About Hadrien Jean

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.

Hadrien Jean Posts (10)

• Essential Math for Data Science: Introduction to Systems of Linear Equations - 06 Aug 2021
  
  In this post, you’ll see how you can use systems of equations and linear algebra to solve a linear regression problem.
• Essential Math for Data Science: Basis and Change of Basis - 28 May 2021
  
  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.
• Essential Math for Data Science: Scalars and Vectors - 12 Feb 2021
  
  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.
• Essential Math for Data Science: Introduction to Matrices and the Matrix Product - 05 Feb 2021
  
  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.
• Essential Math for Data Science: Information Theory - 15 Jan 2021
  
  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.
• Essential Math for Data Science: The Poisson Distribution - 29 Dec 2020
  
  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.
• Essential Math for Data Science: Probability Density and Probability Mass Functions - 07 Dec 2020
  
  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.
• Essential Math for Data Science: Integrals And Area Under The Curve - 25 Nov 2020
  
  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.
• Preprocessing for Deep Learning: From covariance matrix to image whitening - 10 Oct 2018
  
  The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!
• Boost your data science skills. Learn linear algebra. - 03 May 2018
  
  The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.