Definition of problems # Object detection # Bisogna trovare all'interno dell'immagine quali siano gli oggetti presenti, e in più vogliamo sapere dove siano quindi utilizzare una bounding box per caratterizzarli sarebbe buono. Object segmentation # È riuscire a caratterizzare…
The perceptron is a fundamental binary linear classifier introduced by (Rosenblatt 1958) . It maps an input vector x ∈ R n to an output y ∈ { 0 , 1 } using a weighted sum followed by a threshold function. Introduction to the Perceptron # A mathematical model # Given an input…
Backpropagation is perhaps the most important algorithm of the 21st century. It is used everywhere in machine learning and is also connected to computing marginal distributions. This is why all machine learning scientists and data scientists should understand this algorithm very…
Transformers, introduced in NLP language translation in (Vaswani et al. 2017) , are one of the cornerstones of modern deep learning. For this reason, it is quite important to understand how they are done. Introduction to Transformers # Transformers are called in this manner…
In questa serie di appunti proviamo a descrivere tutto quello che sappiamo al meglio riguardanti gli autoencoders Blog di riferimento Blog secondario che sembra buono Introduzione agli autoencoders # L'idea degli autoencoders è rappresentare la stessa cosa attraverso uno spazio…
PAC Learning is one of the most famous theories in learning theory. Learning theory concerns in answering questions like: What is learnable? Somewhat akin to La macchina di Turing for computability theory. How well can you learn something? PAC is a framework that allows to…
Gaussian Mixture Models # This set takes inspiration from chapter 9.2 of (Bishop 2006) . We assume that the reader already knows quite well what is a Gaussian Mixture Model and we will just restate the models here. We will discuss the problem of estimating the best possible…
The DP (Dirichlet Processes) is part of family of models called non-parametric models. Non parametric models concern learning models with potentially infinite number of parameters. One of the classical application is unsupervised techniques like clustering. Intuitively,…
This is a quite good resource about this part of Support Vector Machines (step by step derivation). (Bishop 2006) chapter 7 is a good resource. The main idea about this supervised method is separating with a large gap . The thing is that we have a hyperplane, when this plane is…
Active Learning concerns methods to decide how to sample the most useful information in a specific domain; how can you select the best sample for an unknown model? Gathering data is very costly, we would like to create some principled manner to choose the best data point to…