I like drawing portraits and other things from time to time. So I have decided to share them on my blog to motivate myself to continue doing this activity....
In this article, I will explain the core ideas of neural networks from an abstract mathematical perspective. By "abstract", I mean that I will try to explain the "why" of mathematical concepts without covering all mathematical details. To simplify the mathematical concepts of neural networks, I will use some analogies from real life situations, with visualisations and examples. I will start by explaining why we need neural networks, and then discuss the role of optimisation and backpropagation algorithms. Why Do We Need Neural Networks Neural networks are tools that allow us to approximate complex multivariate functions representing the relationships between dataset inputs and outputs. Typically, it is not feasible to define one explicit equation that can reproduce these multivariate functions. The role of training is thus to approximate them. Indeed, most neural networks architectures are based on a mathematical theorem called Universal Approximation Theorem. Th...
Security is defined by Meriam-Webster Dictionary [1] as the quality or state of being secure. Secure itself is defined as : free from danger or free from risk of loss If we have one million dollars and live in a utopian world, we will not be worried about the dangers that could exist to our money. We may leave our money in the street and label them with our name; no one will take them. Thus, the security and even trust concepts are not relevant in this world because we are sure that everyone is good and has good intentions. In a dystopian world, we are almost sure that dangers exist. Therefore, almost everyone is interested in our assets. In this world, Security is critical as it is the only way that allows us to survive. Trust is relevant also but should be used much less frequently than Security. In the real world, we are between the utopia and dystopia worlds. We are not sure about the existence or absence of dangers, and yet we need to make decis...
We tend to believe that having a big keyspace is necessary to ensure the secrecy of our information. While this belief is correct for most of our communications, there will be cases when we can obtain perfect secure ciphers whose key spaces are small. We need to check Shannon’s definition of Perfect Secrecy to see how it is possible. Claude Shannon has many contributions. One of his main contributions was the transformation of cryptography from an art into a rigorous science using probability theory. In his work, entitled "Communication Theory of Secrecy Systems" 1 , Shannon defined the concept of Perfect Secrecy and proved that the Vernam Cipher is perfectly secure. Shannon stated that " “Perfect Secrecy” is defined by requiring of a system that after a cryptogram is intercepted by the enemy the a posteriori probabilities of this cryptogram representing various messages be identically the same as the a priori probabilities of the same messages before the ...
I like drawing - it is very beautiful art- I like it and i love your light pic more I don't know why but I felt warm
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