Vector valued functions are often encountered in machine learning, computer graphics and computer vision algorithms. They are particularly useful for defining the parametric equations of space curves. It is important to gain a basic understanding of vector valued functions to grasp more complex concepts. In this tutorial, you will discover what vector valued functions are, how to define them and some examples. A gentle iIntroduction to vector valued functions.

As we all know, Covid-19 struck Wuhan on New Year's Eve in 2019, and the city embraced total lockdown. Soon, the rest of China and the world followed. But what many don't know is Chinese education never went into lockdown. Within less than three months, Beijing Normal University (BNU) started a new semester offering more than 3,000 online courses. BNU could do so only because of China's comprehensive education technology (EdTech) drive in the preceding years, as a blog post at the Oxford Internet Institute elaborates.

In this course you will learn about a Humanoid Robot Animation like walking, talking etc. This is more like a fun kind of course where no hard or complex robotics concept will be taught rather, we will use a software to create a humanoid robot animation. Software use for this course is Choregraphe. This software is actually developed for Real NAO Robot (humanoid Robot). It is a visual programming language. It allows you to: create animations and behaviors for your NAO, test them on a simulated robot before trying them with your real NAO, and also monitor and control NAO.

Levi Belnap wants to make this clear from the beginning--artificial intelligence will never replace human teachers, at least not in our lifetime. What he and his colleagues at Merlyn Mind believe is that AI can enhance teachers' work. On this episode of EdTech Today, Levi introduces us to his nascent company's offering and provides some insights on how they believe the classroom experience can be better for all involved. The company launched out of stealth mode last month to unveil the first digital assistant built specifically for education that empowers teachers to more naturally use the technology in their classrooms and simplify their work. The company also announces it has closed $29 million in funding to date, led by Learn Capital.

Learn Advanced AI for Games with Behaviour Trees Create your own Behaviour Tree API in C# and apply it in the Unity Game Engine Hot & New What you'll learn Description Behaviour Trees (BTs) are an A.I. architecture that provide game characters with the ability to select behaviours and carry them out, through a tree-like architecture that defines simple but powerful logic operations. It can be used across a wide range of game genres from first-person shooters to real-time strategies and developing intelligent characters capable of making smart decisions. The codebase is deceptively simple and yet logical, reusable and extremely powerful. The library is written in C# and implemented in Unity 2020, however will easily port to other applications. In this course, Penny demystifies the advanced A.I. technique of BTs used for creating believable and intelligent game characters in games, using her internationally acclaimed teaching style and knowledge from almost 30 years working with games, graphics, and having written two award-winning books on games AI.

We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web. HelpViz transforms text instructions to graphical tutorials in batch, by extracting a sequence of actions from each text instruction through an instruction parsing model, and executing the extracted actions on a simulation infrastructure that manages an array of Android emulators. The automatic execution of each instruction produces a set of graphical and structural assets, including images, videos, and metadata such as clicked elements for each step. HelpViz then synthesizes a tutorial by combining parsed text instructions with the generated assets, and contextualizes the tutorial to user interaction by tracking the user's progress and highlighting the next step. Our experiments with HelpViz indicate that our pipeline improved tutorial execution robustness and that participants preferred tutorials generated by HelpViz over text-based instructions. HelpViz promises a cost-effective approach for generating contextual visual tutorials for mobile interaction at scale.

The goal of representation learning is different from the ultimate objective of machine learning such as decision making, it is therefore very difficult to establish clear and direct objectives for training representation learning models. It has been argued that a good representation should disentangle the underlying variation factors, yet how to translate this into training objectives remains unknown. This paper presents an attempt to establish direct training criterions and design principles for developing good representation learning models. We propose that a good representation learning model should be maximally expressive, i.e., capable of distinguishing the maximum number of input configurations. We formally define expressiveness and introduce the maximum expressiveness (MEXS) theorem of a general learning model. We propose to train a model by maximizing its expressiveness while at the same time incorporating general priors such as model smoothness. We present a conscience competitive learning algorithm which encourages the model to reach its MEXS whilst at the same time adheres to model smoothness prior. We also introduce a label consistent training (LCT) technique to boost model smoothness by encouraging it to assign consistent labels to similar samples. We present extensive experimental results to show that our method can indeed design representation learning models capable of developing representations that are as good as or better than state of the art. We also show that our technique is computationally efficient, robust against different parameter settings and can work effectively on a variety of datasets. Code available at https://github.com/qlilx/odgrlm.git

We are seeing Artificial Intelligence (AI) used in all areas of life and work. Because of the continued growth in and demand for skills in AI, we need to provide opportunities for all students to learn about and understand how AI works. Dave Touretzky, the founder of AI4K12 had stated: "It's important that children be given accurate information about AI so they can understand the technology that is reshaping our lives." Artificial intelligence is increasing in all areas of our world and a recent Forbes article shared five industries that are seeing increased benefits from artificial intelligence. There is a prediction that there will be 33 million self-driving cars on the road by 2040.

This course gives you an overview of Computer Vision, Machine Learning with AWS. In this course, you will learn how to build and train a computer vision model using the Apache MXNet and GluonCV toolkit. This course tells you about AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker GroundTruth, and Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS. In the final project, you have to select the appropriate pre-trained GluonCV model, apply that model to your dataset, and visualize the output of your GluonCV model. Now, let's see the syllabus of the course-

Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works? In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle. We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.