Adaboost is a meta-learning machine learning (ML) algorithm, i.e., it can be used on top of any other ML algorithm. A perceptron classifier is not meta-learning ML. If you have no hidden layer, then perceptron is as good as a linear classifier, if it has one or more hidden layers then it is non-linear classifier. If it is deep (or multiple layers), then hierarchical features can be learned. The output of a perceptron is the linear combination of the feature and their associated weights.

Real-world complex networks describe connections between objects; in reality, those objects are often endowed with some kind of features. How does the presence or absence of such features interplay with the network link structure? Although the situation here described is truly ubiquitous, there is a limited body of research dealing with large graphs of this kind. Many previous works considered homophily as the only possible transmission mechanism translating node features into links. Other authors, instead, developed more sophisticated models, that are able to handle complex feature interactions, but are unfit to scale to very large networks. We expand on the MGJ model, where interactions between pairs of features can foster or discourage link formation. In this work, we will investigate how to estimate the latent feature-feature interactions in this model. We shall propose two solutions: the first one assumes feature independence and it is essentially based on Naive Bayes; the second one, which relaxes the independence assumption assumption, is based on perceptrons. In fact, we show it is possible to cast the model equation in order to see it as the prediction rule of a perceptron. We analyze how classical results for the perceptrons can be interpreted in this context; then, we define a fast and simple perceptron-like algorithm for this task, which can process $10^8$ links in minutes. We then compare these two techniques, first with synthetic datasets that follows our model, gaining evidence that the Naive independence assumptions are detrimental in practice. Secondly, we consider a real, large-scale citation network where each node (i.e., paper) can be described by different types of characteristics; there, our algorithm can assess how well each set of features can explain the links, and thus finding meaningful latent feature-feature interactions.

You've probably seen this internet meme demonstrating the alarming resemblance of chihuahuas and muffins. Everyone in the AI industry (including myself) loves putting the image in their presentations. But, one question I haven't seen anyone answer rigorously is: just how good IS modern AI at disambiguating between a chihuahua and a muffin? For your entertainment and education, I'll be investigating this question today. Binary classification has been possible ever since the perceptron algorithm was invented in 1957.

The single-layer Perceptron is the simplest of the artificial neural networks (ANNs). It was developed by American psychologist Frank Rosenblatt in the 1950s. The Perceptron is a linear classifier used for binary predictions. This means that in order for it to work, the data must be linearly separable. Although the Perceptron is only applicable to linearly separable data, the more detailed Multilayered Perceptron can be applied to more complicated nonlinear datasets.

MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks, but it is hard for MLPs to do classification and regression on sequences. In this code tutorial, a GRU is implemented in TensorFlow. A sequence is an ordered set of items and sequences appear everywhere. In the stock market, the closing price is a sequence. Here, time is the ordering.

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

The field of artificial neural networks is often just called neural networks or multi-layer perceptrons after perhaps the most useful type of neural network. A perceptron is a single neuron model that was a precursor to larger neural networks.

This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over different people involved in different IoTA based application development tasks. The survey reveals that feature selection is the most time consuming and niche skill demanding part of the entire workflow. This paper shows how feature selection is successfully automated without sacrificing the decision making accuracy and thereby reducing the project completion time and cost of hiring expensive resources. Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation. Three data sets are considered to establish the proof-of-concept. Experimental results show that the proposed automation is able to reduce the time for feature selection to $2$ days instead of $4-6$ months which would have been required in absence of the automation. This reduction in time is achieved without any sacrifice in the accuracy of the decision making process. Proposed method is also compared against Multi Layer Perceptron (MLP) model as most of the state of the art works on IoTA uses MLP based Deep Learning. Moreover the feature selection method is compared against SoA feature reduction technique namely Principal Component Analysis (PCA) and its variants. The results obtained show that the proposed method is effective.

Editor's note: ODSC supports the self-education of data enthusiasts of all levels, building the access to information and the means to showcase their data driven passions. The author of this post is a premier example of one such intersection. Caspar is our youngest Data Science Associate yet, and you can come see his talk at ODSC Europe. I spent years programming, solving problems, and producing results, focusing on from computer vision to web development. It seemed right to explore what I think is one of the most magical and exciting methods in technology, neural networks. My excitement with Machine Learning stemmed from the fact I'm solving the problems of problems.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.