A case-study for future entrepreneurs

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In this article, I will walk you through my first entrepreneurial project, what we did right, what we did wrong, and questions we should have wondered earlier. I’ll also explain what this project changed in my life today. Some of the videos and articles I mention are in French, but they’re just illustrations.

How the idea was born

I started my entrepreneurial journey in 2016 when I was 20 years old. I was finishing my Bachelor in Economics at the University of Lausanne (Switzerland) at the time. I’ve always wanted to create something from scratch. …


A visual, practical and mathematical explanation

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In this article, we will review, in the clearest way that I could come up with, the process of training a Gaussian Mixture Model with EM. By the end of the article, you should have a broader understanding of GMMs, what EM does, and applications of all of this.

We will cover the following points:

  1. What are GMMs? And why do we use GMMs?
  2. Training a GMM
  3. Training a GMM with EM
  4. Hard/Viterbi EM
  5. Applications of EM for GMM

As a side note, all the code to generate these graphs and put them into an interactive web application is on…


And the outcome is really funny. Guide to Language Generation.

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Over the course of the past few months, I wrote over 100 articles on my personal blog: https://maelfabien.github.io/. That’s quite a decent amount of content. An idea then came to my mind:

🚀 Train a language generation model to speak like me. 🚀

Or more specifically, to write like me. This is the perfect way to illustrate the main concepts of language generation, its implementation using Keras, and the limits of my model.

The whole code of this article can be found on this repository :

Before we get started, I have found this Kaggle Kernel to be a useful…


Concepts, applications, and examples with Python

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Graphs are becoming central to machine learning these days, whether you’d like to understand the structure of a social network by predicting potential connections, detecting fraud, understand customer’s behavior of a car rental service or making real-time recommendations for example.

In this article, we’ll cover :

  • The main graph learning algorithms
  • Use cases and implementations in Python

I publish all my articles and the corresponding code on this repository :

This is the final article on my Graph series. If you haven’t, make sure to check the first articles of this series:

For what comes next, open a Jupyter Notebook…


Main concepts, properties, and applications in Python

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Graphs are becoming central to machine learning these days, whether you’d like to understand the structure of a social network by predicting potential connections, detecting fraud, understand customer’s behavior of a car rental service or making real-time recommendations for example.

In this article, we’ll cover :

  • The main graph algorithms
  • Illustrations and use-cases
  • Examples in Python

This article was originally published on my personal blog: https://maelfabien.github.io/ml/#

I publish all my articles and the corresponding code on this repository :

If you haven't, make sure to check my first article of this series:

NEW: Part 3 is out!

For what comes…


Main concepts, properties, and applications in Python

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Graphs are becoming central to machine learning these days, whether you’d like to understand the structure of a social network by predicting potential connections, detecting fraud, understand customer’s behavior of a car rental service or making real-time recommendations for example.

In this article, we’ll cover the following topics:

  • What is a graph?
  • How to store a graph?
  • Types of and properties of graphs
  • Examples in Python

This is the first article of a series of three articles dedicated to Graph Theory, Graph Algorithms and Graph Learning.

This article was originally published on my personal blog: https://maelfabien.github.io/ml/#

I publish all my…


Main concepts, properties, and applications

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In this article, we’ll focus on Markov Models, where an when they should be used, and Hidden Markov Models. This article will focus on the theoretical part. In a second article, I’ll present Python implementations of these subjects.

Markov Models, and especially Hidden Markov Models (HMM) are used for :

  • Speech recognition
  • Writing recognition
  • Object or face detection
  • Economic Scenario Generation and specific Finance tasks
  • And several NLP tasks …

This article was originally published on my personal blog: https://maelfabien.github.io/machinelearning/HMM_1/#

I publish all my articles and the corresponding code on this repository :

Don’t hesitate to star the repo :)

I. Stochastic model


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In this tutorial, we’ll see how to create and launch a face detection algorithm in Python using OpenCV and Dlib. We’ll also add some features to detect eyes and mouth on multiple faces at the same time. This article will go through the most basic implementations of face detection including Cascade Classifiers, HOG windows and Deep Learning CNNs.

We’ll cover face detection using :

  • Haar Cascade Classifiers using OpenCV
  • Histogram of Oriented Gradients using Dlib
  • Convolutional Neural Networks using Dlib

This article was originally published on my personal blog : https://maelfabien.github.io/tutorials/face-detection/#

The Github repository of this article (and all the…


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A Visual Explanation

Boosting techniques have recently been rising in Kaggle competitions and other predictive analysis tasks. I’ll try to explain the concepts of Boosting and AdaBoost as clearly as possible. The initial article was published on my personal blog : https://maelfabien.github.io/machinelearning/adaboost/

In this article, we’ll cover :

  • A quick recap of bagging
  • Limits of bagging
  • Detailed concept of Boosting
  • Efficience of boosting in computation
  • A code example

I have recently created a dedicated GitHub repository for some tutorials I’m following and/or building. I have also added ML recaps :

https://github.com/maelfabien/Machine_Learning_Tutorials

I. The limits of Bagging

For what comes next, consider a binary classification problem. We are either…

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