A Data Model for Training and Scoring 5 Members Events Event RSVPs Model Member Predicted RSVP Offline Training Real-Time Scoring 6. Component Integration and Serving 6 Kinesis Producer AWS S3 Spark Model Training Spark Structured Streaming Meetup Stream Meetup Member API Meetup Prediction 7. Producing the Kinesis Firehose Stream 7 requests.get() Save the model to disk for scoringmodel.write.overwrite().save(...) 10. Scoring the Model in Real-time 10 Load the trained modelval model PipelineModel.load(...) Stream meetup event data Score the model val events spark.readStream ML Limitations in Structured Streaming 11 •Structured streaming does not support operations needed by ML methods –count, collect, round, aggregate*, etc. • Many models, transformers, and estimators are not supported –K-Means, SVM, CountVectorizer, VectorAssembler, StringIndexer, etc. 12.

Autonomous cars are very closely associated with Industrial IoT. IoT combined with other technologies such as machine learning, artificial intelligence, local computing etc are providing the essential technologies for autonomous cars. Very inquisitive questions for many is how are these autonomous cars functioning. What actually is working inside to make them work without drivers taking control of the wheel. Very well known that these days cars are equipped with a lot of sensors, actuators, and controllers.

In this tutorial, we will introduce you to Machine Learning with Apache Spark. The hands-on lab for this tutorial is an Apache Zeppelin notebook that has all the steps necessary to ingest and explore data, train, test, visualize, and save a model. We will cover a basic Linear Regression model that will allow us perform simple predictions on a sample data. This model can be further expanded and modified to fit your needs. Most importantly, by the end of this tutorial, you will understand how to create an end-to-end pipeline for setting up and training simple models in Spark.

Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications. Logistic Regression is used when the dependent variable(target) is categorical. Consider a scenario where we need to classify whether an email is spam or not. If we use linear regression for this problem, there is a need for setting up a threshold based on which classification can be done.

Machine Learning is about making predictions. This post will give an introduction to Machine Learning through a problem that most businesses face: predicting customer churn. ML can help predict which of your customers are at risk for leaving in advance, and give you an edge by pre-empting with action. Machine Learning can best be understood through four progressive lenses. Machine Learning allows us to accurately predict things using simple statistical methods, algorithms, and modern computing power.

Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how hidden predictors are linearly combined or the linear mechanism. In traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such an one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. To overcome this difficulty, a two-stage learning (TSL) scheme, featured by learning through deterministic assignment of hidden parameters (LtDaHP) was proposed, which suggests to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. We also present a series of simulations and application examples to support the outperformance of LtDaHP

Recent neural network and language models rely on softmax distributions with an extremely large number of categories. Since calculating the softmax normalizing constant in this context is prohibitively expensive, there is a growing literature of efficiently computable but biased estimates of the softmax. In this paper we propose the first unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and no extra work is required at the end of each epoch). We show that our proposed unbiased methods comprehensively outperform the state-of-the-art on seven real world datasets.

Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence. But many measures of uncertainty exist, including predictive en- tropy and mutual information, each capturing different types of uncertainty. We study these measures, and shed light on why mutual information seems to be effective at the task of adversarial example detection. We highlight failure modes for MC dropout, a widely used approach for estimating uncertainty in deep models. This leads to an improved understanding of the drawbacks of current methods, and a proposal to improve the quality of uncertainty estimates using probabilistic model ensembles. We give illustrative experiments using MNIST to demonstrate the intuition underlying the different measures of uncertainty, as well as experiments on a real world Kaggle dogs vs cats classification dataset.

The stochastic gradient descent (SGD) optimization algorithm plays a central role in machine learning and, in particular, deep learning applications such as image analysis and speech recognition (cf., e.g., [12, 13, 16, 23]). It is therefore important to analyze and quantify the convergence speed of the SGD method. There is a vast amount of scientific literature investigating and providing upper bounds for the SGD method and modifications of it (cf., e.g., [3, 4, 5, 6, 7, 8, 9, 10, 11, 18, 20, 21, 24] and cf., e.g., [14] for a more comprehensive review of the literature). Much less attention has been paid to proving lower error bounds for the SGD method, that is, to quantifying the best possible speed of convergence which the SGD method can achieve (cf., e.g., [2, 17, 19, 22, 25]). It is the key contribution of this paper to make a step in this direction.

Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e.g. number of layers in DL). In this paper, we present an alternative semi-parametric framework which foregoes the ordinarily required feedback, by introducing the novel idea of geometric regularization. We show that certain deep learning techniques such as residual network (ResNet) architecture are closely related to our approach. Hence, our technique can be used to analyze these types of deep learning. Moreover, we present preliminary results which confirm that our approach can be easily trained to obtain complex structures.