Hadrien Jean owns a Ph.D in cognitive science and works as a machine learning scientist specialized in sound and education. He wrote a series of tutorials as notes of the Deep Learning Book from Ian Goodfellow helping thousands of people to learn math for machine learning. He's also working on speech processing and leads projects on biodiversity assessment using deep learning applied to audio recordings. He concurrently teaches machine learning and deep learning in data science bootcamps at Le Wagon.

In this article, you will learn what the basis of a vector space is, see that any vectors of the space are linear combinations of the basis vectors, and see how to change the basis using change of basis matrices.

Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations.

As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.

In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory.

The Poisson distribution, named after the French mathematician Denis Simon Poisson, is a discrete distribution function describing the probability that an event will occur a certain number of times in a fixed time (or space) interval.

In this article, we’ll cover probability mass and probability density function in this sample. You’ll see how to understand and represent these distribution functions and their link with histograms.

In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.