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Articles by Hadrien Jean
Automating Speech Audiometry in Quiet and in Noise Using a Deep Neural Network
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Predictable sequential structure augments auditory sensitivity at threshold
Abstract Human hearing is highly sensitive and allows us to detect acoustic events at low levels. However, sensitivity is not only a function of the integrity of cochlear transduction mechanisms, but also constrained by central processes such as attention and expectation. While the effects of distraction and attentional orienting are generally acknowledged, the extent to which probabilistic expectations influence sensitivity at threshold is not clear.
Predictable sequential structure enhances auditory sensitivity in clinical audiograms
Abstract Human hearing is highly sensitive and allows us to detect acoustic events at low levels and at great distances. However, sensitivity is not only a function of the integrity of cochlear mechanisms, but also constrained by central processes such as attention and expectation. While the effects of distraction and attentional orienting are generally acknowledged, the extent to which probabilistic expectations influence sensitivity is not clear.
Essential Math for Data Science: Eigenvectors and Application to PCA
Matrix decomposition, also called matrix factorization is the process of splitting a matrix into multiple pieces. In the context of data science, you can for instance use it to select parts of the data, aimed at reducing dimensionality without losing much information (as for instance in Principal Component Analysis, as you’ll later in this post). Some operations are also more easily computed on the matrices resulting from the decomposition.
Essential Math for Data Science: Visual Introduction to Singular Value Decomposition
In this article, you’ll learn about Singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning. It is for instance used to calculate the Principal Component Analysis (PCA). You’ll need some understanding of linear algebra basics (feel free to check the previous article and the book Essential Math for Data Science.
Essential Math for Data Science: Introduction to Systems of Linear Equations
By Hadrien Jean, Machine Learning Scientist. Systems of Linear Equations In this article, you’ll be able to use what you learned about vectors and matrices, and linear combinations (respectively Chapter 05, 06 and 07 of Essential Math for Data Science). This will allow you to convert data into systems of linear equations. At the end of this chapter (in Essential Math for Data Science), 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
By Hadrien Jean, Machine Learning Scientist. Basis and Change of Basis One way to understand eigendecomposition is to consider it as a change of basis. You’ll learn in this article what is the basis of a vector space. You’ll see that any vector of the space are linear combinations of the basis vectors and that the number you see in vectors depends on the basis you choose. Finally, you’ll see how to change the basis using change of basis matrices.
Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD)
ESSENTIAL MATH FOR DATA SCIENCE (image by author) In this article, you’ll learn about Singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning. It is for instance used to calculate the Principal Component Analysis (PCA). You’ll need some understanding of linear algebra basics (feel free to check the previous article and the book Essential Math for Data Science).
Essential Math for Data Science: Linear Transformation with Matrices
By Hadrien Jean, Machine Learning Scientist As you can see in Essential Math for Data Science, being able to manipulate vectors and matrices is critical to create machine learning and deep learning pipelines, for instance for reshaping your raw data before using it with machine learning libraries. The goal of this chapter is to get you to the next level of understanding of vectors and matrices.
Essential Math for Data Science: Scalars and Vectors
By Hadrien Jean, Machine Learning Scientist. Machines only understand numbers. For instance, if you want to create a spam detector, you have first to convert your text data into numbers (for instance, through word embeddings). Data can then be stored in vectors, matrices, and tensors. For instance, images are represented as matrices of values between 0 and 255 representing the luminosity of each color for each pixel.
Essential Math for Data Science: Introduction to Matrices and the Matrix Product
Matrices and Tensors As you saw in Essential Math for Data Science, vectors are a useful way to store and manipulate data. You can represent them geometrically as arrows, or as arrays of numbers (the coordinates of their ending points). However, it can be helpful to create more complicated data structures – and that is where matrices need to be introduced. Introduction As vectors, matrices are data structures allowing you to organize numbers.
Essential Math for Data Science: Information Theory
By Hadrien Jean, Machine Learning Scientist. The field of information theory studies the quantification of information in signals. In the context of machine learning, some of these concepts are used to characterize or compare probability distributions. The ability to quantify information is also used in the decision tree algorithm, to select the variables associated with the maximum information gain.
Essential Math for Data Science: The Poisson Distribution
By Hadrien Jean, Machine Learning Scientist 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. It is used to model count-based data, like the number of emails arriving in your mailbox in one hour or the number of customers walking into a shop in one day, for instance.
Guide to Encoding Categorical Features Using Scikit-Learn For Machine Learning
And why you should stop using Pandas get_dummies One of the most crucial preprocessing steps in any machine learning project is feature encoding. It is the process of turning categorical data in a dataset into numerical data. It is essential that we perform feature encoding because most machine learning models can only interpret numerical data and not data in text form.
Essential Math for Data Science: Probability Density and Probability Mass Functions
By Hadrien Jean, Machine Learning Scientist In the chapter 02 of Essential Math for Data Science, you can learn about basic descriptive statistics and probability theory. 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. Deterministic processes give the same results when they are repeated multiple times.
Essential Math for Data Science: Integrals And Area Under The Curve
By Hadrien Jean, Machine Learning Scientist Calculus is a branch of mathematics that gives tools to study the rate of change of functions through two main areas: derivatives and integrals. In the context of machine learning and data science, you might use integrals to calculate the area under the curve (for instance, to evaluate the performance of a model with the ROC curve, or to calculate probability from densities.
Essential Math for Data Science: The Poisson Distribution
ESSENTIAL MATH FOR DATA SCIENCE Learn about this important discrete distribution through code and example. (Image by author) 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: Area Under The ROC Curve and Integrals
Essential Math for Data Science Learn about integrals and model performance comparison using the area under the ROC curve (Image by author) Calculus is a branch of mathematics that gives tools to study the rate of change of functions through two main areas: derivatives and integrals.
Probability Mass and Density Functions
By Hadrien Jean, Machine Learning Scientist at Ava Accessibility Github: the corresponding Python notebook can be found here. Introduction I’m happy to present here the following of my series on the Deep Learning Book by Goodfellow et al. This is the first post/notebook made from chapter 3 on Probability. For those who already know my posts/notebooks about Chapter 2 on linear algebra, the aim and the structure are the same.
Preprocessing for Deep Learning: From covariance matrix to image whitening
The goal of this post is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (such as Python/NumPy) to better understand abstract mathematical notions. Thinking by coding! ūüí• We will start with basic but very useful concepts in data science and machine learning/deep learning, like variance and covariance matrices. We will go further to some preprocessing techniques used to feed images into neural networks.
Machine Learning Basics - The Norms
Tutorial Learn what the normal equation is and how can you use it to build machine learning models. Tutorial A SciPy tutorial in which you'll learn the basics of linear algebra that you need for machine learning in Python, with a focus how to with NumPy. Tutorial An easy-to-follow scikit-learn tutorial that will help you get started with Python machine learning. Tutorial Learn what formulates a regression problem and how a linear regression algorithm works in Python.
Boost your data science skills. Learn linear algebra.
By Hadrien Jean, Ecole normale Supérieure (ENS) 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 data science algorithms.
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