There were three workshops held at AIIDE-20, held virtually October 19-23, 2020, including Experimental AI in Games, Intelligent Narrative Technologies, and Artificial Intelligence for Strategy Games. For more information the AIIDE conference, please see aiide.org. INT returned for its 12th meeting in 2020 with two excellent keynote talks and a wide variety of topics on applying AI to games and other interactive stories. The 12th workshop on Intelligent Narrative Technologies was held this year as part of the AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment. INT brings together a multidisciplinary team of researchers interested in artificial intelligence, narrative theory, game development, psychology, social justice, and many other topics. This year's workshop featured two keynotes.

Have you always wanted to learn JavaScript but you just don't know where to start? Or maybe you have started to learn Javascript, but you just don't know how to work with basic concepts like the intermediate level JavaScript programming, object-oriented programming in JavaScript, asynchronous programming in JavaScript and JSON objects. If that Sounds Like you…. Then our complete Mastering JavaScript Essentials 2021 Novice to Professional is for You! Join 800,000 Students Who Have Enrolled in our Udemy Courses! Watch the Promo Video to see how you can Get Started Today!

… 12-week-long “Online Training and Certification Course (OTCC) in Cyber Security, Artificial Intelligence and Machine Learning (AI & ML)” conducted …

Educational technology is the use of both physical hardware, software, and educational theoretic to facilitate learning and improve performance by creating, using, and managing appropriate technological processes and resources. In EdTech, the most significant uses of AI are in content recommendation, AI-powered teaching assistants such as chat-bots performing specific tasks and accessibility functions such as text to speech and voice recognition. When used effectively, these tools are empowering teachers. Continual, formative assessment data is used as input for adaptive algorithms that power an output that is a work programme. This data can also be shared with the teacher, saving them hours in manually collecting the data and giving them eyes on the strengths and weaknesses of students.

If you want to become an expert in machine learning, you must also learn deep learning. There are many paid and free courses on the internet that can give you a comprehensive knowledge of the concepts of deep learning. So, if you want to know about the best deep learning courses, this article is for you. In this article, I'm going to introduce you to some of the best deep learning courses you can choose for learning deep learning. I found and selected two deep learning courses on the Internet.

Check out the Tutorial tab for learning materials and an instructional video! Task A level-order traversal, also known as a breadth-first search, visits each level of a tree's nodes from left to right, top to bottom. You are given a pointer,, pointing to the root of a binary search tree. Complete the levelOrder function provided in your editor so that it prints the level-order traversal of the binary search tree. Hint: You'll find a queue helpful in completing this challenge.

I wanted an easy way to get a state-of-the-art image augmentation pipeline with no manual iteration, no separate models to train and no thinking. To provide that, I created MuarAugment (Model Uncertainty- And Randomness-based Augmentation), a GPU-supported Python package built on Pytorch, Albumentations and Kornia. There are a few resources you can use to master MuarAugment. There are Colab tutorials demonstrating MuarAugment. Most of the material in this article comes from those.

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability.

This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists and python developers to learn the AzureML . This course is designed based on latest changes done in DP-100 Certification. This course would also be useful for the experts who needs to know how to create and deploy a machine learning environment in production. Will train machine learning and deep learning algorithm in azure ml in local machine and same code will be executed in azure as well.

The future of museums is virtual. Moving collections online and creating virtual environments for patrons to experience a museum's offerings exponentially expands the levels of impact and reach a museum can have. Providing digital experiences of museum collections is a necessity for museums that want to build impact, prestige, and revenue. In fact, 98% of museums agree that their highest investment priorities include online platforms and digitalizing their collections [1]. It is clear that museums see the value of digital transformation; however, 69% of museums claimed to have a digital strategy [2], but only 23% of museums have digitalized parts of their collection [3].