All to understand and practice A.I. in a simple way
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 š
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.
As soon as you begin to create machine learning models, you will be faced with the delicate problem of balance in the adjustment of bias and variance. In this article I try to simply explain how understand these two very important concepts.
If this method of “prediction” based on probabilities and states / transitions had its heyday, it now seems less fashionable. In this article we will come back to the fundamentals of Markov chains and their application in Python.
The purpose of this article is to show through a concrete and French case the method to perform a sentiment analysis with Python.
Discover Auto-ML with AutoGluon. Simple accelerator or real revolution in the way of creating machine learning models? get an idea by doing …
In this article we will see by practice if deep learning via keras can help us find Titanic survivors more efficiently than traditional algorithms.
In this article we will see how to install and especially use the Python Flask micro-framework. very useful for publishing your Machine learning models in REST!
In this short article you will see how in a few lines of Python code you can save your model and recall it.
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