The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building. An advanced neural network classifier and SVM are employed to (1) learn Hodge numbers and report a remarkable improvement over previous efforts, (2) query for favourability, and (3) predict discrete symmetries, a highly imbalanced problem to which the Synthetic Minority Oversampling Technique (SMOTE) is applied to boost performance. In each case study, we employ a genetic algorithm to optimise the hyperparameters of the neural network. We demonstrate that our approach provides quick diagnostic tools capable of shortlisting quasi-realistic string models based on compactification over smooth CICYs and further supports the paradigm that classes of problems in algebraic geometry can be machine learned.

What are some underdeveloped areas in computer science research right now (2018)? Over the past few decades, computer science research, either in industry or academia, has led to ground breaking technology innovations such as the internet, which continues to change our lives. In the post-Moore's Law era, advances in cloud computing affected so many sub-areas of computer science like operating systems and database systems. Furthermore, solid state drives (SSDs) changed the way we design storage systems, which were previously tailored for the mechanical hard drive (HDD). Recently, quantum computing promises lightning-speed calculations as opposed to classic electronics-based computers.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.

Machine learning and AI are reshaping the industries. Gartner predicts that the global enterprise value derived from AI will total $1.2 trillion in 2018, which is a 70% increase from 2017. By 2022, the number will reach $3.9 trillion [1]. From IBM Watson to Google Alpha Go, enterprises have made great strides in AI research in the last couple of years. While almost all executives believe AI is the key driver of growth and success [2], they are still in the early stage of applying the technology to their businesses.

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This is in furtherance of DSN's drive for accelerated applications of artificial intelligence to solve social and business problems through a strategic partnership between the academic community, industry, and local and international technology hubs similar to the highly successful Stanford University-Silicon Valley model. DSN is committed to supporting Nigerian students and encouraging them to turn their academic research into innovative start-ups or social enterprises aligned with millennial developmental goals. DSN has a particular interest in solving local problems in the fields of health, education, agriculture and financial inclusion. The Hub will be the community centre for data science enthusiasts and experts to fraternize, learn, share ideas and participate in local and international projects. DSN also plans to use the Hub for face-to-face masterclasses, industry meet-ups, and dial-in conferences with its over 100 mentors from across the globe.

The Steam gaming platform will allow video games containing virtual reality porn and other controversial content, in an abrupt reversal of policy. Valve, which owns the popular online video game store, came under fire recently after a video game billed as a "school shooting simulation" appeared on the platform. Parents of school shooting victims objected to the Active Shooter game and an online petition received more than 200,000 signatures to have it removed. Valve eventually pulled the game but says it now wants to avoid engaging in debates of how it should police its platform. "The harsh reality of this space, that lies at the root of our dilemma, is that there is absolutely no way we can navigate it without making some of our players really mad," Valve's Erik Johnson said in a post to the Steam Store on Wednesday, 6 June.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. Scala has emerged as an important tool for efficiently performing various data analysis tasks. So, if you're interested to load raw datasets with Spark, and perform exploratory data analysis on those via plotting Scala libraries, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

AI is especially influential for children who need special education. These children mostly have one type of learning disability, so they have some impairments in social skills like language and communication or they have trouble in reading, writing and math. Having all this in mind, it is obvious that the traditional one-size-fits-all approach is not applicable for children taking special education. More specifically, for example, autism is a spectrum which children with autism are so different from each other in their impairments and social skills. Because of their uniqueness, education that they take should be different from each other to be effective.

Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine.