Machine Learning Guide


:star: :star: :star: :star: :star:

Notes

  • After AI winter, AI was revived through more realistic applications (recommenders), better computers, and more data. Also somewhat better algorithms
  • Reactions to AI: fear and optimism. We’ve survived through every revolution so far
  • AI is the next stage of evolution. It’s our duty to create AI
  • ML stages: predict, error, learn
  • An algorithm is code which describes a machine learning process
  • Gradient descent and linear regression are building blocks
  • Logistic regression is a bit of a misnomer. Logistic function is performed in linear regression
  • A logit is basically a probability
  • Objective function is the name of the function in the predict step. It is a sigmoid function in logistic regression
  • Gradient descent is descent to where the error is the lowest
  • 3 maths are linear algebra, stats, and calculus
  • Cooking analogy. Linalg is mise en place, stats are the recipe or cookbook, and calculus is actually cooking
  • Deep learning is closer to automating a mental task
  • Deep learning stacks shallow learning algorithms (eg logistic regression logit)
  • GPA is a good approximation of PCA
  • Kernel trick is like looking at a dataset from a different angle so you can see a better solution. Or like colored goggles
  • Markov: no current states have dependencies on previous states
  • NLP is a good specialization. It’s everywhere.
  • 3 levels from lowest to highest: parts, tasks, goals
  • Parts are corpora, lexicon, etc
  • Tasks are named entity recognition, part of speech
  • Goals are text classification, sentiment analysis, spell check
  • GPU is 20-100x performance gain
  • Nvidia provides cuda
  • Pandas to get and munge data. Then pass to numpy for transformations
  • In CNNs, window and stride gobhand in hand for filter definitions
  • Hyperparameter is anything the human chooses
  • When feature scaling. Normalization always does 0 to 1. Standardization is mean 0 with unit stdev. Standardization is more common