Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.

Various studies have shown that students tend to get higher marks when assessed through coursework based assessment methods which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of educational data mining studies that preprocess data through conventional data mining processes including data preparation process, but they are using transcript data as they stand without looking at examination and coursework results weighting which could affect prediction accuracy. This paper proposes a different data preparation process through investigating more than 230000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled modules assessment methods. They must rather be investigated thoroughly and considered during EDMs data preprocessing phases. More generally, it is concluded that educational data should not be prepared in the same way as other data types due to differences as data sources, applications, and types of errors in them. Therefore, an attribute, coursework assessment ratio, is proposed to be used in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR on prediction process using the random forest classification technique has been investigated. It is shown that considering CAR as an attribute increases the accuracy of predicting students second year averages based on their first year results.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

Computer vision is yet another technology that was made possible in the wake of the recent AI revolution. Thanks to modern machine learning algorithms, you can now train a computer to distinguish between objects in images and videos. This opens a world of stunning opportunities for business across industries. Below, we explore five examples of how AI and computer vision are helping to revamp the shopping experience, both online and offline. Likely inspired by Amazon Go, AiFi offers a technology that can turn your brick-and-mortar venue into a line-free store.

Live Online Workshop During the workshop, you'll learn the basics of data science using Python programming through examples provided by the instructor. No prior programming experience is necessary. You must join the webinar via a computer. In this live online workshop, you will learn the basics of machine learning. Participants will learn how to apply machine learning to predict customer loan default probability through an instructor-led live demo.

Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These kinds of problems are present in all fields, the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning, and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

Time travel becomes a family affair in Bill and Ted Face the Music, the long-awaited third film in the popular Bill and Ted comedy franchise. Fans won't be disappointed: the film is most excellent, capturing that same breezy, chaotic, let's-just-have-fun-with-this madcap magic of its predecessors. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "We were trying to pay some homage to the original two [films] while making it feel like it was contemporary," Parisot told Ars about how he approached bringing the Bill and Ted franchise into the 21st century.

Not sure where this quote originally came from, it is sometimes kind of half-attributed to Albert Einstein. Anyway, this post is my attempt of explaining (to myself and others) how the neural…

This Specialization will master you in fundamentals of probabilistic graphical models. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains.

Offered by deeplearning.ai. Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.