Many social services including online dating, social media, recruitment and online learning, largely rely on \matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained by utilizing the information from a larger model (teacher model). In this paper, we present a survey of knowledge distillation techniques applied to deep learning models. To compare the performances of different techniques, we propose a new metric called distillation metric. Distillation metric compares different knowledge distillation algorithms based on sizes and accuracy scores. Based on the survey, some interesting conclusions are drawn and presented in this paper.

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.

Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.

In this 28-video series, you will learn important concepts and technologies to build your end-to-end machine learning applications on Azure.

Teachers at Criftins C of E Primary School, in Dudleston Heath, near Ellesmere, say that the Maths-Whizz program has been a vital tool to children's education. The program uses artificial intelligence (AI) to mirror the behaviour of a human tutor through interactive learning and numbers games, in order to tailor maths lessons to each child's individual ability. Mandy Jones, headteacher at Criftins school, said: "Our pupils have enjoyed using Maths-Whizz throughout the closure period. "It meets the need of every child because there is enough scope and excitement to challenge the most able, as well as those who need support at every level. "We have been using it for several years but since the lockdown it has become all the more invaluable. It is widely accepted that the Covid-19 pandemic, which has resulted in many pupils being out of school since March, will produce substantial losses in learning that will be further impacted by the summer holidays. Experts say maths knowledge normally regresses by two to three months over the summer break due to lack of practice. This loss of learning is known as the'summer slide'. It means many schools will potentially face disruption in September – with pupils struggling to catch up. Richard Marett, CEO of global learning company Whizz Education, which supplied Maths-Whizz, said: "Many parents are rightly concerned about their child's education.

About Our Team: At our core, we are an analytics company. The insights we gain from our analyses guide our strategic path forward as we grow revenue, enter new markets, and strengthen our customer relationships. The Data Science team proactively leads and collaborates to identify the most valuable problems to solve, constructs a Roadmap to delivery, and executes our plan. All of the outputs of the Data Science team models will feed into the Product portfolio at DRIVIN, aiding tens of thousands of internal and external stakeholders in their decision-making processes. About Our Candidate: Can think for themselves and discover new and insightful ways to solve difficult problems without having a clear roadmap laid out Can communicate effectively with data science teammates and non-technical audiences alike Can deliver quality code in an Agile framework that ships to a production environment Has confidence, hustle, energy, and drive – accountability is key and the impact of your work is crucial to our success What You Will Be Doing: Own the machine learning engineering required to efficiently operate computer vision, optimization, and other data driven decision systems Partner with our computer vision and data scientists to improve model performance and responsiveness Work in an Agile environment with team members, delivering solutions quickly and continuously exploring ways to improve our results Work closely with colleagues in Engineering, Product, Operations, and Sales to structure problems and understand the impact across various departments within the company What You Need to Be Successful: Candidates tend to have at least a Bachelor's Degree in a quantitative field, but if you can explain how your experience and background can be leveraged as a senior contributor to a data science team we are all ears Significant experience (roughly 5 years) in a Data Science, Deep Learning, Machine Learning Engineering, Computer Vision, or Data Engineering position building data products in a production environment Experience in the full project lifecycle from requirements gathering to proof of concept to production delivery Experience designing and implementing machine learning models that are production-ready Familiarity with designing and implementing models for computer vision such as neural networks using deep learning frameworks like PyTorch or TensorFlow Experience coding with Python and SQL Familiarity with CUDA and C Experience with developing within a cloud environment.

Get a job as a Machine Learning Engineer is by no means an easy task (a positive note to start the article). But it is completely doable if you have the patience and the discipline. The bad news is that you have to study a lot to land a job in a tech company. The good news is that there is a shortage of skilled Machine Learning Engineers, even in big tech, and the salaries are crazy. For me it totally is.

