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…
Introduction to Convolutional NN # Design Goals # We want to be invariant to some transformations but also at the same time to be specific to some thing. Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for image processing…
Egocentric vision is a sub-field of computer vision that studies vision understanding from a centered point of view, that typical of animals. One historical thing is MIT 1997 they had to bring around very heavy cameras. Now we have glasses. Other examples of egocentric vision…
Generative Adversarial Network has been introduced in 2014 by Ian Goodfellow (at that time they where still gray and white). Now the images have been improved with Diffusion Models , that can be considered the new paradigm. This idea has been considered by Yann LeCun as one of…
Many problems in scientific computing include: Solving linear equations Eigenvalue computations Singular value decomposition LU/Cholesky/QR decompositions etc... And the userbase is quite large for this types of computation (number of scientists in the world is growing…
An historical perspective # The origins of motion capture # One of the earliest starts of motion capturing is the famous horse in 1878 in motion "video". This was the start of all the modern cameras. One of the earliest human body motion capture was in military for moving…
The algorithm has been the same, some ideas are in Fourier Series , but architectures change, which means there are new ways to make this algorithm even faster. Example of transforms # We have learned in Algebra lineare numerica , Cambio di Base that linear transforms are…
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…
Locality principles # Remember the two locality principles in Memoria . temporal locality and spatial locality . Temporal Locality # Some elements just are accessed many times in time. This is an example of a temporal locality . Spatial locality # Some elements are accessed…