A target output is the true output or labels on a given dataset. The function that maps the input to its correct labels is called the target function. Therefore, the underlying goal of many machine learning methods is to produce a function that matches the target function as close as possible without giving up generalizability. The target output can be used to compare the predictions of a model and determine its accuracy. Consider a neural network that classifies images.
There are many different algorithms used to train a neural network, and many variations of each. In this article, I am going to outline five algorithms that will give you an all-rounded understanding of how a neural network works. I will start with an overview of how a neural network works, mentioning at what stage the algorithms are used. Neural networks are fairly similar to the human brain. They are made up of artificial neurons, take in multiple inputs, and produce a single output.
Data scientists use many different algorithms to train neural networks, and there are many variations of each. In this article, I will outline five algorithms that will give you a rounded understanding of how neural networks operate. I will start with an overview of how a neural network works, mentioning at what stage the algorithms are used. Neural networks are fairly similar to the human brain. They are made up of artificial neurons, take in multiple inputs, and produce a single output.
Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are the two main tools for statistical modeling. While DNNs provide the ability to model rich and complex relationships between input and output variables, PGMs provide the ability to encode dependencies among the output variables themselves. End-to-end training methods for models with structured graphical dependencies on top of neural predictions have recently emerged as a principled way of combining these two paradigms. While these models have proven to be powerful in discriminative settings with discrete outputs, extensions to structured continuous spaces, as well as performing efficient inference in these spaces, are lacking. We propose non-parametric structured output networks (NSON), a modular approach that cleanly separates a non-parametric, structured posterior representation from a discriminative inference scheme but allows joint end-to-end training of both components.
In the literature on graphical models, there has been increased attention paid to the problems of learning hidden structure (see Heckerman [H96] for survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In most settings we should expect the former to be difficult, and the latter potentially impossible without experimental intervention. In this work, we examine some restricted settings in which perfectly reconstruct the hidden structure solely on the basis of observed sample data.