Boost your data science skills. Learn linear algebra.

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

By Hadrien Jean, Ecole normale Supérieure (ENS)

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I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). 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

Light introduction to vectors, matrices, transpose and basic operations (addition of vectors of matrices). Introduces also Numpy functions and finally a word on broadcasting.

2. Multiplying Matrices and Vectors

This chapter is mainly on the dot product (vector and/or matrix multiplication). We will also see some of its properties. Then, we will see how to synthesize a system of linear equations using matrix notation. This is a major process for the following chapters.

3. Identity and Inverse Matrices

We will see two important matrices: the identity matrix and the inverse matrix. We will see why they are important in linear algebra and how to use it with Numpy. Finally, we will see an example on how to solve a system of linear equations with the inverse matrix.

4. Linear Dependence and Span

In this chapter we will continue to study systems of linear equations. We will see that such systems can’t have more than one solution and less than an infinite number of solutions. We will see the intuition, the graphical representation and the proof behind this statement. Then we will go back to the matrix form of the system and consider what Gilbert Strang call the *row figure* (we are looking at the rows, that is to say multiple equations) and the *column figure* (looking at the columns, that is to say the linear combination of the coefficients). We will also see what is linear combination. Finally, we will see examples of overdetermined and underdetermined systems of equations.

5. Norms

The norm of a vector is a function that takes a vector in input and outputs a positive value. It can be thinks as the *length* of the vector. It is for example used to evaluate the distance between the prediction of a model and the actual value. We will see different kind of norms (L⁰, L⊃1;, L⊃2;…) with examples.

6. Special Kinds of Matrices and Vectors

We have seen in 2.3 some special matrices that are very interesting. We will see other type of vectors and matrices in this chapter. It is not a big chapter but it is important to understand the next ones.

7. Eigendecomposition

We will see some major concepts of linear algebra in this chapter. We will start by getting some ideas on eigenvectors and eigenvalues. We will see that a matrix can be seen as a linear transformation and that applying a matrix on its eigenvectors gives new vectors with same direction. Then we will see how to express quadratic equations into a matrix form. We will see that the eigendecomposition of the matrix corresponding to the quadratic equation can be used to find its minimum and maximum. As a bonus, we will also see how to visualize linear transformation in Python!

8. Singular Value Decomposition

We will see another way to decompose matrices: the Singular Value Decomposition or SVD. Since the beginning of this series, I emphasized the fact that you can see matrices as linear transformation in space. With the SVD, you decompose a matrix in three other matrices. We will see that we can see these new matrices as *sub-transformation* of the space. Instead of doing the transformation in one movement, we decompose it in three movements. As a bonus, we will apply the SVD to image processing. We will see the effect of SVD on an example image of Lucy the goose so keep on reading!

9. The Moore-Penrose Pseudoinverse

We saw that not all matrices have an inverse. It is unfortunate because the inverse is used to solve system of equations. In some cases, a system of equation has no solution, and thus the inverse doesn’t exist. However it can be useful to find a value that is almost a solution (in term of minimizing the error). This can be done with the pseudoinverse! We will see for instance how we can find the best-fit line of a set of data points with the pseudoinverse.

10. The Trace Operator

We will see what is the Trace of a matrix. It will be needed for the last chapter on the Principal Component Analysis (PCA).

11. The Determinant

This chapter is about the determinant of a matrix. This special number can tell us a lot of things about our matrix!

12. Example: Principal Components Analysis

This is the last chapter of this series on linear algebra! It is about Principal Components Analysis (PCA). We will use some knowledge that we acquired along the preceding chapters to understand this important data analysis tool!


This content is aimed at beginners but it should be easier for people with at least some experience with mathematics.


I hope that you will find something interesting in that series. I tried to be as accurate as I could. If you find errors/misunderstandings/typos… Please report it! You can send me emails or open issues and pull request in the notebooks Github.


Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Bio: Hadrien Jean is currently a PhD student in cognitive science (end expected: 2018) at the Ecole normale Supérieure (ENS) in Paris. He is studying auditory perceptual learning (pitch perception and auditory selective attention) using psychophysics and electrophysiology (EEG). During his PhD, he designed online auditory experiments using Django/Javascript. Passionate about data analysis, he used R to analyse behavioural data and create vizualisations and Python to analyse EEG data and elaborate offline and online signal processing workflows.

Original. Reposted with permission.


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