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By Ajit Jaokar. This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge the gap between high school maths and deep learning.

**Background**

As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University of Oxford, I see more students who are familiar with programming than with mathematics.

They have last learnt maths years ago at University. And then, suddenly they find that they encounter matrices, linear algebra etc when they start learning Data Science.

Ideas they thought they would not face again after college! Worse still, in many cases, they do not know where precisely these concepts apply to data science.

If you consider the maths foundations needed to learn data science, you could divide them into four key areas

- Linear Algebra
- Probability Theory and Statistics
- Multivariate Calculus
- Optimization

All of these are taught (at least partially) in high schools (14 to 17 years of age). In this book, we start with these ideas and co-relate them to data science and AI.

Read full article here.

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