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.

Artificial neural networks became very popular in recent years, mostly because of their success in tasks of image and speech recognition. While research in this area started more than 60 years ago and many different network architectures were developed during first decades of research, the only architecture that became popular in applications is MLP (multilayer perceptron) -- parametrized multilayer functions trained (optimized) with variations of gradient descent. Later based on MLP approach of training application-specific architectures emerged (such as convolutional and recurrent networks). Probably it is a good idea to understand the behavior of a neural network by visualizing it. While dependencies modelled in machine learning, in particular by neural networks, are multidimensional, we are limited in our visualization abilities to three dimensions.

Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn't (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class). To do this, we can train a Classifier with a'training dataset' and after such a Classifier is trained (we have determined its model parameters) and can accurately classify the training set, we can use it to classify new data (test set). If the training is done properly, the Classifier should predict the class probabilities of the new data with a similar accuracy.