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Mis à jour le 22/03/2021

Discover the Fundamentals of Deep Learning

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In recent years, artificial intelligence (AI) has been receiving a lot of media attention. The focus of much of this attention has been directed at a particular AI sub-discipline: deep learning.

Deep learning is a sub-discipline of machine learning
Deep learning is a sub-discipline of machine learning

But what exactly is deep learning?

Deep learning involves convolutional artificial neural networks. Does that sound like a foreign language to you? Don’t panic: let’s decipher it together!

First, you need to know what an artificial neural network is. Then we’ll look at convolution. Once these two concepts are defined, you’ll grasp deep learning, no problem! Ready?

The Inspiration Behind Artificial Neural Networks

In the early days of artificial intelligence, the goal was to replicate the human brain or, more accurately, to draw inspiration from it. Researchers focused first on the functioning of the biological brain, specifically concerning neurons. These neurons communicate with one another using synapses.

A neuron: the inspiration behind artificial neural networks
A neuron: the inspiration behind artificial neural networks

Algorithms were developed in an attempt to simulate this neural architecture. Of course, artificial intelligence doesn’t recreate neurons; it simply draws inspiration from them.

Let’s see what that looks like!

What are Artificial Neural Networks?

To get a clearer picture of how artificial neural networks work, let’s look at an example of how they are used in image recognition. We’ll focus on image sorting. Digital photos tend to pile up, and sorting through them can become a real chore. Like in the video with cats and dogs, we’re going to build an artificial intelligence program that will do the sorting. Its mission will be to tag the people pictured below with their names.

To train an artificial intelligence system to identify family members who appear in snapshots, gather a series of photos of each individual.

Photos of 3 family members
Photos of family members

To analyze family photos, we'll use an artificial neural network.

An artificial neural network can be imagined as a box with:

  •  An entrance: the data input (a photo).

  •  An exit: the expected output (the first name of the person in the photo).

  •  A neural network (between the two): a series of layers of artificial neurons.

Image of Anne on the left, a neutral network in the middle, and the name
An artificial neural network help to identify Anne as Anne!

So, should we use supervised or unsupervised for this?

You can use deep learning with both supervised and unsupervised learning. In this instance, we are dealing with a typical supervised learning problem. We want to train an algorithm on annotated examples.

What is going on in this neural network? How does the learning take place?

Neurons make up the network, grouped into three different layers: input, hidden, and output.

The Input Layer

Thus, if an image has a million pixels, the first layer will be made up of a million neurons.

The Hidden Layers

The results of these mathematical computations—known as activation functions—determine whether a neuron will be activated or not, as each neuron takes input data from the preceding layer and returns a 1 (neuron activation) or a 0 (non-activation).

The Output Layer

In our example, this will be the name of the person in the photo.

We can represent the three types of layers schematically as follows:

An image of Anne on the left. The image passes through the input layer, the hidden layers, and finally, the output layers.
A simplified version of a neural network

OK, the neural network receives images as input and delivers a response as output. But how does it learn?

For the algorithm to learn, it must train itself by passing each photo through its different neuron layers until it obtains a result. In the beginning, the responses will be somewhat random because the algorithm has no experience!

This image shows that the neural network identifies Anne as Cindy. This is incorrect.
The neural network got it wrong! It did not correctly identify Anne in the photo.

As training continues, the network will change the way the neurons transmit information to each other to arrive at the correct image categorization. The images pass through the neural network one by one. The network makes adjustments as needed to annotate them correctly.

As training continues, the neural network adjusts its parameters and gets its right! It correctly identify Anne in the photo.
As training continues, the neural network adjusts its parameters and correctly identify Anne in the photo.

What Are Convolutional Neural Networks?

We have finished training our neural network. Good news: it’s very good at sorting our portrait photos!

We need to improve our AI tool so that it can tag more than one person in the same image.

There are four different people in this photo.
This time, we are going to identify the different people in this photo.

To do this, we will use a special neural network called a convolutional neural network (CNN).

We will move sequentially from one part of the image to the other, making passes.

To the left, there is a diagram of a filter on an image. To the right, there are multiple different examples of that filter moving across the image to capture information at different moments.
A filter on an image

Deep Learning: Artificial Neural Networks and Convolution

Deep learning came about when scientists combined artificial neural networks with convolution.

 What distinguishes deep learning and makes it so popular?

Its impressive results and future potential! One of the key advantages of deep learning is its ability to process huge volumes of data. The more data these neural networks receive, the more they can improve. Some image recognition systems are trained on datasets containing tens of millions of images.

Let's Recap!

  •   Deep learning is an especially powerful sub-discipline of machine learning.

  •   It involves the construction of brain-inspired systems of artificial neural networks.

  • It also uses the principle of convolution, which can be used to analyze an image in successive passes through a filter window, making it even more powerful.

You have mastered machine learning and deep learning basics, and can identify the various stages of an artificial intelligence project. Before completing this course, test what you’ve learned from the last few chapters!

Exemple de certificat de réussite
Exemple de certificat de réussite