After the explosive growth of open source machine learning and deep learning frameworks, the field is more accessible than ever. Thanks to this, it went from a tool for researchers to a widely adopted and used method, fueling the insane growth of technology we experience now. Understanding how the algorithms really work can give you a huge advantage in designing, developing and debugging machine learning systems. Due to its mathematical nature, this task can seem daunting for many. However, this does not have to be the way.

From a high level, there are four pillars of mathematics in machine learning.

Linear algebra

Probability theory

Multivariate calculus

Optimization theory

It takes time to build a solid foundation of these and understand the inner workings of the state of the art machine learning algorithms such as convolutional networks, generative adversarial networks, and many others. This won’t be an afternoon project, but given that you consistently dedicate time for this, you can go pretty far in a short amount of time. There are some great resources to guide you along the way. In this post, I have selected the five which were most helpful for me.

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