  Main concepts around neural networks, backpropagation and reinforcement learning
• Machine Learning Tips and Tricks
  Good habits and sanity checks to make sure that your model is trained the right way

Other mathematics and coding cheat sheets can be found here:

• Probabilities and Statistics
  Formulas about combinatorics, random variables, main probability distributions, and parameter estimation
• Linear Algebra and Calculus
  Matrix-vector notations as well as algebra and calculus properties
• Getting started with Matlab
  Main features and good practices to adopt

The complete cheat sheets can also be found on Github.