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Table Of Contents

  • Notes on using Jupyter
  • Introduction to Python
  • Functions
  • Classes
  • Strings
  • Using numpy
  • Graphics in Python
  • Data
  • SQL
  • Machine Learning with sklearn
  • Code Optimization
  • Just-in-time compilation (JIT)
  • Cython
  • Parallel Programming
  • Multi-Core Parallelism
  • Using ipyparallel
  • Using C++
  • Using pybind11
  • Linear Algebra Review
  • Linear Algebra and Linear Systems
  • Matrix Decompositions
  • Linear Algebra Examples
  • Applications of Linear Alebra: PCA
  • Sparse Matrices
  • Optimization and Root Finding
  • Algorithms for Optimization and Root Finding for Multivariate Problems
  • Using optimization routines from scipy and statsmodels
  • Random numbers and probability models
  • Resampling and Monte Carlo Simulations
  • Numerical Evaluation of Integrals
  • Probabilistic Graphical Models with pgmpy
  • Working with large data sets
  • Biggish Data
  • Efficient storage of data in memory
  • Working with large data sets
  • Using Spark
  • Using Spark Efficiently
  • Spark MLLib
  • Spark SQL
  • Spark Streaming
  • Spark on Cloud
  • Using PyMC3
  • PyStan
  • Metropolis and Gibbs Sampling
  • Using Auxiliary Variables in MCMC proposals
  • TensorFlow and Edward
  • Bonus Material: The Humble For Loop
  • Bonus Material: Word count
  • Symbolic Algebra with sympy

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