TensorFlow is a Python-friendly open source library that can be used for complex computation, making Machine Learning more efficient. With a convenient front-end API, it allows developers to execute complex tasks using existing libraries in neural mapping, deep learning, etc. The technology can be used to train and run complex neural networks for a variety of tasks. Since it is an AI library, it can be used to design robust models involving complex dataflow graphs. Each node within the dataflow graph shows a mathematical operation, with each connection being a multidimensional array (tensor). Development teams can create neural networks of large scales, that have multiple layers interacting with one another. It can then help in managing complex structures, like road safety management, through data provided from smart cameras, sensors and radar detectors.

Machine learning continues to evolve with pace, bringing us to the latest advanced algorithms like deep learning. In this article, we will discuss this branch and why it is so good. We will also share the best ML frameworks to try before 2020 comes. Deep learning is simply great when it comes to accuracy. It plays a key role in bridging the gap between human intelligence and AI.

Data Mining: Concepts, Models, Methods, and Algorithms, 3rd Edition Books by Mehmed Kantardzic Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author―a noted expert on the topic―explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: Explores big data and cloud computing Examines deep learning Includes information on convolutional neural networks (CNN) Offers reinforcement learning Contains semi-supervised learning and S3VM Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Advancements in robotics and artificial intelligence (AI) are enabling the development of artificial agents designed to assist humans in a variety of everyday settings. One of the many possible uses for these systems could be to escort humans or valuable goods that are being transferred from one location to another, defending them from threats or attacks. Fascinated by this idea, a team of researchers at the University of New Mexico has recently introduced a new end-to-end solution for coordinating robotic escort teams that are protecting high-value payloads or goods. The technique they proposed, presented in a paper pre-published on arXiv, is based on deep reinforcement learning (RL), which entails training algorithms to make effective predictions by analyzing data. "I first came up with the idea behind this study when thinking about lugging my suitcase through a crowded airport," Lydia Tapia, the lead researcher on the study, told TechXplore.

Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents PYTHIA, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle longterm context information, and deal efficiently with missing or corrupted character and word representations. To train it, we wrote a nontrivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML.

You've been learning about data science and want to get rocking immediately on solving some problems. This article will introduce you to the essential data science libraries so you can start flying today. Python has three core data science libraries upon which many others have been built. For simplicity, you can think of Numpy as your go-to for arrays. Numpy arrays are different from standard Python lists in many ways, but a few to remember are they are faster, take up less space, and have more functionality. It is important to note, though, that these arrays are of a fixed size and type, which you define at creation.

Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability. Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.

The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger and trip characteristics) and dynamic (real-time passenger tracking). A real-world case study exemplifies the application of these models, using anonymous WiFi traces collected at Bologna Airport to train the networks. The performance of the models were evaluated according to the misclassification rate of passenger activity choices. In the LSTM approach, two different multi-step forecasting strategies are tested. According to our findings, the direct LSTM approach provides better results than the FNN, especially when the prediction horizon is relatively short (20 minutes or less).

User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user's preference when executing an intention. Learning representations that uncover and disentangle these latent factors can bring enhanced robustness, interpretability, and controllability. However, learning such disentangled representations from user behavior is challenging, and remains largely neglected by the existing literature. In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior. Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately. A micro-disentanglement regularizer, stemming from an information-theoretic interpretation of VAEs, then forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We further demonstrate that the learned representations are interpretable and controllable, which can potentially lead to a new paradigm for recommendation where users are given fine-grained control over targeted aspects of the recommendation lists.

--Deep learning is increasingly used for state estimation problems such as tracking, navigation, and pose estimation. The uncertainties associated with these measurements are typically assumed to be a fixed covariance matrix. For many scenarios this assumption is inaccurate, leading to worse subsequent filtered state estimates. We show how to model multivariate uncertainty for regression problems with neural networks, incorporating both aleatoric and epistemic sources of heteroscedastic uncertainty. We train a deep uncertainty covariance matrix model in two ways: directly using a multivariate Gaussian density loss function, and indirectly using end-to-end training through a Kalman filter . We experimentally show in a visual tracking problem the large impact that accurate multivariate uncertainty quantification can have on Kalman filter estimation for both in-domain and out-of- domain evaluation data.