\[ \newcommand{\prob}[1]{\operatorname{P}\left(#1\right)} \newcommand{\Var}[1]{\operatorname{Var}\left(#1\right)} \newcommand{\sd}[1]{\operatorname{sd}\left(#1\right)} \newcommand{\Cor}[1]{\operatorname{Corr}\left(#1\right)} \newcommand{\Cov}[1]{\operatorname{Cov}\left(#1\right)} \newcommand{\E}[1]{\operatorname{E}\left(#1\right)} \newcommand{\defeq}{\overset{\text{\tiny def}}{=}} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} \DeclareMathOperator*{\mini}{minimize} \]

  1. AI
  2. 18  image.html
Bayes, AI and Deep Learning
  • Preface
  • 1  Principles of Data Science
  • Bayes
    • 2  Probability and Uncertainty
    • 3  Bayes Rule
    • 4  Bayesian Parameter Learning
    • 5  Utility and Decisions
    • 6  Hypothesis Testing
    • 7  AB Testing
    • 8  Field vs Observational
  • AI
    • 9  Unreasonable Effectiveness of Data
    • 10  Theory of AI
    • 11  Linear and Generalized Liner Models
    • 12  Logistic Regression
    • 13  tree.html
    • 14  Forecasting
  • AI
    • 15  nn.html
    • 16  theorydl.html
    • 17  Automatic Differentiation
    • 18  image.html
    • 19  nlp.html
    • 20  robotics.html
  • References
17  Automatic Differentiation
19  nlp.html