Code Efficiency and Vectorization

Compare code efficiency between Python built-in functions and component-wise programming.

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Matrix multiplication using component-wise programming
Matrix multiplication using built-in function dot
Comparing the execution time
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Machine learning is about working with large collections of data. Such data is kept in databases, spreadsheets, or simply in text files, but to work with it, we load it into arrays. Since we define operations on such arrays, it is better to treat these arrays as vectors, matrices, or, generally, as tensors. Traditional programming languages such as C and Fortran require us to write code that loops over all the indices in order to specify operations that are defined on all the data. For example, let us define two random $n*n$ matrices with the NumPy random number generator for uniformly distributed numbers as follows:

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Getting started

Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Code Efficiency and Vectorization