> Science's COVID-19 coverage is supported by the Pulitzer Center. In a normal summer, Appledore Island, a 39-hectare outcrop 12 kilometers off the coast of Maine and New Hampshire, becomes a classroom. Students from high school to graduate level live in close quarters, eat in a communal dining hall, and work shoulder to shoulder to explore the biology of the shore and waters in 18 courses organized by the Shoals Marine Laboratory. But this summer, with the pandemic surging, students have stayed home. Instead, a skeleton staff on Appledore is streaming field trips and dissections of fish and invertebrates and setting up cameras to gather data for students. Rather than leading students around the island, coastal restoration ecologist Gregg Moore from the University of New Hampshire (UNH), Durham, hauls a backpack full of equipment: “a dual modem with two different cellular carriers, a signal-boosting directional antenna, and a large DC power source,” he says. The equipment allows him to teach 12 remote students—twice the course's usual enrollment—basic techniques of coastal ecology. Moore's is just one of hundreds of lab and field courses forced online by COVID-19—“a seismic shift for those who were not already involved in distance or online education,” says Martin Storksdieck, a science education researcher at Oregon State University, Corvallis. Some researchers worry students will miss out on certain practical and problem-solving skills and won't be able to judge whether the hands-on work of a scientist is a good fit for them. But instructors are developing high-tech ways to simulate the field and lab experiences. “I would say [these courses] are not virtual,” says Jennifer Seavey, director of the Shoals lab. “They are real.” And some advantages are emerging. By lowering geographical and financial barriers, Seavey says, “Virtual field courses are democratizing fieldwork.” The shift has taken ingenuity. “Professors must get creative and use a combination of what is available,” including online videos and free or commercially available online labs, says Mildred Pointer, a physiologist at Howard University who is working on a fall course in general biology. No single tool meets all their needs, Pointer says. As the pandemic gained momentum, emails flew among the leaders of the National Association of Geoscience Teachers. Many U.S. geology majors must take a “capstone” field course to graduate. The cancellation of more than three-quarters of these courses jeopardized graduation for many majors. So the association invited instructors to develop learning objectives that did not depend on students doing fieldwork. It also compiled online exercises to help the 29 field courses that have moved online this summer. Lessons range from “Orienteering in Minecraft” to “Geology of Yosemite Valley,” which includes a 43-stop Google Earth tour with photos and embedded text. Like Moore, geoscientist Jim Handschy wanted to give remote students “as close to the real experience as possible.” He runs Indiana University's Judson Mead Geologic Field Station in Montana, which had enrolled 60 students before classes were canceled in March. He and a few instructors visited each outcrop in their course plan, filmed the rocks and landscape, and captured magnified views of samples. Each week, the class delves deeper into the rock layers and their history. For their final project, students digitally map a 3100-hectare landscape. Shannon Dulin, a geologist at the University of Oklahoma, Norman, who just finished teaching a field course, sees the value of learning how to survey a landscape without setting foot on it. On their class evaluations, her students said they gained unexpected skills. “And these are skills they are going to need on the job,” she adds, as geologists are increasingly being asked to evaluate sites they don't visit. In other fields, hands-on learning takes place in labs. Typically, students work in pairs and share equipment, “so there are a lot of issues about virus transmission,” says Heather Lewandowski, a physicist at the University of Colorado (CU), Boulder. At her university this fall, lab exercises as diverse as building an electrical circuit or analyzing solar flare data will most likely be completely remote. Luckily, physics already had a foot in the virtual lab world—especially at CU. There, back in 2002, Nobel laureate Carl Wieman developed the Physics Education Technology (PhET) Interactive Simulations project to provide “games” that teach students basic physics concepts. The PhET web portal now has 106 physics-based simulations and another 50 or so for other disciplines. It became a go-to place this spring for faculty shifting to online teaching; traffic increased fivefold, says Director Katherine Perkins. In addition, several universities have adopted a handheld device called the iOLab that rents for $50 a semester. With it, students can measure magnetism, light intensity, acceleration, temperature, gravity, and atmospheric pressure, and do basic physics experiments at home. “They like that we trust them and are not just giving them instructions,” says iOLab inventor and physicist Mats Selen at the University of Illinois, Urbana-Champaign. Lewandowski and her colleagues surveyed physics instructors and students about their experiences and posted their findings on arXiv, the physics preprint server, on 2 July. Respondents said online labs work best when projects are open-ended, and online class meetings are kept small. They complained about technical difficulties, students having unequal access to the internet and materials, and longer prep times for both students and instructors. But they reported they could meet most key learning objectives, Lewandowski says, even though “there are lots of things we can't replicate in remote experiments,” such as such as building vacuum chambers or troubleshooting equipment. Some institutions decided this spring that virtual just wouldn't do. The Marine Biological Laboratory (MBL) in Woods Hole, Massachusetts, simply canceled its summer courses. “MBL courses are world-renowned for the intensity of the hands-on nature of the lab work,” says Director Nipam Patel. Students spend long hours with famous faculty and do their own projects using organisms collected locally. “We felt that it would be exceedingly difficult to replicate these experiences as a virtual lab course.” Other institutions will try for a mix of in-person and virtual labs. Suely Black, chemistry chair at Norfolk State University, expects only half of his students will be in lab each week this fall, while the other half will be in online classes analyzing data and writing reports. “The crisis has caused us to more critically evaluate what activities students must experience in the lab setting,” he says. Similarly, this fall, organic chemistry students at the University of Michigan (UM), Ann Arbor, will rotate into the lab in small groups, giving each a taste of the hands-on experience. Personal protection equipment is standard for this course and all the work is done in hoods with excellent air exchange, so “they are really fully protected,” says UM biochemist Kathleen Nolta. Storksdieck, an advocate of online learning, questions the value of smelling fumes or using a pipette. “We have to ask whether all the hands-on taught so far was all that great,” he says. Dominique Durand, a biomedical engineer at Case Western Reserve University, says after he put a master's program in biomedical engineering completely online 5 years ago, he concluded that solving problems was more important than hands-on experience. And University of California, Santa Cruz, ecologist Erika Zavaleta thinks virtual courses will open fieldwork to far more students. “There are things you can do online that you can't do in person,” she adds, such as visiting more places than possible by driving. Even so, Handschy laments that his geology students will not have the 12-hour-a-day immersive interactions with each other and faculty that past classes have had. Natalie White, a rising junior at UNH who took Moore's course on Appledore last year, agrees: “You don't have all the time in between when you walk around the island and can ask impromptu questions.” Appledore Island is the source of some her fondest memories. “I think they are missing out on the community.”

If you don't know, Keras is a both powerful and easy-to-use Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries like Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code, which is just awesome. In this course, you will learn how to build an end-to-end Python machine learning project using Keras and tune a deep learning model and neural network. The best part of this course is that n the course, we will walk through every line of code so you'll be able to understand the model and the process.