All to understand and practice A.I. in a simple way
In this tutorial, I invite you to discover a small open source framework that is very easy to set up and use and which will allow you to create an interface for your Machine Learning models. Follow the leader …
In this article we will discuss the concept of Transfer Learning … or how to avoid redoing long and consuming learning by partially reusing a pre-trained neural network. To do this we will use a network which is the reference in the matter: VGG-Net (vgg16).
I propose in this article to create a convolutional neural network to do NLP, and for the data I will use a dataset that you can simply find in the Kaggle datasets: FrenchFakeNewsDetector. You have understood the objective is twofold: on the one hand to see how we can use the convolution technique with vectors (1 dimension instead of images with 2+ dimensions) and on the other hand to do NLP with data in French.
When I finished the article on gradient descent, I realized that there were two important points missing. The first concerns the stochastic approach when we have too large data sets, the second being to see very concretely what happens when we poorly choose the value of the learning rate. I will therefore take advantage of this article to finally continue the previous article 😉
How to talk about Machine Learning or even Deep Learning without addressing the – famous – gradient descent? There are many articles on this subject of course, but often you have to read several in order to fully understand all the mechanisms. Often too mathematical or not enough, I will try especially here to explain its operation smoothly and step by step in order to try to demystify the subject.
For an analysis I wanted to do and after several searches, I realized that it was not that easy to get historical weather data. Of course, as i’m french I went to Meteo France Open Data and tried other open data site. But nothing really usable or it seems without a paid subscription. So I decided to retrieve them through a Python program and the scraping technique.
In this article we will see step by step how to create and use a convolutional neural network (CNN) to classify images.
Automation is echoing everywhere. So, simple buz or real fundamental movement? Personally, and beyond yet another industrial revolution, I like to position this approach simply as a natural way to “bring IT” back to its original goal: to simplify our lives
We will discuss in this post a kind of filters widely used by all images software (such as Photoshop or Gimp). In fact and to go further (without “sploiling” the following posts either) this convolution principle will also be widely used by neural networks (Deep Learning) … but we will see that later. First of all, let’s focus on the principle of these convolution filters.
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