Unplash: Halacious

A new theory on intelligence and how the neocortex works.


For a long time, I have been following Numenta, a startup of neuroscientists whose goal is to understand the neocortex to reproduce the mechanisms in learning algorithms.

The founder, Jeff Hawkins, wrote the book A Thousand Brains: A New Theory of Intelligence. Attractive title for me who loves neuroscience and…

Source: Image taken by the author

A theoretical way through n-grams, tf-idf, one-hot encoding, word embeddings. And a surprise with pre-trained models.

Natural Language Processing seeks to map language to representations that capture morphological, lexical, syntactic, semantic, or discourse characteristics that can be processed by machine learning methods.

Kamath, J. Liu, and Whitake 2019

It is necessary to present elements of theory before entering the algorithmic methodology. …

Take by the author — Deep Learning with Python, 2017, François Chollet book and my cat

A great tour in the world of Deep Learning in less than 10 minutes.

It took me a long time to open this book. More for fear of finding that I knew nothing more than for fear of being frustrated with knowing everything. I regularly receive newsletters on "best of" or "most read" books about artificial intelligence, machine learning, or deep learning. Deep Learning…

Source: Images taken by the author and annotated by him

The difference between the techniques and their applications

This article is the first part of three articles about computer vision. Part 2 will explain Object Recognition. Part 3 will be about Image Segmentation.

With this article is provided a notebook: here on GitHub

What is more exciting than seeing the world? To be able to see the best…

Source: Result of the study — computed by the author

An application of the RNN family

For a long time, I heard that the problem of time series could only be approached by statistical methods (AR[1], AM[2], ARMA[3], ARIMA[4]). These techniques are generally used by mathematicians who try to improve them continuously to constrain stationary and non-stationary time series.

A friend of mine (mathematician, professor of…

Source: Image by Free-Photos from Pixabay

In the world of machine learning and Kaggle competitions, the XGBoost algorithm has the first place.

Like many data scientists, XGBoost is now part of my toolkit. This algorithm is among the most popular in the world of data science (real-world or competition). Its multitasking aspect allows it to be used in regression or classification projects. It can be used on tabular, structured, and unstructured data.

Photo by Markus Spiske on Unsplash

Make your data beautiful and understandable with EDA libraries, features importance, feature selection, and feature extraction

A notebook containing all the relevant code is available on GitHub.

I — Exploratory Data Analysis or commonly EDA

Yes, this is a new post among many that address the subject of EDA. This step is the most important of a Data Science project. Why? …

Christophe Pere

Research Scientist, AI

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