Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark. Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation.

Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn't just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential. Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score -- typically between 0 and 1 -- to indicate the likelihood that it's fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not -- typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable. False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed.

Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

--In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions. The stunning success of deep learning technology, convolu-tional neural networks (CNN) in particular [1]-[3], has led to a rush towards technology development for new applications that ten years ago would have been considered unrealistic.

In order to get one number that tells us how good our curve is, we can calculate the Area Under the ROC Curve, or ROC AUC score. The more top-left your curve is the higher the area and hence higher ROC AUC score. Alternatively, it can be shown that ROC AUC score is equivalent to calculating the rank correlation between predictions and targets. From an interpretation standpoint, it is more useful because it tells us that this metric shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.

We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anomaly measure for a point that we call PIDScore, which measures the minimum density of data points over all subcubes containing the point. We present PIDForest: a random forest based algorithm that finds anomalies based on this definition. We show that it performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.

Computer architecture is currently undergoing a radical and exciting transition as the end of Moore's Law nears, and the burden of increasing humanity's ability to compute falls to the creativity of computer architects and their ability to fuse together the application and the silicon. A case in point is the recent explosion of deep neural networks, which occurred as a result of a drop in the cost of compute because of successful parallelization with GPGPUs (general-purpose graphics processing units) and the ability of cloud companies to gather massive amounts of data to feed the algorithms. As improvements in general-purpose architecture slow to a standstill, we must specialize the architecture for the application in order to overcome fundamental energy efficiency limits that prevent humanity's progress. This drive to specialize will bring another wave of chips with neural-network specific accelerators currently in development worldwide, but also a host of other kinds of accelerators, each specialized for a particular planet-scale purpose. Organizations like Google, Microsoft, and Amazon are increasingly finding reasons to bypass the confines imposed by traditional silicon companies by rolling their own silicon that is tailored to their own datacenter needs.

In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that will satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.